Zobrazeno 1 - 10
of 110
pro vyhledávání: '"Soltoggio, Andrea"'
Despite basic differences between Spiking Neural Networks (SNN) and Artificial Neural Networks (ANN), most research on SNNs involve adapting ANN-based methods for SNNs. Pruning (dropping connections) and quantization (reducing precision) are often us
Externí odkaz:
http://arxiv.org/abs/2408.02961
Autor:
Dick, Jeffery, Nath, Saptarshi, Peridis, Christos, Benjamin, Eseoghene, Kolouri, Soheil, Soltoggio, Andrea
Context detection involves labeling segments of an online stream of data as belonging to different tasks. Task labels are used in lifelong learning algorithms to perform consolidation or other procedures that prevent catastrophic forgetting. Inferrin
Externí odkaz:
http://arxiv.org/abs/2405.19047
Autonomous robotic systems, like autonomous vehicles and robotic search and rescue, require efficient on-device training for continuous adaptation of Deep Reinforcement Learning (DRL) models in dynamic environments. This research is fundamentally mot
Externí odkaz:
http://arxiv.org/abs/2308.15039
Autor:
Nath, Saptarshi, Peridis, Christos, Ben-Iwhiwhu, Eseoghene, Liu, Xinran, Dora, Shirin, Liu, Cong, Kolouri, Soheil, Soltoggio, Andrea
Lifelong learning agents aim to learn multiple tasks sequentially over a lifetime. This involves the ability to exploit previous knowledge when learning new tasks and to avoid forgetting. Modulating masks, a specific type of parameter isolation appro
Externí odkaz:
http://arxiv.org/abs/2305.10997
Autor:
Soltoggio, Andrea, Ben-Iwhiwhu, Eseoghene, Peridis, Christos, Ladosz, Pawel, Dick, Jeffery, Pilly, Praveen K., Kolouri, Soheil
This paper introduces a set of formally defined and transparent problems for reinforcement learning algorithms with the following characteristics: (1) variable degrees of observability (non-Markov observations), (2) distal and sparse rewards, (3) var
Externí odkaz:
http://arxiv.org/abs/2302.10887
Autor:
Baker, Megan M., New, Alexander, Aguilar-Simon, Mario, Al-Halah, Ziad, Arnold, Sébastien M. R., Ben-Iwhiwhu, Ese, Brna, Andrew P., Brooks, Ethan, Brown, Ryan C., Daniels, Zachary, Daram, Anurag, Delattre, Fabien, Dellana, Ryan, Eaton, Eric, Fu, Haotian, Grauman, Kristen, Hostetler, Jesse, Iqbal, Shariq, Kent, Cassandra, Ketz, Nicholas, Kolouri, Soheil, Konidaris, George, Kudithipudi, Dhireesha, Learned-Miller, Erik, Lee, Seungwon, Littman, Michael L., Madireddy, Sandeep, Mendez, Jorge A., Nguyen, Eric Q., Piatko, Christine D., Pilly, Praveen K., Raghavan, Aswin, Rahman, Abrar, Ramakrishnan, Santhosh Kumar, Ratzlaff, Neale, Soltoggio, Andrea, Stone, Peter, Sur, Indranil, Tang, Zhipeng, Tiwari, Saket, Vedder, Kyle, Wang, Felix, Xu, Zifan, Yanguas-Gil, Angel, Yedidsion, Harel, Yu, Shangqun, Vallabha, Gautam K.
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 t
Externí odkaz:
http://arxiv.org/abs/2301.07799
Autor:
Ben-Iwhiwhu, Eseoghene, Nath, Saptarshi, Pilly, Praveen K., Kolouri, Soheil, Soltoggio, Andrea
Publikováno v:
Transactions on Machine Learning Research (2023)
Lifelong learning aims to create AI systems that continuously and incrementally learn during a lifetime, similar to biological learning. Attempts so far have met problems, including catastrophic forgetting, interference among tasks, and the inability
Externí odkaz:
http://arxiv.org/abs/2212.11110
Measuring similarities between different tasks is critical in a broad spectrum of machine learning problems, including transfer, multi-task, continual, and meta-learning. Most current approaches to measuring task similarities are architecture-depende
Externí odkaz:
http://arxiv.org/abs/2208.11726
Learning from set-structured data is an essential problem with many applications in machine learning and computer vision. This paper focuses on non-parametric and data-independent learning from set-structured data using approximate nearest neighbor (
Externí odkaz:
http://arxiv.org/abs/2112.05872
Autor:
Ben-Iwhiwhu, Eseoghene, Dick, Jeffery, Ketz, Nicholas A., Pilly, Praveen K., Soltoggio, Andrea
Publikováno v:
Neural Networks 2022
Meta-reinforcement learning (meta-RL) algorithms enable agents to adapt quickly to tasks from few samples in dynamic environments. Such a feat is achieved through dynamic representations in an agent's policy network (obtained via reasoning about task
Externí odkaz:
http://arxiv.org/abs/2111.00134