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Pierre Baldi
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- affiliation: University of California, Irvine, CA, USA
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2020 – today
- 2024
- [j147]Mohammadamin Tavakoli, Ryan J. Miller, Mirana Angel, Michael A. Pfeiffer, Eugene S. Gutman, Aaron Mood, David Van Vranken
, Pierre Baldi
PMechDB: A Public Database of Elementary Polar Reaction Steps. J. Chem. Inf. Model. 64(6): 1975-1983 (2024) - [c91]Stephen Marcus McAleer, JB Lanier, Kevin A. Wang, Pierre Baldi, Tuomas Sandholm, Roy Fox:
Toward Optimal Policy Population Growth in Two-Player Zero-Sum Games. ICLR 2024 - [c90]Kolby Nottingham, Yasaman Razeghi, Kyungmin Kim, JB Lanier, Pierre Baldi, Roy Fox, Sameer Singh:
Selective Perception: Learning Concise State Descriptions for Language Model Actors. NAACL (Short Papers) 2024: 327-341 - [c89]Antonios Alexos, Pierre Baldi:
FastStitch: Speech editing by hitch-hiking a pre-trained FastSpeech2 model. NLDL 2024: 1-6 - [c88]Pierre Baldi:
Deep Learning Over-Parameterization: the Shallow Fallacy. NLDL 2024: 7-12 - [i57]Antonios Alexos, Pierre Baldi:
AttentionStitch: How Attention Solves the Speech Editing Problem. CoRR abs/2403.04804 (2024) - [i56]Shahriar Hojjati Emmami, Ali Pilehvar Meibody, Lobat Tayebi, Mohammadamin Tavakoli, Pierre Baldi:
Unraveling the Molecular Magic: AI Insights on the Formation of Extraordinarily Stretchable Hydrogels. CoRR abs/2403.05129 (2024) - [i55]Antonios Alexos, Yu-Dai Tsai, Ian Domingo, Maryam Pishgar, Pierre Baldi:
Neural Erosion: Emulating Controlled Neurodegeneration and Aging in AI Systems. CoRR abs/2403.10596 (2024) - [i54]Alexander Shmakov, Kevin Greif, Michael James Fenton, Aishik Ghosh, Pierre Baldi, Daniel Whiteson:
Full Event Particle-Level Unfolding with Variable-Length Latent Variational Diffusion. CoRR abs/2404.14332 (2024) - [i53]Pierre Baldi, Alireza Rahmansetayesh:
From Local to Global Order: A Theory of Neural Synaptic Balance. CoRR abs/2405.09688 (2024) - 2023
- [j146]Pierre Baldi
The quarks of attention: Structure and capacity of neural attention building blocks. Artif. Intell. 319: 103901 (2023) - [j145]Sabino Miranda, Obdulia Pichardo-Lagunas, Bella Martínez-Seis, Pierre Baldi:
Evaluating the Performance of Large Language Models for Spanish Language in Undergraduate Admissions Exams. Computación y Sistemas (CyS) 27(4) (2023) - [j144]Mohammadamin Tavakoli, Yin Ting T. Chiu
RMechDB: A Public Database of Elementary Radical Reaction Steps. J. Chem. Inf. Model. 63(4): 1114-1123 (2023) - [c87]Geunwoo Kim, Pierre Baldi, Stephen McAleer:
Language Models can Solve Computer Tasks. NeurIPS 2023 - [c86]Alexander Shmakov, Kevin Greif, Michael James Fenton, Aishik Ghosh, Pierre Baldi, Daniel Whiteson:
End-To-End Latent Variational Diffusion Models for Inverse Problems in High Energy Physics. NeurIPS 2023 - [c85]Mohammadamin Tavakoli, Pierre Baldi, Ann Marie Carlton, Yin Ting T. Chiu, Alexander Shmakov, David Van Vranken:
AI for Interpretable Chemistry: Predicting Radical Mechanistic Pathways via Contrastive Learning. NeurIPS 2023 - [c84]Sungduk Yu, Walter M. Hannah, Liran Peng, Jerry Lin, Mohamed Aziz Bhouri, Ritwik Gupta, Björn Lütjens, Justus C. Will, Gunnar Behrens, Julius Busecke, Nora Loose, Charles Stern, Tom Beucler, Bryce E. Harrop, Benjamin R. Hillman, Andrea M. Jenney, Savannah L. Ferretti, Nana Liu, Animashree Anandkumar, Noah D. Brenowitz, Veronika Eyring, Nicholas Geneva, Pierre Gentine, Stephan Mandt, Jaideep Pathak, Akshay Subramaniam, Carl Vondrick, Rose Yu, Laure Zanna, Tian Zheng, Ryan Abernathey, Fiaz Ahmed, David C. Bader, Pierre Baldi, Elizabeth A. Barnes, Christopher S. Bretherton, Peter M. Caldwell, Wayne Chuang, Yilun Han, Yu Huang, Fernando Iglesias-Suarez, Sanket R. Jantre, Karthik Kashinath, Marat Khairoutdinov, Thorsten Kurth, Nicholas J. Lutsko, Po-Lun Ma, Griffin Mooers, J. David Neelin, David A. Randall, Sara Shamekh, Mark Taylor, Nathan M. Urban, Janni Yuval, Guang Zhang, Mike Pritchard:
ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation. NeurIPS 2023 - [c83]Mirana Angel, Anuj Patel, Amal Alachkar, Pierre Baldi:
Clinical Knowledge and Reasoning Abilities of Large Language Models in Pharmacy: A Comparative Study on the NAPLEX Exam. SNAMS 2023: 1-4 - [c82]Mirana Angel, Anuj Patel, Haiyi Xing, Dylan Balsz, Cody L. Arbuckle, David Bruyette, Pierre Baldi:
AI and Veterinary Medicine: Performance of Large Language Models on the North American Licensing Examination. SNAMS 2023: 1-4 - [i52]Alexander Shmakov, Alejandro Yankelevich, Jianming Bian, Pierre Baldi:
Interpretable Joint Event-Particle Reconstruction for Neutrino Physics at NOvA with Sparse CNNs and Transformers. CoRR abs/2303.06201 (2023) - [i51]Geunwoo Kim, Pierre Baldi, Stephen McAleer:
Language Models can Solve Computer Tasks. CoRR abs/2303.17491 (2023) - [i50]Alexander Shmakov, Kevin Greif, Michael James Fenton
End-To-End Latent Variational Diffusion Models for Inverse Problems in High Energy Physics. CoRR abs/2305.10399 (2023) - [i49]Junze Liu, Aishik Ghosh, Dylan Smith, Pierre Baldi, Daniel Whiteson:
Generalizing to new calorimeter geometries with Geometry-Aware Autoregressive Models (GAAMs) for fast calorimeter simulation. CoRR abs/2305.11531 (2023) - [i48]Sungduk Yu, Walter M. Hannah, Liran Peng, Mohamed Aziz Bhouri, Ritwik Gupta, Jerry Lin, Björn Lütjens, Justus C. Will, Tom Beucler
ClimSim: An open large-scale dataset for training high-resolution physics emulators in hybrid multi-scale climate simulators. CoRR abs/2306.08754 (2023) - [i47]Kolby Nottingham, Yasaman Razeghi, Kyungmin Kim, JB Lanier, Pierre Baldi, Roy Fox, Sameer Singh:
Selective Perception: Optimizing State Descriptions with Reinforcement Learning for Language Model Actors. CoRR abs/2307.11922 (2023) - [i46]Michael James Fenton, Alexander Shmakov, Hideki Okawa, Yuji Li, Ko-Yang Hsiao, Shih-Chieh Hsu, Daniel Whiteson, Pierre Baldi:
Extended Symmetry Preserving Attention Networks for LHC Analysis. CoRR abs/2309.01886 (2023) - [i45]Mohammadamin Tavakoli, Yin Ting T. Chiu, Alexander Shmakov, Ann Marie Carlton, David Van Vranken, Pierre Baldi:
AI for Interpretable Chemistry: Predicting Radical Mechanistic Pathways via Contrastive Learning. CoRR abs/2311.01118 (2023) - [i44]Antonios Alexos, Junze Liu, Akash Tiwari, Kshitij Bhardwaj, Sean Hayes, Pierre Baldi, Satish T. S. Bukkapatnam, Suhas Bhandarkar:
Machine Learning-Enhanced Prediction of Surface Smoothness for Inertial Confinement Fusion Target Polishing Using Limited Data. CoRR abs/2312.10553 (2023) - [i43]Sabino Miranda, Obdulia Pichardo-Lagunas, Bella Martínez-Seis, Pierre Baldi:
Evaluating the Performance of Large Language Models for Spanish Language in Undergraduate Admissions Exams. CoRR abs/2312.16845 (2023) - 2022
- [j143]Gregor Urban, Christophe N. Magnan, Pierre Baldi
SSpro/ACCpro 6: almost perfect prediction of protein secondary structure and relative solvent accessibility using profiles, deep learning and structural similarity. Bioinform. 38(7): 2064-2065 (2022) - [j142]Lars Hertel, Pierre Baldi, Daniel L. Gillen:
Reproducible Hyperparameter Optimization. J. Comput. Graph. Stat. 31(1): 84-99 (2022) - [j141]Pierre Baldi
Call for a Public Open Database of All Chemical Reactions. J. Chem. Inf. Model. 62(9): 2011-2014 (2022) - [j140]Mohammadamin Tavakoli, Aaron Mood, David Van Vranken, Pierre Baldi
Quantum Mechanics and Machine Learning Synergies: Graph Attention Neural Networks to Predict Chemical Reactivity. J. Chem. Inf. Model. 62(9): 2121-2132 (2022) - [j139]Siwei Chen, Gregor Urban, Pierre Baldi
Weakly Supervised Polyp Segmentation in Colonoscopy Images Using Deep Neural Networks. J. Imaging 8(5): 121 (2022) - [j138]Muntaha Samad, Forest Agostinelli, Tomoki Sato
CircadiOmics: circadian omic web portal. Nucleic Acids Res. 50(W1): 183-190 (2022) - [d1]Babak Shahbaba
Hippocampal ensembles represent sequential relationships among an extended sequence of nonspatial events. Zenodo, 2022 - [i42]Mohammadamin Tavakoli, Alexander Shmakov, Francesco Ceccarelli, Pierre Baldi:
Rxn Hypergraph: a Hypergraph Attention Model for Chemical Reaction Representation. CoRR abs/2201.01196 (2022) - [i41]Stephen McAleer, Kevin Wang, John B. Lanier, Marc Lanctot, Pierre Baldi, Tuomas Sandholm, Roy Fox:
Anytime PSRO for Two-Player Zero-Sum Games. CoRR abs/2201.07700 (2022) - [i40]Pierre Baldi, Roman Vershynin:
The Quarks of Attention. CoRR abs/2202.08371 (2022) - [i39]Alexander Shmakov, Mohammadamin Tavakoli, Pierre Baldi, Christopher M. Karwin, Alex Broughton
Deep Learning Models of the Discrete Component of the Galactic Interstellar Gamma-Ray Emission. CoRR abs/2206.02819 (2022) - [i38]Stephen McAleer, John B. Lanier, Kevin A. Wang, Pierre Baldi, Roy Fox, Tuomas Sandholm:
Self-Play PSRO: Toward Optimal Populations in Two-Player Zero-Sum Games. CoRR abs/2207.06541 (2022) - [i37]John B. Lanier, Stephen McAleer, Pierre Baldi, Roy Fox:
Feasible Adversarial Robust Reinforcement Learning for Underspecified Environments. CoRR abs/2207.09597 (2022) - [i36]Junze Liu, Aishik Ghosh, Dylan Smith, Pierre Baldi, Daniel Whiteson:
Geometry-aware Autoregressive Models for Calorimeter Shower Simulations. CoRR abs/2212.08233 (2022) - 2021
- [j137]Pietro Di Lena
Fold recognition by scoring protein maps using the congruence coefficient. Bioinform. 37(4): 506-513 (2021) - [j136]Mohammadamin Tavakoli
SPLASH: Learnable activation functions for improving accuracy and adversarial robustness. Neural Networks 140: 1-12 (2021) - [j135]Pierre Baldi
A theory of capacity and sparse neural encoding. Neural Networks 143: 12-27 (2021) - [j134]Christine K. Lee, Muntaha Samad, Ira Hofer, Maxime Cannesson
Development and validation of an interpretable neural network for prediction of postoperative in-hospital mortality. npj Digit. Medicine 4 (2021) - [c81]Yasaman Razeghi, Kalev Kask, Yadong Lu, Pierre Baldi, Sakshi Agarwal, Rina Dechter:
Deep Bucket Elimination. IJCAI 2021: 4235-4242 - [c80]Stephen McAleer, John B. Lanier, Kevin A. Wang, Pierre Baldi, Roy Fox:
XDO: A Double Oracle Algorithm for Extensive-Form Games. NeurIPS 2021: 23128-23139 - [c79]Farima Farmahinifarahani, Yadong Lu, Vaibhav Saini, Pierre Baldi, Cristina V. Lopes:
D-REX: Static Detection of Relevant Runtime Exceptions with Location Aware Transformer. SCAM 2021: 198-208 - [i35]Jordan Ott, David Bruyette, Cody L. Arbuckle, Dylan Balsz, Silke Hecht, Lisa Shubitz, Pierre Baldi:
Detecting Pulmonary Coccidioidomycosis (Valley fever) with Deep Convolutional Neural Networks. CoRR abs/2102.00280 (2021) - [i34]Forest Agostinelli, Alexander Shmakov, Stephen McAleer, Roy Fox, Pierre Baldi:
A* Search Without Expansions: Learning Heuristic Functions with Deep Q-Networks. CoRR abs/2102.04518 (2021) - [i33]Pierre Baldi, Roman Vershynin:
A theory of capacity and sparse neural encoding. CoRR abs/2102.10148 (2021) - [i32]Stephen McAleer, John B. Lanier, Pierre Baldi, Roy Fox:
XDO: A Double Oracle Algorithm for Extensive-Form Games. CoRR abs/2103.06426 (2021) - [i31]Mohammadamin Tavakoli, Aaron Mood, David Van Vranken, Pierre Baldi:
Quantum Mechanics and Machine Learning Synergies: Graph Attention Neural Networks to Predict Chemical Reactivity. CoRR abs/2103.14536 (2021) - [i30]Alexander Shmakov, Michael James Fenton, Ta-Wei Ho, Shih-Chieh Hsu, Daniel Whiteson, Pierre Baldi:
SPANet: Generalized Permutationless Set Assignment for Particle Physics using Symmetry Preserving Attention. CoRR abs/2106.03898 (2021) - [i29]Stephen McAleer, John B. Lanier, Michael Dennis, Pierre Baldi, Roy Fox:
Improving Social Welfare While Preserving Autonomy via a Pareto Mediator. CoRR abs/2106.03927 (2021) - [i28]Mohammadamin Tavakoli, Peter J. Sadowski, Pierre Baldi:
Tourbillon: a Physically Plausible Neural Architecture. CoRR abs/2107.06424 (2021) - 2020
- [j133]Jordan Ott, Erik Linstead
Learning in the machine: To share or not to share? Neural Networks 126: 235-249 (2020) - [j132]Ira Hofer, Christine K. Lee, Eilon Gabel
Development and validation of a deep neural network model to predict postoperative mortality, acute kidney injury, and reintubation using a single feature set. npj Digit. Medicine 3 (2020) - [j131]Lars Hertel, Julian Collado
Sherpa: Robust hyperparameter optimization for machine learning. SoftwareX 12: 100591 (2020) - [j130]Jordan Ott, Mike Pritchard, Natalie Best, Erik Linstead, Milan Curcic, Pierre Baldi
A Fortran-Keras Deep Learning Bridge for Scientific Computing. Sci. Program. 2020: 8888811:1-8888811:13 (2020) - [c78]Mohammadamin Tavakoli, Pierre Baldi:
Continuous Representation of Molecules using Graph Variational Autoencoder. AAAI Spring Symposium: MLPS 2020 - [c77]Stephen McAleer, John B. Lanier, Roy Fox, Pierre Baldi:
Pipeline PSRO: A Scalable Approach for Finding Approximate Nash Equilibria in Large Games. NeurIPS 2020 - [i27]Mohammadamin Tavakoli, Pierre Baldi:
Continuous Representation of Molecules Using Graph Variational Autoencoder. CoRR abs/2004.08152 (2020) - [i26]Jordan Ott, Mike Pritchard, Natalie Best, Erik Linstead, Milan Curcic, Pierre Baldi:
A Fortran-Keras Deep Learning Bridge for Scientific Computing. CoRR abs/2004.10652 (2020) - [i25]Lars Hertel, Julian Collado, Peter J. Sadowski, Jordan Ott, Pierre Baldi:
Sherpa: Robust Hyperparameter Optimization for Machine Learning. CoRR abs/2005.04048 (2020) - [i24]Stephen McAleer, John B. Lanier, Roy Fox, Pierre Baldi:
Pipeline PSRO: A Scalable Approach for Finding Approximate Nash Equilibria in Large Games. CoRR abs/2006.08555 (2020) - [i23]Mohammadamin Tavakoli, Forest Agostinelli
SPLASH: Learnable Activation Functions for Improving Accuracy and Adversarial Robustness. CoRR abs/2006.08947 (2020) - [i22]Lars Hertel, Pierre Baldi, Daniel L. Gillen:
Quantity vs. Quality: On Hyperparameter Optimization for Deep Reinforcement Learning. CoRR abs/2007.14604 (2020) - [i21]Michael James Fenton, Alexander Shmakov, Ta-Wei Ho, Shih-Chieh Hsu, Daniel Whiteson
Permutationless Many-Jet Event Reconstruction with Symmetry Preserving Attention Networks. CoRR abs/2010.09206 (2020) - [i20]Stephen McAleer, Alex Fast, Yuntian Xue, Magdalene Seiler, William Tang
Deep machine learning-assisted multiphoton microscopy to reduce light exposure and expedite imaging. CoRR abs/2011.06408 (2020) - [i19]Junze Liu, Jordan Ott, Julian Collado, Benjamin Jargowsky, Wenjie Wu
Deep-Learning-Based Kinematic Reconstruction for DUNE. CoRR abs/2012.06181 (2020)
2010 – 2019
- 2019
- [j129]Lingge Li
Neural network gradient Hamiltonian Monte Carlo. Comput. Stat. 34(1): 281-299 (2019) - [j128]Forest Agostinelli
Solving the Rubik's cube with deep reinforcement learning and search. Nat. Mach. Intell. 1(8): 356-363 (2019) - [j127]Pierre Baldi
The capacity of feedforward neural networks. Neural Networks 116: 288-311 (2019) - [j126]Pierre Baldi, Roman Vershynin:
Polynomial Threshold Functions, Hyperplane Arrangements, and Random Tensors. SIAM J. Math. Data Sci. 1(4): 699-729 (2019) - [j125]Gregor Urban, Kevin Bache, Duc T. T. Phan
Deep Learning for Drug Discovery and Cancer Research: Automated Analysis of Vascularization Images. IEEE ACM Trans. Comput. Biol. Bioinform. 16(3): 1029-1035 (2019) - [j124]Siyu Shao
Highly Accurate Machine Fault Diagnosis Using Deep Transfer Learning. IEEE Trans. Ind. Informatics 15(4): 2446-2455 (2019) - [c76]Lingge Li, Nitish Nayak, Jianming Bian, Pierre Baldi:
Efficient Neutrino Oscillation Parameter Inference with Gaussian Process. AAAI 2019: 9967-9968 - [c75]Stephen McAleer, Forest Agostinelli, Alexander Shmakov
Solving the Rubik's Cube with Approximate Policy Iteration. ICLR (Poster) 2019 - [c74]Vaibhav Saini, Farima Farmahinifarahani, Yadong Lu, Di Yang, Pedro Martins, Hitesh Sajnani, Pierre Baldi, Cristina V. Lopes:
Towards automating precision studies of clone detectors. ICSE 2019: 49-59 - [c73]Pierre Baldi, Peter J. Sadowski, Zhiqin Lu:
Learning in the Machine: Random Backpropagation and the Deep Learning Channel (Extended Abstract). IJCAI 2019: 6348-6352 - [c72]Lingge Li, Dustin S. Pluta, Babak Shahbaba, Norbert Fortin, Hernando Ombao, Pierre Baldi:
Modeling Dynamic Functional Connectivity with Latent Factor Gaussian Processes. NeurIPS 2019: 8261-8271 - [i18]Pierre Baldi, Roman Vershynin:
The capacity of feedforward neural networks. CoRR abs/1901.00434 (2019) - [i17]John B. Lanier, Stephen McAleer, Pierre Baldi:
Curiosity-Driven Multi-Criteria Hindsight Experience Replay. CoRR abs/1906.03710 (2019) - [i16]Jordan Ott, Erik Linstead, Nicholas LaHaye, Pierre Baldi:
Learning in the Machine: To Share or Not to Share? CoRR abs/1909.11483 (2019) - [i15]Alexander Shmakov, John B. Lanier, Stephen McAleer, Rohan Achar, Cristina V. Lopes, Pierre Baldi:
ColosseumRL: A Framework for Multiagent Reinforcement Learning in N-Player Games. CoRR abs/1912.04451 (2019) - 2018
- [j123]Pierre Baldi
Learning in the machine: Random backpropagation and the deep learning channel. Artif. Intell. 260: 1-35 (2018) - [j122]Pierre Baldi
The inner and outer approaches to the design of recursive neural architectures. Data Min. Knowl. Discov. 32(1): 218-230 (2018) - [j121]Gregor Urban, Niranjan Subrahmanya, Pierre Baldi
Inner and Outer Recursive Neural Networks for Chemoinformatics Applications. J. Chem. Inf. Model. 58(2): 207-211 (2018) - [j120]Clara H. Eng, Tyler W. H. Backman
ClusterCAD: a computational platform for type I modular polyketide synthase design. Nucleic Acids Res. 46(Database-Issue): D509-D515 (2018) - [j119]Nicholas Ceglia, Yu Liu, Siwei Chen, Forest Agostinelli
CircadiOmics: circadian omic web portal. Nucleic Acids Res. 46(Webserver-Issue): W157-W162 (2018) - [j118]Pierre Baldi
Learning in the machine: Recirculation is random backpropagation. Neural Networks 108: 479-494 (2018) - [c71]Pierre Baldi, Roman Vershynin:
On Neuronal Capacity. NeurIPS 2018: 7740-7749 - [c70]Vaibhav Saini, Farima Farmahinifarahani, Yadong Lu, Pierre Baldi, Cristina V. Lopes:
Oreo: detection of clones in the twilight zone. ESEC/SIGSOFT FSE 2018: 354-365 - [i14]Stephen McAleer, Forest Agostinelli, Alexander Shmakov, Pierre Baldi:
Solving the Rubik's Cube Without Human Knowledge. CoRR abs/1805.07470 (2018) - [i13]Vaibhav Saini, Farima Farmahinifarahani, Yadong Lu, Pierre Baldi, Cristina V. Lopes:
Oreo: Detection of Clones in the Twilight Zone. CoRR abs/1806.05837 (2018) - [i12]Vaibhav Saini, Farima Farmahinifarahani, Yadong Lu, Di Yang, Pedro Martins, Hitesh Sajnani, Pierre Baldi, Cristina V. Lopes:
Towards Automating Precision Studies of Clone Detectors. CoRR abs/1812.05195 (2018) - 2017
- [j117]Yu Liu, Sha Sun, Timothy Bredy
MotifMap-RNA: a genome-wide map of RBP binding sites. Bioinform. 33(13): 2029-2031 (2017) - [j116]Juan Wang, Zhiyuan Fang, Ning Lang
A multi-resolution approach for spinal metastasis detection using deep Siamese neural networks. Comput. Biol. Medicine 84: 137-146 (2017) - [j115]Pierre Baldi, Peter J. Sadowski
Learning in the machine: The symmetries of the deep learning channel. Neural Networks 95: 110-133 (2017) - [j114]Juan Wang
Detecting Cardiovascular Disease from Mammograms With Deep Learning. IEEE Trans. Medical Imaging 36(5): 1172-1181 (2017) - [c69]Peter J. Sadowski
Deep Learning in the Natural Sciences: Applications to Physics. Braverman Readings in Machine Learning 2017: 269-297 - [c68]Forest Agostinelli
