Aswin Raghavan

According to our database, Aswin Raghavan authored at least 29 papers between 2012 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Tools Identification By On-Board Adaptation of Vision-and-Language Models.

Jun Hu, Phil Miller, Michael Lomnitz, Saurabh Farkya, Emre Yilmaz, Aswin Raghavan, David C. Zhang, Michael R. Piacentino

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

A domain-agnostic approach for characterization of lifelong learning systems.

Megan M. Baker, Alexander New, Mario Aguilar-Simon, Ziad Al-Halah, Sébastien M. R. Arnold, Eseoghene Ben-Iwhiwhu, Andrew P. Brna, Ethan Brooks, Ryan C. Brown, Zachary Daniels, Anurag Reddy Daram, Fabien Delattre, Ryan Dellana, Eric Eaton, Haotian Fu, Kristen Grauman, Jesse Hostetler, Shariq Iqbal, Cassandra Kent, Nicholas Ketz, Soheil Kolouri, George Konidaris, Dhireesha Kudithipudi, Erik G. Learned-Miller, Seungwon Lee, Michael Littman, Sandeep Madireddy, Jorge A. Mendez, Eric Q. Nguyen, Christine D. Piatko, Praveen K. Pilly, Aswin Raghavan, Abrar Rahman, Santhosh Kumar Ramakrishnan, Neale Ratzlaff, Andrea Soltoggio, Peter Stone, Indranil Sur, Zhipeng Tang, Saket Tiwari, Kyle Vedder, Felix Wang, Zifan Xu, Angel Yanguas-Gil, Harel Yedidsion, Shangqun Yu, Gautam K. Vallabha

Neural Networks, March, 2023

Improving the Robustness of Quantized Deep Neural Networks to White-Box Attacks using Stochastic Quantization and Information-Theoretic Ensemble Training.

Saurabh Farkya, Aswin Raghavan, Avi Ziskind

CoRR, 2023

Enhancing Multi-Agent Coordination through Common Operating Picture Integration.

Peihong Yu, Bhoram Lee, Aswin Raghavan, Supun Samarasekera, Pratap Tokekar, James Zachary Hare

CoRR, 2023

Efficient Model Adaptation for Continual Learning at the Edge.

Zachary A. Daniels, Jun Hu, Michael Lomnitz, Phil Miller, Aswin Raghavan, Joe Zhang, Michael R. Piacentino, David C. Zhang

CoRR, 2023

2022

Low-power In-pixel Computing with Current-modulated Switched Capacitors.

David C. Zhang, Gooitzen van der Wal, Saurabh Farkya, Thomas Senko, Aswin Raghavan, Michael A. Isnardi, Michael R. Piacentino

CoRR, 2022

Model-Free Generative Replay for Lifelong Reinforcement Learning: Application to Starcraft-2.

Zachary Daniels, Aswin Raghavan, Jesse Hostetler, Abrar Rahman, Indranil Sur, Michael R. Piacentino, Ajay Divakaran

CoRR, 2022

Saccade Mechanisms for Image Classification, Object Detection and Tracking.

Saurabh Farkya, Zachary Daniels, Aswin Nadamuni Raghavan, David C. Zhang, Michael R. Piacentino

CoRR, 2022

Real-time Hyper-Dimensional Reconfiguration at the Edge using Hardware Accelerators.

Indhumathi Kandaswamy, Saurabh Farkya, Zachary Daniels, Gooitzen van der Wal, Aswin Raghavan, Yuzheng Zhang, Jun Hu, Michael Lomnitz, Michael A. Isnardi, David C. Zhang, Michael R. Piacentino

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2022

Model-Free Generative Replay for Lifelong Reinforcement Learning: Application to Starcraft-2.

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

Proceedings of the Conference on Lifelong Learning Agents, 2022

System Design for an Integrated Lifelong Reinforcement Learning Agent for Real-Time Strategy Games.

