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January 2024: I was invited to present a tutorial at the IEEE International Conference on Consumer Electronics (ICCE-2024), which is co-located with the Consumer Electronics Show (CES) 2024. I chose to speak on "Domain Adaptation at the Edge for Personalization of Deep Models", drawing on recent advancements in large pre-trained models and my team's recent works on adapting them to different applications and users. All the tutorial material is on HuggingFace Spaces. (notebook) (notebook) (app)
January 2024: I was invited to present a tutorial at the IEEE International Conference on Consumer Electronics (ICCE-2024), which is co-located with the Consumer Electronics Show (CES) 2024. I chose to speak on "Domain Adaptation at the Edge for Personalization of Deep Models", drawing on recent advancements in large pre-trained models and my team's recent works on adapting them to different applications and users. All the tutorial material is on HuggingFace Spaces. (notebook) (notebook) (app)
The first conference on Lifelong Learning Agents (COLLAs) 2022 recently took place. With keynotes by ML luminaries, the conference highlights the increasing importance of continual and lifelong learning machines, and it may even be a sign of increasing maturity of these topics that have so far only had dedicated workshops. Excited to present my team's research over the past two years: we are able to achieve model-free lifelong reinforcement learning reliably using hidden replay. (Paper)
The first conference on Lifelong Learning Agents (COLLAs) 2022 recently took place. With keynotes by ML luminaries, the conference highlights the increasing importance of continual and lifelong learning machines, and it may even be a sign of increasing maturity of these topics that have so far only had dedicated workshops. Excited to present my team's research over the past two years: we are able to achieve model-free lifelong reinforcement learning reliably using hidden replay. (Paper)
I presented an invited talk at American University (AU) on our recent Lifelong Machine Learning algorithms in April 2022.
I presented an invited talk at American University (AU) on our recent Lifelong Machine Learning algorithms in April 2022.
I was invited to speak about our work on Lifelong Learning at the DARPA Electronic Resurgence Initiative (ERI) Summit for three years 2019-2021. (2021 New Opportunities for Lifelong Learning Machines) (2020 Poster on Eigentasks)
I was invited to speak about our work on Lifelong Learning at the DARPA Electronic Resurgence Initiative (ERI) Summit for three years 2019-2021. (2021 New Opportunities for Lifelong Learning Machines) (2020 Poster on Eigentasks)
Our work introducing Eigentasks has been accepted at ICML 2020 Lifelong ML workshop. Good empirical results in Starcraft2 surpassing DeepMind's RL performance in one case. Same method also works for supervised continual learning! (Paper)
Our work introducing Eigentasks has been accepted at ICML 2020 Lifelong ML workshop. Good empirical results in Starcraft2 surpassing DeepMind's RL performance in one case. Same method also works for supervised continual learning! (Paper)
I gave an invited talk at the American Statistics Association QPRC 2019 conference (QPRC 2019) covering three of our papers on anomaly detection at the processor and controller level for cybersecurity applications (slides).
I gave an invited talk at the American Statistics Association QPRC 2019 conference (QPRC 2019) covering three of our papers on anomaly detection at the processor and controller level for cybersecurity applications (slides).
Our demo "Aesop: A Visual Storytelling Platform for Conversational AI" won the Best demo award at IJCAI 2018. This system was also demonstrated at the 50th anniversary homage to Doug Engelbart's The Mother of All Demos
Our demo "Aesop: A Visual Storytelling Platform for Conversational AI" won the Best demo award at IJCAI 2018. This system was also demonstrated at the 50th anniversary homage to Doug Engelbart's The Mother of All Demos
I won Honorable mention to the Best Dissertation award at International Conference on Automated Planning and Scheduling (ICAPS) 2018.
I won Honorable mention to the Best Dissertation award at International Conference on Automated Planning and Scheduling (ICAPS) 2018.
I graduated with a PhD in Computer Science in Jan 2017 advised by Prof. Prasad Tadepalli at Oregon State University, Corvallis, USA. I joined the Computer Vision Technologies group at SRI International in Princeton. Previously, I was a Student Associate (intern) at Computer Vision Technologies group, SRI International in Princeton (Winter and Spring of 2016) on new Deep Learning algorithms, and prior to that an intern at the Indian Institute of Technology (Madras) and Indian Institute of Management (Bangalore).