From Reinforcement Learning to Deep Reinforcement Learning: An Overview. Braverman Readings in Machine Learning 2017: 298-328 - [i11]Peter J. Sadowski, Balint Radics, Ananya, Yasunori Yamazaki, Pierre Baldi:
Efficient Antihydrogen Detection in Antimatter Physics by Deep Learning. CoRR abs/1706.01826 (2017) - [i10]Pierre Baldi, Peter J. Sadowski, Zhiqin Lu:
Learning in the Machine: the Symmetries of the Deep Learning Channel. CoRR abs/1712.08608 (2017) - 2016
- [j113]Clovis Galiez, Christophe N. Magnan, François Coste
VIRALpro: a tool to identify viral capsid and tail sequences. Bioinform. 32(9): 1405-1407 (2016) - [j112]Pierre Baldi, Teresa M. Przytycka
ISMB 2016 Proceedings. Bioinform. 32(12): 1-2 (2016) - [j111]Forest Agostinelli
What time is it? Deep learning approaches for circadian rhythms. Bioinform. 32(12): 8-17 (2016) - [j110]Forest Agostinelli, Nicholas Ceglia, Babak Shahbaba
What time is it? Deep learning approaches for circadian rhythms. Bioinform. 32(19): 3051 (2016) - [j109]Peter J. Sadowski
Synergies Between Quantum Mechanics and Machine Learning in Reaction Prediction. J. Chem. Inf. Model. 56(11): 2125-2128 (2016) - [j108]Pierre Baldi, Peter J. Sadowski
A theory of local learning, the learning channel, and the optimality of backpropagation. Neural Networks 83: 51-74 (2016) - [c67]Evan Racah, Seyoon Ko
Revealing Fundamental Physics from the Daya Bay Neutrino Experiment Using Deep Neural Networks. ICMLA 2016: 892-897 - [i9]Evan Racah, Seyoon Ko, Peter J. Sadowski, Wahid Bhimji, Craig Tull, Sang-Yun Oh, Pierre Baldi, Prabhat:
Revealing Fundamental Physics from the Daya Bay Neutrino Experiment using Deep Neural Networks. CoRR abs/1601.07621 (2016) - [i8]Pierre Baldi, Kyle Cranmer
Parameterized Machine Learning for High-Energy Physics. CoRR abs/1601.07913 (2016) - [i7]Pierre Baldi, Peter J. Sadowski, Zhiqin Lu:
Learning in the Machine: Random Backpropagation and the Learning Channel. CoRR abs/1612.02734 (2016) - 2015
- [j107]Vishal R. Patel, Nicholas Ceglia, Michael Zeller, Kristin Eckel-Mahan
The pervasiveness and plasticity of circadian oscillations: the coupled circadian-oscillators framework. Bioinform. 31(19): 3181-3188 (2015) - [j106]Alessandro Lusci, Michael R. Browning, David Fooshee, S. Joshua Swamidass
Accurate and efficient target prediction using a potency-sensitive influence-relevance voter. J. Cheminformatics 7: 63:1-63:13 (2015) - [c66]Forest Agostinelli
Learning Activation Functions to Improve Deep Neural Networks. ICLR (Workshop) 2015 - [i6]Pierre Baldi, Peter J. Sadowski:
The Ebb and Flow of Deep Learning: a Theory of Local Learning. CoRR abs/1506.06472 (2015) - 2014
- [j105]Pierre Baldi, Peter J. Sadowski
The dropout learning algorithm. Artif. Intell. 210: 78-122 (2014) - [j104]Ken Nagata, Arlo Z. Randall, Pierre Baldi:
Incorporating post-translational modifications and unnatural amino acids into high-throughput modeling of protein structures. Bioinform. 30(12): 1681-1689 (2014) - [j103]Christophe N. Magnan, Pierre Baldi:
SSpro/ACCpro 5: almost perfect prediction of protein secondary structure and relative solvent accessibility using profiles, machine learning and structural similarity. Bioinform. 30(18): 2592-2597 (2014) - [j102]Michael Zeller, Christophe N. Magnan, Vishal R. Patel, Paul Rigor, Leonard Sender, Pierre Baldi:
A Genomic Analysis Pipeline and Its Application to Pediatric Cancers. IEEE ACM Trans. Comput. Biol. Bioinform. 11(5): 826-839 (2014) - [c65]Davide Chicco
Deep autoencoder neural networks for gene ontology annotation predictions. BCB 2014: 533-540 - [c64]Peter J. Sadowski, Julian Collado, Daniel Whiteson, Pierre Baldi:
Deep Learning, Dark Knowledge, and Dark Matter. HEPML@NIPS 2014: 81-87 - [c63]Peter J. Sadowski, Daniel Whiteson, Pierre Baldi:
Searching for Higgs Boson Decay Modes with Deep Learning. NIPS 2014: 2393-2401 - [c62]Julian Yarkony, Thorsten Beier, Pierre Baldi, Fred A. Hamprecht:
Parallel Multicut Segmentation via Dual Decomposition. NFMCP 2014: 56-68 - [e1]Pierre Baldi, Wei Wang:
Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, BCB '14, Newport Beach, California, USA, September 20-23, 2014. ACM 2014, ISBN 978-1-4503-2894-4 [contents] - [i5]Pierre Baldi, Peter J. Sadowski, Daniel Whiteson:
Enhanced Higgs to $τ^+τ^-$ Searches with Deep Learning. CoRR abs/1410.3469 (2014) - [i4]Pierre Baldi, Kenji Fukumizu, Tomaso A. Poggio:
Deep Learning: Theory, Algorithms, and Applications (NII Shonan Meeting 2014-5). NII Shonan Meet. Rep. 2014 (2014) - 2013
- [j101]Ivan Chang, Pierre Baldi:
A unifying kinetic framework for modeling oxidoreductase-catalyzed reactions. Bioinform. 29(10): 1299-1307 (2013) - [j100]Alessandro Lusci, Gianluca Pollastri
Deep Architectures and Deep Learning in Chemoinformatics: The Prediction of Aqueous Solubility for Drug-Like Molecules. J. Chem. Inf. Model. 53(7): 1563-1575 (2013) - [j99]David Fooshee, Alessio Andronico, Pierre Baldi:
ReactionMap: An Efficient Atom-Mapping Algorithm for Chemical Reactions. J. Chem. Inf. Model. 53(11): 2812-2819 (2013) - [j98]Peter J. Sadowski
Small-Molecule 3D Structure Prediction Using Open Crystallography Data. J. Chem. Inf. Model. 53(12): 3127-3130 (2013) - [c61]Pierre Baldi, Peter J. Sadowski:
Understanding Dropout. NIPS 2013: 2814-2822 - [c60]Francesco Napolitano
An Adaptive Reference Point Approach to Efficiently Search Large Chemical Databases. WIRN 2013: 63-74 - 2012
- [j97]Pietro di Lena
Deep architectures for protein contact map prediction. Bioinform. 28(19): 2449-2457 (2012) - [j96]Pierre Baldi:
Boolean autoencoders and hypercube clustering complexity. Des. Codes Cryptogr. 65(3): 383-403 (2012) - [j95]Pierre Baldi, Cristina Videira Lopes:
The Universal Campus: An open virtual 3-D world infrastructure for research and education. eLearn Mag. 2012(4): 6 (2012) - [j94]Ramzi Nasr, Rares Vernica, Chen Li, Pierre Baldi:
Speeding Up Chemical Searches Using the Inverted Index: The Convergence of Chemoinformatics and Text Search Methods. J. Chem. Inf. Model. 52(4): 891-900 (2012) - [j93]Matthew A. Kayala, Pierre Baldi:
ReactionPredictor: Prediction of Complex Chemical Reactions at the Mechanistic Level Using Machine Learning. J. Chem. Inf. Model. 52(10): 2526-2540 (2012) - [j92]Matthew A. Kayala, Pierre Baldi:
Cyber-T web server: differential analysis of high-throughput data. Nucleic Acids Res. 40(Web-Server-Issue): 553-559 (2012) - [j91]Pierre Baldi, Zhiqin Lu:
Complex-valued autoencoders. Neural Networks 33: 136-147 (2012) - [c59]Pietro Di Lena, Pierre Baldi, Ken Nagata:
Deep Spatio-Temporal Architectures and Learning for Protein Structure Prediction. NIPS 2012: 521-529 - [c58]Pierre Baldi:
Autoencoders, Unsupervised Learning, and Deep Architectures. ICML Unsupervised and Transfer Learning 2012: 37-50 - 2011
- [b5]Pierre Baldi, G. Wesley Hatfield:
DNA Microarrays and Gene Expression - From Experiments to Data Analysis and Modeling. Cambridge University Press 2011, ISBN 978-0-521-17635-4, pp. I-XVI, 1-213 - [j90]Kenneth Daily
MotifMap: integrative genome-wide maps of regulatory motif sites for model species. BMC Bioinform. 12: 495 (2011) - [j89]Alessio Andronico, Arlo Z. Randall, Ryan W. Benz
Data-Driven High-Throughput Prediction of the 3-D Structure of Small Molecules: Review and Progress. J. Chem. Inf. Model. 51(4): 760-776 (2011) - [j88]Matthew A. Kayala, Chloé-Agathe Azencott
Learning to Predict Chemical Reactions. J. Chem. Inf. Model. 51(9): 2209-2222 (2011) - [j87]Pierre Baldi:
Data-Driven High-Throughput Prediction of the 3-D Structure of Small Molecules: Review and Progress. A Response to the Letter by the Cambridge Crystallographic Data Centre. J. Chem. Inf. Model. 51(12): 3029 (2011) - [c57]Pierre Baldi, Roberta Baronio, Emiliano De Cristofaro, Paolo Gasti, Gene Tsudik:
Countering GATTACA: efficient and secure testing of fully-sequenced human genomes. CCS 2011: 691-702 - [c56]Matthew A. Kayala, Pierre Baldi:
A Machine Learning Approach to Predict Chemical Reactions. NIPS 2011: 747-755 - [i3]David Eppstein, Michael T. Goodrich, Pierre Baldi:
Privacy-Enhanced Methods for Comparing Compressed DNA Sequences. CoRR abs/1107.3593 (2011) - [i2]Pierre Baldi, Sholeh Forouzan, Zhiqin Lu:
Complex-Valued Autoencoders. CoRR abs/1108.4135 (2011) - [i1]Pierre Baldi, Roberta Baronio, Emiliano De Cristofaro, Paolo Gasti, Gene Tsudik:
Countering Gattaca: Efficient and Secure Testing of Fully-Sequenced Human Genomes. CoRR abs/1110.2478 (2011) - 2010
- [j86]Sanjay Joshua Swamidass
A CROC stronger than ROC: measuring, visualizing and optimizing early retrieval. Bioinform. 26(10): 1348-1356 (2010) - [j85]Christophe N. Magnan, Michael Zeller, Matthew A. Kayala, Adam Vigil, Arlo Z. Randall, Philip L. Felgner, Pierre Baldi:
High-throughput prediction of protein antigenicity using protein microarray data. Bioinform. 26(23): 2936-2943 (2010) - [j84]Kenny Daily
Data structures and compression algorithms for high-throughput sequencing technologies. BMC Bioinform. 11: 514 (2010) - [j83]Pierre Baldi, Ramzi Nasr:
When is Chemical Similarity Significant? The Statistical Distribution of Chemical Similarity Scores and Its Extreme Values. J. Chem. Inf. Model. 50(7): 1205-1222 (2010) - [j82]Ramzi Nasr, Daniel S. Hirschberg, Pierre Baldi:
Hashing Algorithms and Data Structures for Rapid Searches of Fingerprint Vectors. J. Chem. Inf. Model. 50(8): 1358-1368 (2010) - [j81]Pierre Baldi, Laurent Itti:
Of bits and wows: A Bayesian theory of surprise with applications to attention. Neural Networks 23(5): 649-666 (2010) - [j80]Thomas C. Whisenant, David T. Ho, Ryan W. Benz
Computational Prediction and Experimental Verification of New MAP Kinase Docking Sites and Substrates Including Gli Transcription Factors. PLoS Comput. Biol. 6(8) (2010) - [j79]Sara Javanmardi, Cristina Videira Lopes, Pierre Baldi:
Modeling user reputation in wikis. Stat. Anal. Data Min. 3(2): 126-139 (2010) - [c55]Francesco Napolitano
A scalable reference-point based algorithm to efficiently search large chemical databases. IJCNN 2010: 1-6 - [c54]Erik Linstead, Lindsey Hughes, Cristina Videira Lopes, Pierre Baldi:
Information-Theoretic Metrics for Project-Level Scattering and Tangling. SEKE 2010: 141-146 - [c53]Pierre Baldi, Chloé-Agathe Azencott, Sanjay Joshua Swamidass
Bridging the Gap Between Neural Network and Kernel Methods: Applications to Drug Discovery. WIRN 2010: 3-13
2000 – 2009
- 2009
- [j78]Raja Jurdak
Software-driven sensor networks for short-range shallow water applications. Ad Hoc Networks 7(5): 837-848 (2009) - [j77]Xiaohui Xie, Paul Rigor, Pierre Baldi:
MotifMap: a human genome-wide map of candidate regulatory motif sites. Bioinform. 25(2): 167-174 (2009) - [j76]Marty C. Brandon, Douglas C. Wallace, Pierre Baldi:
Data structures and compression algorithms for genomic sequence data. Bioinform. 25(14): 1731-1738 (2009) - [j75]Christophe N. Magnan, Arlo Z. Randall, Pierre Baldi:
SOLpro: accurate sequence-based prediction of protein solubility. Bioinform. 25(17): 2200-2207 (2009) - [j74]Erik Linstead, Sushil Krishna Bajracharya, Trung Chi Ngo, Paul Rigor, Cristina Videira Lopes, Pierre Baldi:
Sourcerer: mining and searching internet-scale software repositories. Data Min. Knowl. Discov. 18(2): 300-336 (2009) - [j73]Ramzi Nasr, Sanjay Joshua Swamidass
Large scale study of multiple-molecule queries. J. Cheminformatics 1: 7 (2009) - [j72]S. Joshua Swamidass
Influence Relevance Voting: An Accurate And Interpretable Virtual High Throughput Screening Method. J. Chem. Inf. Model. 49(4): 756-766 (2009) - [j71]Pierre Baldi, Daniel S. Hirschberg:
An Intersection Inequality Sharper than the Tanimoto Triangle Inequality for Efficiently Searching Large Databases. J. Chem. Inf. Model. 49(8): 1866-1870 (2009) - [j70]Jonathan H. Chen
No Electron Left Behind: A Rule-Based Expert System To Predict Chemical Reactions and Reaction Mechanisms. J. Chem. Inf. Model. 49(9): 2034-2043 (2009) - [c52]Sara Javanmardi, Yasser Ganjisaffar, Cristina Videira Lopes, Pierre Baldi:
User contribution and trust in Wikipedia. CollaborateCom 2009: 1-6 - [c51]Erik Linstead, Lindsey Hughes, Cristina Videira Lopes, Pierre Baldi:
Capturing Java naming conventions with first-order Markov models. ICPC 2009: 313-314 - [c50]Erik Linstead, Pierre Baldi:
Mining the coherence of GNOME bug reports with statistical topic models. MSR 2009: 99-102 - [c49]Joel Ossher, Sushil Krishna Bajracharya, Erik Linstead, Pierre Baldi, Cristina Videira Lopes:
SourcererDB: An aggregated repository of statically analyzed and cross-linked open source Java projects. MSR 2009: 183-186 - 2008
- [j69]Arlo Z. Randall, Jianlin Cheng
TMBpro: secondary structure, beta-contact and tertiary structure prediction of transmembrane beta-barrel proteins. Bioinform. 24(4): 513-520 (2008) - [j68]Michael J. Sweredoski, Pierre Baldi:
