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Tyler L. Hayes
Person information
- affiliation: NAVER LABS Europe
- affiliation (former): Rochester Institute of Technology, NY, USA
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2020 – today
- 2024
- [j3]Eli Verwimp, Rahaf Aljundi, Shai Ben-David, Matthias Bethge, Andrea Cossu, Alexander Gepperth, Tyler L. Hayes, Eyke Hüllermeier, Christopher Kanan, Dhireesha Kudithipudi, Christoph H. Lampert, Martin Mundt, Razvan Pascanu, Adrian Popescu, Andreas S. Tolias, Joost van de Weijer, Bing Liu, Vincenzo Lomonaco, Tinne Tuytelaars, Gido M. van de Ven:
Continual Learning: Applications and the Road Forward. Trans. Mach. Learn. Res. 2024 (2024) - [c19]Mingxuan Liu, Tyler L. Hayes, Elisa Ricci, Gabriela Csurka, Riccardo Volpi:
SHiNe: Semantic Hierarchy Nexus for Open-Vocabulary Object Detection. CVPR 2024: 16634-16644 - [i23]Tyler L. Hayes, César R. de Souza, Namil Kim, Jiwon Kim, Riccardo Volpi, Diane Larlus:
PANDAS: Prototype-based Novel Class Discovery and Detection. CoRR abs/2402.17420 (2024) - [i22]Mingxuan Liu, Tyler L. Hayes, Elisa Ricci, Gabriela Csurka, Riccardo Volpi:
SHiNe: Semantic Hierarchy Nexus for Open-vocabulary Object Detection. CoRR abs/2405.10053 (2024) - [i21]Gabriela Csurka, Tyler L. Hayes, Diane Larlus, Riccardo Volpi:
What could go wrong? Discovering and describing failure modes in computer vision. CoRR abs/2408.04471 (2024) - 2023
- [j2]Md Yousuf Harun, Jhair Gallardo, Tyler L. Hayes, Ronald Kemker, Christopher Kanan:
SIESTA: Efficient Online Continual Learning with Sleep. Trans. Mach. Learn. Res. 2023 (2023) - [c18]Martin Mundt, Keiland W. Cooper, Devendra Singh Dhami, Adèle H. Ribeiro, James Seale Smith, Alexis Bellot, Tyler L. Hayes:
Continual Causality: A Retrospective of the Inaugural AAAI-23 Bridge Program. AAAI Bridge Program 2023: 1-10 - [c17]Md Yousuf Harun, Jhair Gallardo, Tyler L. Hayes, Christopher Kanan:
How Efficient Are Today's Continual Learning Algorithms? CVPR Workshops 2023: 2431-2436 - [e1]Martin Mundt, Keiland W. Cooper, Devendra Singh Dhami, Adèle H. Ribeiro, James Seale Smith, Alexis Bellot, Tyler L. Hayes:
AAAI Bridge Program on Continual Causality, 7-8 February 2023, Washington, DC, USA. Proceedings of Machine Learning Research 208, PMLR 2023 [contents] - [i20]Md Yousuf Harun, Jhair Gallardo, Tyler L. Hayes, Ronald Kemker, Christopher Kanan:
SIESTA: Efficient Online Continual Learning with Sleep. CoRR abs/2303.10725 (2023) - [i19]Md Yousuf Harun, Jhair Gallardo, Tyler L. Hayes, Christopher Kanan:
How Efficient Are Today's Continual Learning Algorithms? CoRR abs/2303.18171 (2023) - [i18]Eli Verwimp, Rahaf Aljundi, Shai Ben-David, Matthias Bethge, Andrea Cossu, Alexander Gepperth, Tyler L. Hayes, Eyke Hüllermeier, Christopher Kanan, Dhireesha Kudithipudi, Christoph H. Lampert, Martin Mundt, Razvan Pascanu, Adrian Popescu, Andreas S. Tolias, Joost van de Weijer, Bing Liu, Vincenzo Lomonaco, Tinne Tuytelaars, Gido M. van de Ven:
Continual Learning: Applications and the Road Forward. CoRR abs/2311.11908 (2023) - 2022
