Aswin Raghavan

Publication Activity (10 Years)

Years Active: 2012-2024
Publications (10 Years): 26

Top Topics

Reinforcement Learning

Top Venues

Publications

Jun Hu, Phil Miller, Michael Lomnitz, Saurabh Farkya, Emre Yilmaz, Aswin Raghavan, David C. Zhang, Michael R. Piacentino
Tools Identification By On-Board Adaptation of Vision-and-Language Models. AAAI (2024)
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 Dimitri Konidaris, Dhireesha Kudithipudi, Erik G. Learned-Miller, Seungwon Lee, Michael L. 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
A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems. CoRR (2023)
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 L. 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
A domain-agnostic approach for characterization of lifelong learning systems. Neural Networks 160 (2023)
Saurabh Farkya, Aswin Raghavan, Avi Ziskind
Improving the Robustness of Quantized Deep Neural Networks to White-Box Attacks using Stochastic Quantization and Information-Theoretic Ensemble Training. CoRR (2023)
Peihong Yu, Bhoram Lee, Aswin Raghavan, Supun Samarasekera, Pratap Tokekar, James Zachary Hare
Enhancing Multi-Agent Coordination through Common Operating Picture Integration. CoRR (2023)
Zachary A. Daniels, Jun Hu, Michael Lomnitz, Phil Miller, Aswin Raghavan, Joe Zhang, Michael Piacentino, David C. Zhang
Efficient Model Adaptation for Continual Learning at the Edge. CoRR (2023)
Zachary Daniels, Aswin Raghavan, Jesse Hostetler, Abrar Rahman, Indranil Sur, Michael R. Piacentino, Ajay Divakaran
Model-Free Generative Replay for Lifelong Reinforcement Learning: Application to Starcraft-2. CoRR (2022)
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 Piacentino, Jesse Hostetler, Aswin Raghavan
System Design for an Integrated Lifelong Reinforcement Learning Agent for Real-Time Strategy Games. AIMLSystems (2022)
Zachary Alan Daniels, Aswin Raghavan, Jesse Hostetler, Abrar Rahman, Indranil Sur, Michael 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)
David C. Zhang, Gooitzen van der Wal, Saurabh Farkya, Thomas Senko, Aswin Raghavan, Michael A. Isnardi, Michael R. Piacentino
Low-power In-pixel Computing with Current-modulated Switched Capacitors. CoRR (2022)
Indranil Sur, Zachary Daniels, Abrar Rahman, Kamil Faber, Gianmarco J. Gallardo, Tyler L. Hayes, Cameron E. Taylor, Mustafa Burak Gurbuz, James Seale Smith, Sahana Joshi, Nathalie Japkowicz, Michael Baron, Zsolt Kira, Christopher Kanan, Roberto Corizzo, Ajay Divakaran, Michael R. Piacentino, Jesse Hostetler, Aswin Raghavan
System Design for an Integrated Lifelong Reinforcement Learning Agent for Real-Time Strategy Games. CoRR (2022)
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
Real-time Hyper-Dimensional Reconfiguration at the Edge using Hardware Accelerators. CoRR (2022)
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
Real-time Hyper-Dimensional Reconfiguration at the Edge using Hardware Accelerators. CVPR Workshops (2022)
Aswin Raghavan, Jesse Hostetler, Indranil Sur, Abrar Rahman, Ajay Divakaran
Lifelong Learning using Eigentasks: Task Separation, Skill Acquisition, and Selective Transfer. CoRR (2020)
Timothy J. Meo, Chris Kim, Aswin Raghavan, Alex Tozzo, David A. Salter, Amir Tamrakar, Mohamed R. Amer
Aesop: A visual storytelling platform for conversational AI and common sense grounding. AI Commun. 32 (1) (2019)
Zecheng He, Aswin Raghavan, Guangyuan Hu, Sek M. Chai, Ruby B. Lee
Power-Grid Controller Anomaly Detection with Enhanced Temporal Deep Learning. TrustCom/BigDataSE (2019)
Aswin Raghavan, Jesse Hostetler, Sek M. Chai
Generative Memory for Lifelong Reinforcement Learning. CoRR (2019)
Samyak Parajuli, Aswin Raghavan, Sek M. Chai
Generalized Ternary Connect: End-to-End Learning and Compression of Multiplication-Free Deep Neural Networks. CoRR (2018)
Zecheng He, Aswin Raghavan, Sek M. Chai, Ruby B. Lee
Detecting Zero-day Controller Hijacking Attacks on the Power-Grid with Enhanced Deep Learning. CoRR (2018)
Durga Harish Dayapule, Aswin Raghavan, Prasad Tadepalli, Alan Fern
Emergency Response Optimization using Online Hybrid Planning. IJCAI (2018)
Tim Meo, Aswin Raghavan, David A. Salter, Alex Tozzo, Amir Tamrakar, Mohamed R. Amer
Aesop: A Visual Storytelling Platform for Conversational AI. IJCAI (2018)
C. D. Tharindu Mathew, Aswin Raghavan, Sek Chai
Event Prediction in Processors Using Deep Temporal Models. EMC2@ASPLOS (2018)
Sek M. Chai, Aswin Raghavan, David C. Zhang, Mohamed R. Amer, Timothy J. Shields
Low Precision Neural Networks using Subband Decomposition. CoRR (2017)
Aswin Raghavan, Mohamed R. Amer, Timothy J. Shields, David C. Zhang, Sek M. Chai
GPU Activity Prediction using Representation Learning. CoRR (2017)
Aswin Raghavan, Mohamed R. Amer, Sek M. Chai
BitNet: Bit-Regularized Deep Neural Networks. CoRR (2017)
Aswin Raghavan, Scott Sanner, Roni Khardon, Prasad Tadepalli, Alan Fern
Hindsight Optimization for Hybrid State and Action MDPs. AAAI (2017)
Aswin Raghavan, Roni Khardon, Prasad Tadepalli, Alan Fern
Memory-Effcient Symbolic Online Planning for Factored MDPs. UAI (2015)
Saket Joshi, Roni Khardon, Prasad Tadepalli, Aswin Raghavan, Alan Fern
Solving Relational MDPs with Exogenous Events and Additive Rewards. CoRR (2013)
Saket Joshi, Roni Khardon, Prasad Tadepalli, Aswin Raghavan, Alan Fern
Solving Relational MDPs with Exogenous Events and Additive Rewards. ECML/PKDD (1) (2013)
Aswin Raghavan, Roni Khardon, Alan Fern, Prasad Tadepalli
Symbolic Opportunistic Policy Iteration for Factored-Action MDPs. NIPS (2013)
Saket Joshi, Roni Khardon, Prasad Tadepalli, Alan Fern, Aswin Raghavan
Relational Markov Decision Processes: Promise and Prospects. AAAI Workshop: Statistical Relational Artificial Intelligence (2013)
Aswin Raghavan, Saket Joshi, Alan Fern, Prasad Tadepalli, Roni Khardon
Planning in Factored Action Spaces with Symbolic Dynamic Programming. AAAI (2012)