PEPITO: improved discontinuous B-cell epitope prediction using multiple distance thresholds and half sphere exposure. Bioinform. 24(12): 1459-1460 (2008) - [j67]Ryan W. Benz
Discovery of Power-Laws in Chemical Space. J. Chem. Inf. Model. 48(6): 1138-1151 (2008) - [j66]Pierre Baldi, Daniel S. Hirschberg, Ramzi Nasr:
Speeding Up Chemical Database Searches Using a Proximity Filter Based on the Logical Exclusive OR. J. Chem. Inf. Model. 48(7): 1367-1378 (2008) - [j65]Lin Wu, Pierre Baldi:
Learning to play Go using recursive neural networks. Neural Networks 21(9): 1392-1400 (2008) - [c48]Daniel S. Hirschberg, Pierre Baldi:
Effective Compression of Monotone and Quasi-Monotone Sequences of Integers. DCC 2008: 520 - [c47]Erik Linstead, Cristina Videira Lopes, Pierre Baldi:
An Application of Latent Dirichlet Allocation to Analyzing Software Evolution. ICMLA 2008: 813-818 - [c46]Pierre Baldi, Ryan W. Benz
BLASTing small molecules - statistics and extreme statistics of chemical similarity scores. ISMB 2008: 357-365 - [c45]Pierre Baldi, Cristina Videira Lopes, Erik Linstead, Sushil Krishna Bajracharya:
A theory of aspects as latent topics. OOPSLA 2008: 543-562 - 2007
- [j64]Jonathan H. Chen
ChemDB update - full-text search and virtual chemical space. Bioinform. 23(17): 2348-2351 (2007) - [j63]Michael J. Sweredoski, Kevin J. Donovan, Bao D. Nguyen, A. J. Shaka, Pierre Baldi:
Minimizing the overlap problem in protein NMR: a computational framework for precision amino acid labeling. Bioinform. 23(21): 2829-2835 (2007) - [j62]Jianlin Cheng
Improved residue contact prediction using support vector machines and a large feature set. BMC Bioinform. 8 (2007) - [j61]S. Joshua Swamidass
Bounds and Algorithms for Fast Exact Searches of Chemical Fingerprints in Linear and Sublinear Time. J. Chem. Inf. Model. 47(2): 302-317 (2007) - [j60]S. Joshua Swamidass
Mathematical Correction for Fingerprint Similarity Measures to Improve Chemical Retrieval. J. Chem. Inf. Model. 47(3): 952-964 (2007) - [j59]Chloé-Agathe Azencott
One- to Four-Dimensional Kernels for Virtual Screening and the Prediction of Physical, Chemical, and Biological Properties. J. Chem. Inf. Model. 47(3): 965-974 (2007) - [j58]Pierre Baldi, Ryan W. Benz
Lossless Compression of Chemical Fingerprints Using Integer Entropy Codes Improves Storage and Retrieval. J. Chem. Inf. Model. 47(6): 2098-2109 (2007) - [j57]Eduardo Ruiz-Pesini, Marie T. Lott
An enhanced MITOMAP with a global mtDNA mutational phylogeny. Nucleic Acids Res. 35(Database-Issue): 823-828 (2007) - [j56]Raja Jurdak
Adaptive Low Power Listening for Wireless Sensor Networks. IEEE Trans. Mob. Comput. 6(8): 988-1004 (2007) - [c44]Pierre Baldi:
Machine Learning Challenges in Chemoinformatics and Drug Screening and Design. ICMLA 2007 - [c43]Suman Sundaresh, Arlo Z. Randall, Berkay Unal, Jeannine M. Petersen, John T. Belisle
From protein microarrays to diagnostic antigen discovery: a study of the pathogen Francisella tularensis. ISMB/ECCB (Supplement of Bioinformatics) 2007: 508-518 - [c42]Erik Linstead, Paul Rigor, Sushil Krishna Bajracharya, Cristina Videira Lopes, Pierre Baldi:
Mining concepts from code with probabilistic topic models. ASE 2007: 461-464 - [c41]Otávio Augusto Lazzarini Lemos
CodeGenie: using test-cases to search and reuse source code. ASE 2007: 525-526 - [c40]Pierre Baldi:
Learning and Charting Chemical Space with Strings and Graphs: Challenges and Opportunities for AI and Machine Learning. MLG 2007 - [c39]Erik Linstead, Paul Rigor, Sushil Krishna Bajracharya, Cristina Videira Lopes, Pierre Baldi:
Mining Eclipse Developer Contributions via Author-Topic Models. MSR 2007: 30 - [c38]Erik Linstead, Paul Rigor, Sushil Krishna Bajracharya, Cristina Videira Lopes, Pierre Baldi:
Mining Internet-Scale Software Repositories. NIPS 2007: 929-936 - 2006
- [j55]Jianlin Cheng
A machine learning information retrieval approach to protein fold recognition. Bioinform. 22(12): 1456-1463 (2006) - [j54]Suman Sundaresh, Denise L. Doolan
Identification of humoral immune responses in protein microarrays using DNA microarray data analysis techniques. Bioinform. 22(14): 1760-1766 (2006) - [j53]Jianlin Cheng
DOMpro: Protein Domain Prediction Using Profiles, Secondary Structure, Relative Solvent Accessibility, and Recursive Neural Networks. Data Min. Knowl. Discov. 13(1): 1-10 (2006) - [j52]Gianluca Pollastri
Modular DAG-RNN Architectures for Assembling Coarse Protein Structures. J. Comput. Biol. 13(3): 631-650 (2006) - [j51]Eric T. Wang, Greg Kodama, Pierre Baldi, Robert K. Moyzis:
Global landscape of recent inferred Darwinian selection for Homo sapiens. Proc. Natl. Acad. Sci. USA 103(1): 135-140 (2006) - [j50]Cristina Videira Lopes, Amir Haghighat, Atri Mandal, Tony Givargis, Pierre Baldi:
Localization of off-the-shelf mobile devices using audible sound: architectures, protocols and performance assessment. ACM SIGMOBILE Mob. Comput. Commun. Rev. 10(2): 38-50 (2006) - [j49]Samuel A. Danziger, Sanjay Joshua Swamidass
Functional Census of Mutation Sequence Spaces: The Example of p53 Cancer Rescue Mutants. IEEE ACM Trans. Comput. Biol. Bioinform. 3(2): 114-125 (2006) - [c37]Lin Wu, Pierre Baldi:
A Scalable Machine Learning Approach to Go. NIPS 2006: 1521-1528 - [c36]Sushil Krishna Bajracharya, Trung Chi Ngo, Erik Linstead, Yimeng Dou, Paul Rigor, Pierre Baldi, Cristina Videira Lopes:
Sourcerer: a search engine for open source code supporting structure-based search. OOPSLA Companion 2006: 681-682 - 2005
- [j48]Steven E. Hampson, Brandon S. Gaut, Pierre Baldi:
Statistical detection of chromosomal homology using shared-gene density alone. Bioinform. 21(8): 1339-1348 (2005) - [j47]Jonathan H. Chen
ChemDB: a public database of small molecules and related chemoinformatics resources. Bioinform. 21(22): 4133-4139 (2005) - [j46]Jianlin Cheng
Accurate Prediction of Protein Disordered Regions by Mining Protein Structure Data. Data Min. Knowl. Discov. 11(3): 213-222 (2005) - [j45]Jianlin Cheng
Sigmoid: A Software Infrastructure for Pathway Bioinformatics and Systems Biology. IEEE Intell. Syst. 20(3): 68-75 (2005) - [j44]Suman Sundaresh, She-pin Hung, G. Wesley Hatfield, Pierre Baldi:
How noisy and replicable are DNA microarray data? Int. J. Bioinform. Res. Appl. 1(1): 31-50 (2005) - [j43]Marty C. Brandon, Marie T. Lott
MITOMAP: a human mitochondrial genome database - 2004 update. Nucleic Acids Res. 33(Database-Issue): 611-613 (2005) - [j42]Jianlin Cheng
