40 citations · 77 across the 12 of their papers we have counts for
6 papers · 1 filter
Value Explicit Pretraining for Learning Transferable Representations
Kiran Lekkala, Henghui Bao, Sumedh A. Sontakke +2
Understanding visual inputs for a given task amidst varied changes is a key challenge posed by visual reinforcement learning agents. We propose \textit{Value Explicit Pretraining}…
Lightweight Learner for Shared Knowledge Lifelong Learning
Yunhao Ge, Yuecheng Li, Di Wu +12
In Lifelong Learning (LL), agents continually learn as they encounter new conditions and tasks. Most current LL is limited to a single agent that learns tasks sequentially. Dedicat…
Model2Detector: Widening the Information Bottleneck for Out-of-Distribution Detection using a Handful of Gradient Steps
Sumedh A Sontakke, Buvaneswari Ramanan, Laurent Itti +1
Out-of-distribution detection is an important capability that has long eluded vanilla neural networks. Deep Neural networks (DNNs) tend to generate over-confident predictions when…
GalilAI: Out-of-Task Distribution Detection using Causal Active Experimentation for Safe Transfer RL
Sumedh A Sontakke, Stephen Iota, Zizhao Hu +3
Out-of-distribution (OOD) detection is a well-studied topic in supervised learning. Extending the successes in supervised learning methods to the reinforcement learning (RL) settin…
Causal Curiosity: RL Agents Discovering Self-supervised Experiments for Causal Representation Learning
Sumedh A. Sontakke, Arash Mehrjou, Laurent Itti +1
Animals exhibit an innate ability to learn regularities of the world through interaction. By performing experiments in their environment, they are able to discern the causal factor…
SHERLock: Self-Supervised Hierarchical Event Representation Learning
Sumegh Roychowdhury, Sumedh A. Sontakke, Nikaash Puri +5
Temporal event representations are an essential aspect of learning among humans. They allow for succinct encoding of the experiences we have through a variety of sensory inputs. Al…