I graduated with a PhD in Computer Science in Jan 2017 advised by Prof. Prasad Tadepalli at Oregon State University, Corvallis, USA. I joined the Computer Vision Technologies group at SRI International in Princeton. Previously, I was a Student Associate (intern) at Computer Vision Technologies group, SRI International in Princeton (Winter and Spring of 2016) on new Deep Learning algorithms, and prior to that an intern at the Indian Institute of Technology (Madras) and Indian Institute of Management (Bangalore).
Jun Hu, Phil Miller, Michael Lomnitz, Saurabh Farkya, Emre Yilmaz, Aswin Raghavan, David Zhang, Michael Piacentino. "Tools Identification By On-Board Adaptation of Vision-and-Language Models". To appear in the AAAI-24 Demonstrations Program at the Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI-24).
Farkya, Saurabh, Aswin Raghavan, and Avi Ziskind. "Improving the Robustness of Quantized Deep Neural Networks to White-Box Attacks using Stochastic Quantization and Information-Theoretic Ensemble Training" arXiv preprint arXiv:2312.00105 (2023). (arxiv)
Peihong Yu, Bhoram Lee, Aswin Raghavan, Supun Samarasekera, Pratap Tokekar (U. of Maryland), James Zachary Hare (DEVCOM Army Research Lab), "Enhancing Multi-Agent Coordination through Common Operating Picture Integration", Out-of-Distribution Generalization in Robotics (OOD Workshop) at Conference on Robot Learning (CoRL) 2023. (Paper)
Indranil Sur, Zachary A Daniels, Aswin Raghavan, Jesse Hostetler, Abrar Rahman, Michael R Piacentino, Ajay Divakaran, Roberto Corizzo (American University), Kamil Faber (AGH University of Science and Technology), Nathalie Japkowicz (American University), Michael Baron (American University), James S Smith (Georgia Institute of Technology), Sahana Joshi (Georgia Institute of Technology), Zsolt Kira (Georgia Institute of Technology), Tyler L Hayes (RIT), Christopher Kanan (University of Rochester), Gianmarco J Gallardo (RIT). "System Design for an Integrated Lifelong Reinforcement Learning Agent For Real-Time Strategy Games". Accepted for oral presentation at the Second International Conference on AI-ML Systems (2022). (conf)
Daniels, Zachary, Aswin Raghavan, Jesse Hostetler, Abrar Rahman, Indranil Sur, Michael Piacentino, and Ajay Divakaran. "Model-Free Generative Replay for Lifelong Reinforcement Learning: Application to Starcraft-2." In Proceedings of The First Conference on Lifelong Learning Agents (CoLLAs) (2022) PMLR (Paper)
Farkya, Saurabh; Daniels, Zachary A; Raghavan, Aswin; Zhang, David Zhang; Piacentino, Michael. Saccade Mechanisms for Image Classification, Object Detection and Tracking. NeuroVision workshop at CVPR 2022. (Page) (PDF)
Indhumathi Kandaswamy, Saurabh Farkya, Zachary Daniels, Gooitzen van der Wal, Aswin Raghavan, Yuzheng Zhang, Jun Hu, Michael Lomnitz, Michael Isnardi, David Zhang, Michael Piacentino. Real-Time Hyper-Dimensional Reconfiguration at the Edge using Hardware Accelerators, Embedded Vision Workshop (EVW) at CVPR 2022. (link)
Raghavan, A., Hostetler, J., Sur, I., Rahman, A., & Divakaran, A. Lifelong Learning using Eigentasks: Task Separation, Skill Acquisition, and Selective Transfer. Lifelong Machine Learning Workshop, International Conference on Machine Learning (ICML 2020). (workshop) (paper) (video)
He, Zecheng, Aswin Raghavan, Sek Chai, and Ruby Lee Power-Grid Controller Anomaly Detection with Enhanced Temporal Deep Learning TrustCom 2019 (18th IEEE International Conference on Trust, Security and Privacy in Computing and Communications) (arxiv)