- [c16]Indranil Sur
, Zachary Daniels
System Design for an Integrated Lifelong Reinforcement Learning Agent for Real-Time Strategy Games. AIMLSystems 2022: 12:1-12:9 - [c15]Tyler L. Hayes, Maximilian Nickel, Christopher Kanan, Ludovic Denoyer, Arthur Szlam:
Can I see an Example? Active Learning the Long Tail of Attributes and Relations. BMVC 2022: 134 - [c14]Tyler L. Hayes, Christopher Kanan:
Online Continual Learning for Embedded Devices. CoLLAs 2022: 744-766 - [c13]Zachary Alan Daniels, Aswin Raghavan, Jesse Hostetler, Abrar Rahman, Indranil Sur, Michael R. Piacentino, Ajay Divakaran, Roberto Corizzo, Kamil Faber, Nathalie Japkowicz, Michael Baron, James Seale Smith, Sahana Pramod Joshi, Zsolt Kira, Cameron Ethan Taylor, Mustafa Burak Gurbuz, Constantine Dovrolis, Tyler L. Hayes, Christopher Kanan, Jhair Gallardo:
Model-Free Generative Replay for Lifelong Reinforcement Learning: Application to Starcraft-2. CoLLAs 2022: 1120-1145 - [i17]Tyler L. Hayes, Maximilian Nickel
Can I see an Example? Active Learning the Long Tail of Attributes and Relations. CoRR abs/2203.06215 (2022) - [i16]Tyler L. Hayes, Christopher Kanan:
Online Continual Learning for Embedded Devices. CoRR abs/2203.10681 (2022) - [i15]Indranil Sur, Zachary Daniels, Abrar Rahman, Kamil Faber, Gianmarco J. Gallardo, Tyler L. Hayes, Cameron E. Taylor, Mustafa Burak Gurbuz, James Seale Smith, Sahana Pramod Joshi, Nathalie Japkowicz
System Design for an Integrated Lifelong Reinforcement Learning Agent for Real-Time Strategy Games. CoRR abs/2212.04603 (2022) - 2021
- [j1]Tyler L. Hayes, Giri P. Krishnan, Maxim Bazhenov, Hava T. Siegelmann, Terrence J. Sejnowski, Christopher Kanan:
Replay in Deep Learning: Current Approaches and Missing Biological Elements. Neural Comput. 33(11): 2908-2950 (2021) - [c12]Gianmarco J. Gallardo, Tyler L. Hayes, Christopher Kanan:
Self-Supervised Training Enhances Online Continual Learning. BMVC 2021: 168 - [c11]Tyler L. Hayes, Christopher Kanan:
Selective Replay Enhances Learning in Online Continual Analogical Reasoning. CVPR Workshops 2021: 3502-3512 - [c10]Vincenzo Lomonaco
Avalanche: An End-to-End Library for Continual Learning. CVPR Workshops 2021: 3600-3610 - [i14]Tyler L. Hayes, Christopher Kanan:
Selective Replay Enhances Learning in Online Continual Analogical Reasoning. CoRR abs/2103.03987 (2021) - [i13]Jhair Gallardo, Tyler L. Hayes, Christopher Kanan:
Self-Supervised Training Enhances Online Continual Learning. CoRR abs/2103.14010 (2021) - [i12]Vincenzo Lomonaco, Lorenzo Pellegrini, Andrea Cossu, Antonio Carta, Gabriele Graffieti, Tyler L. Hayes, Matthias De Lange, Marc Masana, Jary Pomponi, Gido M. van de Ven, Martin Mundt, Qi She, Keiland W. Cooper
Avalanche: an End-to-End Library for Continual Learning. CoRR abs/2104.00405 (2021) - [i11]Tyler L. Hayes, Giri P. Krishnan, Maxim Bazhenov, Hava T. Siegelmann, Terrence J. Sejnowski, Christopher Kanan:
Replay in Deep Learning: Current Approaches and Missing Biological Elements. CoRR abs/2104.04132 (2021) - [i10]Yipeng Zhang, Tyler L. Hayes, Christopher Kanan:
Disentangling Transfer and Interference in Multi-Domain Learning. CoRR abs/2107.05445 (2021) - 2020
- [c9]Manoj Acharya, Tyler L. Hayes, Christopher Kanan:
RODEO: Replay for Online Object Detection. BMVC 2020 - [c8]Tyler L. Hayes, Christopher Kanan:
Lifelong Machine Learning with Deep Streaming Linear Discriminant Analysis. CVPR Workshops 2020: 887-896 - [c7]Ryne Roady, Tyler L. Hayes, Hitesh Vaidya, Christopher Kanan:
Stream-51: Streaming Classification and Novelty Detection from Videos. CVPR Workshops 2020: 925-934 - [c6]Ryne Roady, Tyler L. Hayes, Christopher Kanan:
Improved Robustness to Open Set Inputs via Tempered Mixup. ECCV Workshops (1) 2020: 186-201 - [c5]Tyler L. Hayes
REMIND Your Neural Network to Prevent Catastrophic Forgetting. ECCV (8) 2020: 466-483 - [i9]Zhongchao Qian, Tyler L. Hayes, Kushal Kafle, Christopher Kanan:
Do We Need Fully Connected Output Layers in Convolutional Networks? CoRR abs/2004.13587 (2020) - [i8]Manoj Acharya, Tyler L. Hayes, Christopher Kanan:
RODEO: Replay for Online Object Detection. CoRR abs/2008.06439 (2020) - [i7]Ryne Roady, Tyler L. Hayes, Christopher Kanan:
Improved Robustness to Open Set Inputs via Tempered Mixup. CoRR abs/2009.04659 (2020)
2010 – 2019
- 2019
- [c4]Tyler L. Hayes, Nathan D. Cahill
Memory Efficient Experience Replay for Streaming Learning. ICRA 2019: 9769-9776 - [i6]Tyler L. Hayes, Christopher Kanan:
Lifelong Machine Learning with Deep Streaming Linear Discriminant Analysis. CoRR abs/1909.01520 (2019) - [i5]Tyler L. Hayes, Kushal Kafle, Robik Shrestha, Manoj Acharya, Christopher Kanan:
REMIND Your Neural Network to Prevent Catastrophic Forgetting. CoRR abs/1910.02509 (2019) - [i4]Ryne Roady, Tyler L. Hayes, Ronald Kemker, Ayesha Gonzales, Christopher Kanan:
Are Out-of-Distribution Detection Methods Effective on Large-Scale Datasets? CoRR abs/1910.14034 (2019) - 2018
- [c3]Ronald Kemker, Marc McClure, Angelina Abitino, Tyler L. Hayes, Christopher Kanan:
Measuring Catastrophic Forgetting in Neural Networks. AAAI 2018: 3390-3398 - [c2]Nathan D. Cahill
Compassionately Conservative Balanced Cuts for Image Segmentation. CVPR 2018: 1683-1691 - [c1]Tyler L. Hayes, Ronald Kemker, Nathan D. Cahill
New Metrics and Experimental Paradigms for Continual Learning. CVPR Workshops 2018: 2031-2034 - [i3]Nathan D. Cahill, Tyler L. Hayes, Renee T. Meinhold, John F. Hamilton:
Compassionately Conservative Balanced Cuts for Image Segmentation. CoRR abs/1803.09903 (2018) - [i2]Tyler L. Hayes, Nathan D. Cahill, Christopher Kanan:
Memory Efficient Experience Replay for Streaming Learning. CoRR abs/1809.05922 (2018) - 2016
- [i1]Renee T. Meinhold, Tyler L. Hayes, Nathan D. Cahill:
Efficiently Computing Piecewise Flat Embeddings for Data Clustering and Image Segmentation. CoRR abs/1612.06496 (2016)
Coauthor Index
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last updated on 2024-10-07 01:20 CEST by the dblp team
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