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Aakriti Agrawal
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2020 – today
- 2024
- [c4]Bang An, Mucong Ding, Tahseen Rabbani, Aakriti Agrawal, Yuancheng Xu, Chenghao Deng, Sicheng Zhu, Abdirisak Mohamed, Yuxin Wen, Tom Goldstein, Furong Huang:
WAVES: Benchmarking the Robustness of Image Watermarks. ICML 2024 - [i9]Bang An, Mucong Ding, Tahseen Rabbani, Aakriti Agrawal, Yuancheng Xu, Chenghao Deng, Sicheng Zhu, Abdirisak Mohamed, Yuxin Wen, Tom Goldstein, Furong Huang:
Benchmarking the Robustness of Image Watermarks. CoRR abs/2401.08573 (2024) - [i8]Mucong Ding, Chenghao Deng, Jocelyn Choo, Zichu Wu, Aakriti Agrawal, Avi Schwarzschild, Tianyi Zhou, Tom Goldstein, John Langford, Anima Anandkumar, Furong Huang:
Easy2Hard-Bench: Standardized Difficulty Labels for Profiling LLM Performance and Generalization. CoRR abs/2409.18433 (2024) - 2023
- [c3]Aakriti Agrawal, Amrit Singh Bedi, Dinesh Manocha:
RTAW: An Attention Inspired Reinforcement Learning Method for Multi-Robot Task Allocation in Warehouse Environments. ICRA 2023: 1393-1399 - [c2]Aakriti Agrawal, Milind Rao, Anit Kumar Sahu, Gopinath Chennupati, Andreas Stolcke:
Learning When to Trust Which Teacher for Weakly Supervised ASR. INTERSPEECH 2023: 381-385 - [p1]Aakriti Agrawal, Aashay Anil Bhise, Rohitkumar Arasanipalai, Lima Agnel Tony, Shuvrangshu Jana, Debasish Ghose:
Accurate Estimation of 3D-Repetitive-Trajectories using Kalman Filter, Machine Learning and Curve-Fitting Method for High-speed Target Interception. Artificial Intelligence for Robotics and Autonomous Systems Applications 2023: 93-122 - [i7]Aakriti Agrawal, Milind Rao, Anit Kumar Sahu, Gopinath Chennupati, Andreas Stolcke:
Learning When to Trust Which Teacher for Weakly Supervised ASR. CoRR abs/2306.12012 (2023) - [i6]Aakriti Agrawal, Rohith Aralikatti, Yanchao Sun, Furong Huang:
Robustness to Multi-Modal Environment Uncertainty in MARL using Curriculum Learning. CoRR abs/2310.08746 (2023) - 2022
- [c1]Aakriti Agrawal, Senthil Hariharan Arul, Amrit Singh Bedi, Dinesh Manocha:
DC-MRTA: Decentralized Multi-Robot Task Allocation and Navigation in Complex Environments. IROS 2022: 11711-11718 - [i5]Aakriti Agrawal, Senthil Hariharan Arul, Amrit Singh Bedi, Dinesh Manocha:
DC-MRTA: Decentralized Multi-Robot Task Allocation and Navigation in Complex Environments. CoRR abs/2209.02865 (2022) - [i4]Aakriti Agrawal, Amrit Singh Bedi, Dinesh Manocha:
RTAW: An Attention Inspired Reinforcement Learning Method for Multi-Robot Task Allocation in Warehouse Environments. CoRR abs/2209.05738 (2022) - 2020
- [i3]Rohitkumar Arasanipalai, Aakriti Agrawal, Debasish Ghose:
Mid-flight Propeller Failure Detection and Control of Propeller-deficient Quadcopter using Reinforcement Learning. CoRR abs/2002.11564 (2020) - [i2]Aakriti Agrawal, V. S. Rajashekhar, Rohitkumar Arasanipalai, Debasish Ghose:
Control of a Nature-inspired Scorpion using Reinforcement Learning. CoRR abs/2008.13712 (2020) - [i1]Aakriti Agrawal, Aashay Anil Bhise, Rohitkumar Arasanipalai, Lima Agnel Tony, Shuvrangshu Jana, Debasish Ghose:
Accurate Prediction and Estimation of 3D-Repetitive-Trajectories using Kalman Filter, Machine Learning and Curve-Fitting Method. CoRR abs/2009.00067 (2020)
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