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, Michael Baron, Zsolt Kira, Christopher Kanan, Roberto Corizzo, Ajay Divakaran, Michael R. Piacentino, Jesse Hostetler, Aswin Raghavan

Proceedings of the Second International Conference on AI-ML Systems, 2022

2020

Lifelong Learning using Eigentasks: Task Separation, Skill Acquisition, and Selective Transfer.

Aswin Raghavan, Jesse Hostetler, Indranil Sur, Abrar Rahman, Ajay Divakaran

CoRR, 2020

2019

Generative Memory for Lifelong Reinforcement Learning.

Aswin Raghavan, Jesse Hostetler, Sek M. Chai

CoRR, 2019

Aesop: A visual storytelling platform for conversational AI and common sense grounding.

Timothy J. Meo, Chris Kim, Aswin Raghavan, Alex Tozzo, David A. Salter, Amir Tamrakar, Mohamed R. Amer

AI Commun., 2019

Power-Grid Controller Anomaly Detection with Enhanced Temporal Deep Learning.

Zecheng He, Aswin Raghavan, Guangyuan Hu, Sek M. Chai, Ruby B. Lee

Proceedings of the 18th IEEE International Conference On Trust, 2019

2018

Generalized Ternary Connect: End-to-End Learning and Compression of Multiplication-Free Deep Neural Networks.

Samyak Parajuli, Aswin Raghavan, Sek M. Chai

CoRR, 2018

Detecting Zero-day Controller Hijacking Attacks on the Power-Grid with Enhanced Deep Learning.

Zecheng He, Aswin Raghavan, Sek M. Chai, Ruby B. Lee

CoRR, 2018

Aesop: A Visual Storytelling Platform for Conversational AI.

Tim Meo, Aswin Raghavan, David A. Salter, Alex Tozzo, Amir Tamrakar, Mohamed R. Amer

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

Emergency Response Optimization using Online Hybrid Planning.

Durga Harish Dayapule, Aswin Raghavan, Prasad Tadepalli, Alan Fern

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

Event Prediction in Processors Using Deep Temporal Models.

C. D. Tharindu Mathew, Aswin Raghavan, Sek Chai

Proceedings of the 1st Workshop on Energy Efficient Machine Learning and Cognitive Computing for Embedded Applications, 2018

2017

BitNet: Bit-Regularized Deep Neural Networks.

Aswin Raghavan, Mohamed R. Amer, Sek M. Chai

CoRR, 2017

GPU Activity Prediction using Representation Learning.

Aswin Raghavan, Mohamed R. Amer, Timothy J. Shields, David C. Zhang, Sek M. Chai

CoRR, 2017

Low Precision Neural Networks using Subband Decomposition.

Sek M. Chai, Aswin Raghavan, David C. Zhang, Mohamed R. Amer, Timothy J. Shields

CoRR, 2017

Hindsight Optimization for Hybrid State and Action MDPs.

Aswin Raghavan, Scott Sanner, Roni Khardon, Prasad Tadepalli, Alan Fern

Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2015

Memory-Effcient Symbolic Online Planning for Factored MDPs.

Aswin Raghavan, Roni Khardon, Prasad Tadepalli, Alan Fern

Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, 2015

2013

Solving Relational MDPs with Exogenous Events and Additive Rewards.

Saket Joshi, Roni Khardon, Prasad Tadepalli, Aswin Raghavan, Alan Fern

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2013

Symbolic Opportunistic Policy Iteration for Factored-Action MDPs.

Aswin Raghavan, Roni Khardon, Alan Fern, Prasad Tadepalli

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Relational Markov Decision Processes: Promise and Prospects.

Saket Joshi, Roni Khardon, Prasad Tadepalli, Alan Fern, Aswin Raghavan

Proceedings of the Statistical Relational Artificial Intelligence, 2013

2012

Planning in Factored Action Spaces with Symbolic Dynamic Programming.

Aswin Raghavan, Saket Joshi, Alan Fern, Prasad Tadepalli, Roni Khardon

Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, 2012