Meo, Timothy J., Chris Kim, Aswin Raghavan, Alex Tozzo, David A. Salter, Amir Tamrakar, and Mohamed R. Amer. "Aesop: A visual storytelling platform for conversational AI and common sense grounding." AI Communications Preprint (2019): 1-18. (PDF)
Samyak Parajuli and Aswin Raghavan and Sek Chai, Generalized Ternary Connect: End-to-End Learning and Compression of Multiplication-Free Deep Neural Networks, (arxiv)
Durga Harish Dayapule, Aswin Raghavan, Prasad Tadepalli, Alan Fern, Emergency Response Optimization using Online Hybrid Planning, 27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence (IJCAI-ECAI 2018) (PDF)
Mohamed Amer, Tim Meo, Aswin Raghavan, Alex Tozzo, David Salter, Amir Tamrakar, Aesop: A Visual Storytelling Platform for Conversational AI, Demos Track of IJCAI-ECAI 2018. (PDF)
Tharindu Mathew, Aswin Raghavan, Sek Chai, Event Prediction in Processors using Deep Temporal Models, 1st Workshop on Energy Efficient Machine Learning And Cognitive Computing for Embedded Applications (EMC2), 23rd ACM Intl. Conf. on Architectural Support for Programming Languages and Operating Systems (ASPLOS 2018) (IEEE)
Aswin Raghavan, Scott Sanner, Roni Khardon, Prasad Tadepalli, Alan Fern, Hindsight Optimization for Hybrid State and Action MDPs, Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI-2017). (PDF)
Aswin Raghavan, Mohamed R. Amer, Timothy Shields, David Zhang, Sek Chai (SRI International), GPU Activity Prediction using Representation Learning, ML Systems Workshop, International Conference on Machine Learning (ICML), 2016. (PDF)
Sek Chai, Aswin Raghavan, David Zhang, Mohamed Amer, Tim Shields, Low Precision Neural Networks using Subband Decomposition, Presented at CogArch Workshop, Atlanta, GA, April 2016. (PDF)
Aswin Raghavan, Prasad Tadepalli, Alan Fern, Roni Khardon, Memory-Efficient Symbolic Online Planning for Factored MDPs, 31st Conference on Uncertainty in Artificial Intelligence (UAI), 2015. (PDF) (Poster)
A. Raghavan, A. Fern, P. Tadepalli, and R. Khardon, Symbolic Opportunistic Policy Iteration for Factored-Action MDPs, Proceedings of the International Conference on Neural Information Processing Systems (NIPS), 2013. (PDF)(BibTex)(Poster)
S. Joshi, R. Khardon, P. Tadepalli, A. Fern, A. Raghavan, Relational Markov Decision Processes: Promise and Prospects, StarAI Workshop help at the Twenty-Seventh AAAI National Conference on Artificial Intelligence (StarAI), 2013 (PDF).
S. Joshi, R. Khardon, P. Tadepalli, A. Raghavan, A. Fern, Solving Relational MDPs with Exogenous Events and Additive Rewards, The European Conference on Machine Learning (ECML/PKDD) , 2013 (PDF) (Arxiv).
Aswin Raghavan, Saket Joshi, Alan Fern, Prasad Tadepalli, Roni Khardon, Planning in Factored action spaces using Symbolic Dynamic Programming, Proceedings of the 26th Conference on Artificial Intelligence (AAAI-12) Toronto, Canada. (source)(pdf)(poster).
Aswin N. R., Manimaran S. S., Harini A., and Ravindran, B. (2010), Accurate Mobile Robot Localization in Indoor environments using Bluetooth. In the Proceedings of the 2010 IEEE International Conference on Robotics and Automation (ICRA 2010), pp. 4391-4396. IEEE Press. (PDF).
R.Malmathanraj, Aswin N Raghavan, V.Srivas and R.Gowtham Rangarajan, Mammogram tumor classification using Q learning based thresholding, BEATS 2010 : International Conference on Biomedical Engineering and Assistive Technologies, NIT JALANDHAR.
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