1 citations · 1 across the 6 of their papers we have counts for
6 papers
Tail-Likelihood Reinforcement Learning
Shrinivas Ramasubramanian, Daman Arora, Fahim Tajwar +11
Reinforcement learning typically optimizes average reward. For generative policies, the average can hide an important distinction: two policies can achieve the same mean reward whi…
Cost-Sensitive Learning for Long-Tailed Temporal Action Segmentation
Zhanzhong Pang, Fadime Sener, Shrinivas Ramasubramanian +1
Temporal action segmentation in untrimmed procedural videos aims to densely label frames into action classes. These videos inherently exhibit long-tailed distributions, where actio…
Long-Tail Temporal Action Segmentation with Group-wise Temporal Logit Adjustment
Zhanzhong Pang, Fadime Sener, Shrinivas Ramasubramanian +1
Procedural activity videos often exhibit a long-tailed action distribution due to varying action frequencies and durations. However, state-of-the-art temporal action segmentation m…
Semantic Graph Consistency: Going Beyond Patches for Regularizing Self-Supervised Vision Transformers
Chaitanya Devaguptapu, Sumukh Aithal, Shrinivas Ramasubramanian +2
Self-supervised learning (SSL) with vision transformers (ViTs) has proven effective for representation learning as demonstrated by the impressive performance on various downstream…
Selective Mixup Fine-Tuning for Optimizing Non-Decomposable Objectives
Shrinivas Ramasubramanian, Harsh Rangwani, Sho Takemori +3
The rise in internet usage has led to the generation of massive amounts of data, resulting in the adoption of various supervised and semi-supervised machine learning algorithms, wh…
Cost-Sensitive Self-Training for Optimizing Non-Decomposable Metrics
Harsh Rangwani, Shrinivas Ramasubramanian, Sho Takemori +3
Self-training based semi-supervised learning algorithms have enabled the learning of highly accurate deep neural networks, using only a fraction of labeled data. However, the major…