SCRATCH: a protein structure and structural feature prediction server. Nucleic Acids Res. 33(Web-Server-Issue): 72-76 (2005) - [j41]Pierre Baldi, Michal Rosen-Zvi
On the relationship between deterministic and probabilistic directed Graphical models: From Bayesian networks to recursive neural networks. Neural Networks 18(8): 1080-1086 (2005) - [j40]Liva Ralaivola, Sanjay Joshua Swamidass
Graph kernels for chemical informatics. Neural Networks 18(8): 1093-1110 (2005) - [j39]Raja Jurdak
U-MAC: a proactive and adaptive UWB medium access control protocol. Wirel. Commun. Mob. Comput. 5(5): 551-566 (2005) - [c35]Atri Mandal, Cristina V. Lopes, Tony Givargis, Amir Haghighat, Raja Jurdak
Beep: 3D indoor positioning using audible sound. CCNC 2005: 348-353 - [c34]Laurent Itti, Pierre Baldi:
A Principled Approach to Detecting Surprising Events in Video. CVPR (1) 2005: 631-637 - [c33]Liva Ralaivola, Lin Wu, Pierre Baldi:
SVM and pattern-enriched common fate graphs for the game of go. ESANN 2005: 485-490 - [c32]Raja Jurdak
State-Driven Energy Optimization in Wireless Sensor Networks. Systems Communications 2005: 356-363 - [c31]Jianlin Cheng
Three-stage prediction of protein ?-sheets by neural networks, alignments and graph algorithms. ISMB (Supplement of Bioinformatics) 2005: 75-84 - [c30]Sanjay Joshua Swamidass
Kernels for small molecules and the prediction of mutagenicity, toxicity and anti-cancer activity. ISMB (Supplement of Bioinformatics) 2005: 359-368 - [c29]Laurent Itti, Pierre Baldi:
Bayesian Surprise Attracts Human Attention. NIPS 2005: 547-554 - [c28]Sanjay Joshua Swamidass, Pierre Baldi:
Statistical Distribution of Chemical Fingerprints. WILF 2005: 11-18 - 2004
- [j38]Arlo Z. Randall, Pierre Baldi, Luis P. Villarreal:
Structural proteomics of the poxvirus family. Artif. Intell. Medicine 31(2): 105-115 (2004) - [j37]Yimeng Dou, Pierre-François Baisnée, Gianluca Pollastri
ICBS: a database of interactions between protein chains mediated by ?-sheet formation. Bioinform. 20(16): 2767-2777 (2004) - [j36]Raja Jurdak
A survey, classification and comparative analysis of medium access control protocols for ad hoc networks. IEEE Commun. Surv. Tutorials 6(1-4): 2-16 (2004) - [j35]Yann Guermeur, Gianluca Pollastri
Combining protein secondary structure prediction models with ensemble methods of optimal complexity. Neurocomputing 56: 305-327 (2004) - [c27]Raja Jurdak
An Acoustic Identification Scheme for Location Systems. ICPS 2004: 61-70 - [c26]Pierre Baldi, Jianlin Cheng, Alessandro Vullo:
Large-Scale Prediction of Disulphide Bond Connectivity. NIPS 2004: 97-104 - 2003
- [b4]Pierre Baldi, Paolo Frasconi, Padhraic Smyth:
Modeling the Internet and the Web: Probabilistic Method and Algorithms. John Wiley 2003, ISBN 0-470-84906-1 - [j34]Pierre Baldi, Gianluca Pollastri:
The Principled Design of Large-Scale Recursive Neural Network Architectures--DAG-RNNs and the Protein Structure Prediction Problem. J. Mach. Learn. Res. 4: 575-602 (2003) - [c25]Pierre Baldi:
Machine Learning Methods for Computational Proteomics and Beyond. AI 2003: 8 - 2002
- [b3]Pierre Baldi:
The Shattered Self - The End of Natural Evolution. MIT Press 2002, ISBN 978-0-262-52334-9, pp. I-XII, 1-259 - [j33]Steven Hampson, Dennis F. Kibler, Pierre Baldi:
Distribution patterns of over-represented k-mers in non-coding yeast DNA. Bioinform. 18(4): 513-528 (2002) - [j32]Pierre-François Baisnée, Steven Hampson, Pierre Baldi:
Why are complementary DNA strands symmetric? Bioinform. 18(8): 1021-1033 (2002) - [j31]Pierre Baldi, Gianluca Pollastri
A Machine-Learning Strategy for Protein Analysis. IEEE Intell. Syst. 17(2): 28-35 (2002) - [j30]Pierre Baldi, Luca De Nardis
Modeling and optimization of UWB communication networks through a flexible cost function. IEEE J. Sel. Areas Commun. 20(9): 1733-1744 (2002) - [c24]Gianluca Pollastri, Pierre Baldi:
Prediction of contact maps by GIOHMMs and recurrent neural networks using lateral propagation from all four cardinal corners. ISMB 2002: 62-70 - [c23]Gianluca Pollastri, Pierre Baldi, Alessandro Vullo, Paolo Frasconi:
Prediction of Protein Topologies Using Generalized IOHMMS and RNNs. NIPS 2002: 1449-1456 - 2001
- [b2]Pierre Baldi, Søren Brunak:
Bioinformatics - the machine learning approach (2. ed.). MIT Press 2001, ISBN 978-0-262-02506-5, pp. I-XXI, 1-452 - [j29]Pierre-François Baisnée, Pierre Baldi, Søren Brunak, Anders Gorm Pedersen
Flexibility of the genetic code with respect to DNA structure. Bioinform. 17(3): 237-248 (2001) - [j28]Pierre Baldi, Anthony D. Long:
A Bayesian framework for the analysis of microarray expression data: regularized t -test and statistical inferences of gene changes. Bioinform. 17(6): 509-519 (2001) - [c22]Gianluca Pollastri, Pierre Baldi, Piero Fariselli, Rita Casadio:
Improved prediction of the number of residue contacts in proteins by recurrent neural networks. ISMB (Supplement of Bioinformatics) 2001: 234-242 - [c21]Pierre Baldi, Søren Brunak:
Bioethics, Fiction Science, and the Future of Mankind - Session Introduction. Pacific Symposium on Biocomputing 2001 - [c20]Pierre Baldi, Richard H. Lathrop:
DNA Structure, Protein-DNA Interactions, and DNA-Protein Expression - Session Introduction. Pacific Symposium on Biocomputing 2001: 101-102 - [c19]Pierre Baldi, Søren Brunak, Paolo Frasconi, Gianluca Pollastri
Bidirectional Dynamics for Protein Secondary Structure Prediction. Sequence Learning 2001: 80-104 - 2000
- [b1]Pierre Baldi, Søren Brunak:
Bioinformatics - the machine learning approach. MIT Press 2000, ISBN 978-0-262-02442-6, pp. 1-44 - [j27]Pierre Baldi:
On the convergence of a clustering algorithm for protein-coding regions in microbial genomes. Bioinform. 16(4): 367-371 (2000) - [j26]Pierre Baldi, Søren Brunak, Yves Chauvin, Claus A. F. Andersen, Henrik Nielsen
Assessing the accuracy of prediction algorithms for classification: an overview. Bioinform. 16(5): 412-424 (2000) - [j25]Pierre Baldi, Pierre-François Baisnée:
Sequence analysis by additive scales: DNA structure for sequences and repeats of all lengths. Bioinform. 16(10): 865-889 (2000) - [c18]Pierre Baldi, Gianluca Pollastri
Protein β-Sheet Partner Prediction by Neural Networks. ANNIMAB 2000: 3-9 - [c17]Pierre Baldi, Gianluca Pollastri, Claus A. F. Andersen, Søren Brunak:
Matching Protein b-Sheet Partners by Feedforward and Recurrent Neural Networks. ISMB 2000: 25-36 - [c16]Steven Hampson, Pierre Baldi, Dennis F. Kibler, Suzanne B. Sandmeyer:
Analysis of Yeast's ORF Upstream Regions by Parallel Processing, Microarrays, and Computational Methods. ISMB 2000: 190-201
1990 – 1999
- 1999
- [j24]Pierre Baldi, Søren Brunak, Yves Chauvin, Anders Gorm Pedersen
Structural basis for triplet repeat disorders: a computational analysis. Bioinform. 15(11): 918-929 (1999) - [j23]Pierre Baldi, Søren Brunak, Paolo Frasconi, Giovanni Soda, Gianluca Pollastri
Exploiting the past and the future in protein secondary structure prediction. Bioinform. 15(11): 937-946 (1999) - [j22]Anders Gorm Pedersen
The Biology of Eukaryotic Promoter Prediction - A Review. Comput. Chem. 23(3-4): 191-207 (1999) - 1998
- [j21]Pierre Baldi, Michael C. Vanier, James M. Bower:
On the Use of Bayesian Methods for Evaluating Compartmental Neural Models. J. Comput. Neurosci. 5(3): 285-314 (1998) - [c15]Pierre Baldi, Søren Brunak, Yves Chauvin, Anders Gorm Pedersen:
Computational Applications of DNA Structural Scales. ISMB 1998: 35-42 - 1997
- [c14]Pierre Baldi:
Probabilistic Models of Neuronal Spike Trains. Summer School on Neural Networks 1997: 198-228 - 1996
- [j20]Pierre Baldi, Yves Chauvin:
Hybrid Modeling, HMM/NN Architectures, and Protein Applications. Neural Comput. 8(7): 1541-1565 (1996) - [c13]Anders Gorm Pedersen, Pierre Baldi, Søren Brunak, Yves Chauvin:
Characterization of Prokaryotic and Eukaryotic Promoters Using Hidden Markov Models. ISMB 1996: 182-191 - 1995
- [j19]Pierre Baldi:
Substitution Matrices and Hidden Markov Models. J. Comput. Biol. 2(3): 487-491 (1995) - [j18]Pierre Baldi:
Gradient descent learning algorithm overview: a general dynamical systems perspective. IEEE Trans. Neural Networks 6(1): 182-195 (1995) - [j17]Pierre Baldi, Kurt Hornik
Learning in linear neural networks: a survey. IEEE Trans. Neural Networks 6(4): 837-858 (1995) - [c12]Pierre Baldi, Søren Brunak, Yves Chauvin, Jacob Engelbrecht, Anders Krogh:
Periodic Sequence Patterns in Human Exons. ISMB 1995: 30-38 - [c11]Pierre Baldi, Yves Chauvin:
Protein Modeling with Hybrid Hidden Markov Model/Neural Network Architectures. ISMB 1995: 39-47 - [c10]Pierre Baldi, Kurt Hornik:
Universal Approximnation and Learning of Trajectories Using Oscillators. NIPS 1995: 451-457 - 1994
- [j16]Pierre Baldi, Yves Chauvin:
Hidden Markov Models of the G-Protein-Coupled Receptor Family. J. Comput. Biol. 1(4): 311-336 (1994) - [j15]Pierre Baldi, Yves Chauvin:
Smooth On-Line Learning Algorithms for Hidden Markov Models. Neural Comput. 6(2): 307-318 (1994) - [j14]Pierre Baldi, Amir F. Atiya
How delays affect neural dynamics and learning. IEEE Trans. Neural Networks 5(4): 612-621 (1994) - [c9]Padhraic Smyth, Usama M. Fayyad, Michael C. Burl, Pietro Perona, Pierre Baldi:
Inferring Ground Truth from Subjective Labelling of Venus Images. NIPS 1994: 1085-1092 - 1993
- [j13]Pierre Baldi, Yves Chauvin:
Neural Networks for Fingerprint Recognition. Neural Comput. 5(3): 402-418 (1993) - [j12]Pierre Baldi, Santosh S. Venkatesh:
Random interactions in higher order neural networks. IEEE Trans. Inf. Theory 39(1): 274-283 (1993) - [c8]Nikzad Benny Toomarian, Pierre Baldi:
Trajectory learning using hierarchy of oscillatory modules. ESANN 1993 - [c7]Pierre Baldi, Nikzad Benny Toomarian:
Learning trajectories with a hierarchy of oscillatory modules. ICNN 1993: 1172-1176 - [c6]Pierre Baldi, Søren Brunak, Yves Chauvin, Jacob Engelbrecht, Anders Krogh:
Hidden Markov Models for Human Genes. NIPS 1993: 761-768 - 1992
- [c5]Pierre Baldi, Yves Chauvin, Tim Hunkapiller, Marcella A. McClure:
Hidden Markov Models in Molecular Biology: New Algorithms and Applications. NIPS 1992: 747-754 - 1991
- [j11]Santosh S. Venkatesh, Pierre Baldi:
Programmed interactions in higher-order neural networks: Maximal capacity. J. Complex. 7(3): 316-337 (1991) - [j10]Santosh S. Venkatesh, Pierre Baldi:
Programmed interactions in higher-order neural networks: The outer-product algorithm. J. Complex. 7(4): 443-479 (1991) - [j9]Pierre Baldi, Fernando J. Pineda:
Contrastive Learning and Neural Oscillations. Neural Comput. 3(4): 526-545 (1991) - [j8]Pierre Baldi, Yves Chauvin:
Temporal Evolution of Generalization during Learning in Linear Networks. Neural Comput. 3(4): 589-603 (1991) - 1990
- [j7]Pierre Baldi:
On a generalized family of colorings. Graphs Comb. 6(2): 95-110 (1990) - [j6]Pierre Baldi, Ronny Meir:
Computing with Arrays of Coupled Oscillators: An Application to Preattentive Texture Discrimination. Neural Comput. 2(4): 458-471 (1990) - [c4]Pierre Baldi:
Computing with Arrays of Bell-Shaped and Sigmoid Functions. NIPS 1990: 735-742
1980 – 1989
- 1989
- [j5]Amir F. Atiya, Pierre Baldi:
Oscillations and Synchronizations in Neural Networks: an Exploration of the Labeling Hypothesis. Int. J. Neural Syst. 1(2): 103-124 (1989) - [j4]Pierre Baldi, Kurt Hornik
Neural networks and principal component analysis: Learning from examples without local minima. Neural Networks 2(1): 53-58 (1989) - [c3]Pierre Baldi, Yosef Rinott, Charles Stein:
On the Distribution of the Number of Local Minima of a Random Function on a Graph. NIPS 1989: 727-732 - 1988
- [j3]Pierre Baldi:
Group Actions and Learning for a Family of Automata. J. Comput. Syst. Sci. 36(1): 1-15 (1988) - [j2]Pierre Baldi:
Neural Networks, Acyclic Orientations of the Hypercube, and Sets of Orthogonal Vectors. SIAM J. Discret. Math. 1(1): 1-13 (1988) - [j1]Pierre Baldi:
Neural networks, orientations of the hypercube, and algebraic threshold functions. IEEE Trans. Inf. Theory 34(3): 523-530 (1988) - [c2]Pierre Baldi:
Linear Learning: Landscapes and Algorithms. NIPS 1988: 65-72 - 1987
- [c1]Pierre Baldi, Santosh S. Venkatesh:
On Properties of Networks of Neuron-Like Elements. NIPS 1987: 41-51
Coauthor Index
Cristina V. Lopes
aka: Cristina Videira Lopes
aka: Cristina Videira Lopes
Stephen McAleer
aka: Stephen Marcus McAleer
aka: Stephen Marcus McAleer
Sanjay Joshua Swamidass
aka: S. Joshua Swamidass
aka: S. Joshua Swamidass
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Unpaywalled article links
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Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from ,
, and
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and
to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from .
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-10-10 22:16 CEST by the dblp team
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