no code implementations • 8 Nov 2024 • Indranil Sur, Aswin Raghavan, Abrar Rahman, James Z Hare, Daniel Cassenti, Carl Busart
The integration of unmanned platforms equipped with advanced sensors promises to enhance situational awareness and mitigate the "fog of war" in military operations.
Deep Reinforcement Learning
Multi-agent Reinforcement Learning
+1
no code implementations • 8 Aug 2024 • Saurabh Farkya, Zachary Alan Daniels, Aswin Raghavan, Gooitzen van der Wal, Michael Isnardi, Michael Piacentino, David Zhang
We study a data-driven system that combines dynamic sensing at the pixel level with computer vision analytics at the video level and propose a feedback control loop to minimize data movement between the sensor front-end and computational back-end without compromising detection and tracking precision.
no code implementations • 30 Nov 2023 • Saurabh Farkya, Aswin Raghavan, Avi Ziskind
We present a method to improve the robustness of quantized DNNs to white-box adversarial attacks.
no code implementations • 8 Nov 2023 • Peihong Yu, Bhoram Lee, Aswin Raghavan, Supun Samarasekara, Pratap Tokekar, James Zachary Hare
Our results demonstrate the efficacy of COP integration, and show that COP-based training leads to robust policies compared to state-of-the-art Multi-Agent Reinforcement Learning (MARL) methods when faced with out-of-distribution initial states.
no code implementations • 3 Aug 2023 • Zachary A. Daniels, Jun Hu, Michael Lomnitz, Phil Miller, Aswin Raghavan, Joe Zhang, Michael Piacentino, David Zhang
This paper presents the Encoder-Adaptor-Reconfigurator (EAR) framework for efficient continual learning under domain shifts.
no code implementations • 18 Jan 2023 • Megan M. Baker, Alexander New, Mario Aguilar-Simon, Ziad Al-Halah, Sébastien M. R. Arnold, Ese Ben-Iwhiwhu, Andrew P. Brna, Ethan Brooks, Ryan C. Brown, Zachary Daniels, Anurag 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 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
Despite the advancement of machine learning techniques in recent years, state-of-the-art systems lack robustness to "real world" events, where the input distributions and tasks encountered by the deployed systems will not be limited to the original training context, and systems will instead need to adapt to novel distributions and tasks while deployed.
no code implementations • 8 Dec 2022 • Indranil Sur, Zachary Daniels, Abrar Rahman, Kamil Faber, Gianmarco J. Gallardo, Tyler L. Hayes, Cameron E. Taylor, Mustafa Burak Gurbuz, James Smith, Sahana Joshi, Nathalie Japkowicz, Michael Baron, Zsolt Kira, Christopher Kanan, Roberto Corizzo, Ajay Divakaran, Michael Piacentino, Jesse Hostetler, Aswin Raghavan
In this paper, we introduce the Lifelong Reinforcement Learning Components Framework (L2RLCF), which standardizes L2RL systems and assimilates different continual learning components (each addressing different aspects of the lifelong learning problem) into a unified system.
no code implementations • 9 Aug 2022 • Zachary Daniels, Aswin Raghavan, Jesse Hostetler, Abrar Rahman, Indranil Sur, Michael Piacentino, Ajay Divakaran
We present a version of GR for LRL that satisfies two desiderata: (a) Introspective density modelling of the latent representations of policies learned using deep RL, and (b) Model-free end-to-end learning.
no code implementations • 10 Jun 2022 • Indhumathi Kandaswamy, Saurabh Farkya, Zachary Daniels, Gooitzen van der Wal, Aswin Raghavan, Yuzheng Zhang, Jun Hu, Michael Lomnitz, Michael Isnardi, David Zhang, Michael Piacentino
In this paper we present Hyper-Dimensional Reconfigurable Analytics at the Tactical Edge (HyDRATE) using low-SWaP embedded hardware that can perform real-time reconfiguration at the edge leveraging non-MAC (free of floating-point MultiplyACcumulate operations) deep neural nets (DNN) combined with hyperdimensional (HD) computing accelerators.
no code implementations • 14 Jul 2020 • Aswin Raghavan, Jesse Hostetler, Indranil Sur, Abrar Rahman, Ajay Divakaran
We propose a wake-sleep cycle of alternating task learning and knowledge consolidation for learning in our framework, and instantiate it for lifelong supervised learning and lifelong RL.
no code implementations • ICML Workshop LifelongML 2020 • Aswin Raghavan, Jesse Hostetler, Indranil Sur, Abrar Rahman, Ajay Divakaran
We propose a wake-sleep cycle of alternating task learning and knowledge consolidation for learning in our framework, and instantiate it for lifelong supervised learning and lifelong RL.
no code implementations • 22 Feb 2019 • Aswin Raghavan, Jesse Hostetler, Sek Chai
Our research is focused on understanding and applying biological memory transfers to new AI systems that can fundamentally improve their performance, throughout their fielded lifetime experience.
no code implementations • 12 Nov 2018 • Samyak Parajuli, Aswin Raghavan, Sek Chai
The use of deep neural networks in edge computing devices hinges on the balance between accuracy and complexity of computations.
no code implementations • 18 Jun 2018 • Zecheng He, Aswin Raghavan, Guangyuan Hu, Sek Chai, Ruby Lee
Specifically, we first train a temporal deep learning model, using only normal HPC readings from legitimate processes that run daily in these power-grid systems, to model the normal behavior of the power-grid controller.
no code implementations • ICLR 2018 • Mohamed Amer, Aswin Raghavan, Graham W. Taylor, Sek Chai
Our key idea is to control the expressive power of the network by dynamically quantizing the range and set of values that the parameters can take.
no code implementations • 16 Aug 2017 • Aswin Raghavan, Mohamed Amer, Sek Chai, Graham Taylor
The parameters of neural networks are usually unconstrained and have a dynamic range dispersed over all real values.
no code implementations • 27 Mar 2017 • Aswin Raghavan, Mohamed Amer, Timothy Shields, David Zhang, Sek Chai
GPU activity prediction is an important and complex problem.
no code implementations • 24 Mar 2017 • Sek Chai, Aswin Raghavan, David Zhang, Mohamed Amer, Tim Shields
In this paper, we present a unique approach using lower precision weights for more efficient and faster training phase.
no code implementations • NeurIPS 2013 • Aswin Raghavan, Roni Khardon, Alan Fern, Prasad Tadepalli
We address the scalability of symbolic planning under uncertainty with factored states and actions.