5 citations · 9 across the 9 of their papers we have counts for
7 papers · 1 filter
Theory, Analysis, and Best Practices for Sigmoid Self-Attention
Jason Ramapuram, Federico Danieli, Eeshan Dhekane +8
Attention is a key part of the transformer architecture. It is a sequence-to-sequence mapping that transforms each sequence element into a weighted sum of values. The weights are t…
Poly-View Contrastive Learning
Amitis Shidani, Devon Hjelm, Jason Ramapuram +3
Contrastive learning typically matches pairs of related views among a number of unrelated negative views. Views can be generated (e.g. by augmentations) or be observed. We investig…
Bootstrap Your Own Variance
Polina Turishcheva, Jason Ramapuram, Sinead Williamson +3
Understanding model uncertainty is important for many applications. We propose Bootstrap Your Own Variance (BYOV), combining Bootstrap Your Own Latent (BYOL), a negative-free Self-…
Elastic Weight Consolidation Improves the Robustness of Self-Supervised Learning Methods under Transfer
Andrius Ovsianas, Jason Ramapuram, Dan Busbridge +2
Self-supervised representation learning (SSL) methods provide an effective label-free initial condition for fine-tuning downstream tasks. However, in numerous realistic scenarios,…
Iterated learning for emergent systematicity in VQA
Ankit Vani, Max Schwarzer, Yuchen Lu +2
Although neural module networks have an architectural bias towards compositionality, they require gold standard layouts to generalize systematically in practice. When instead learn…
Transfer Learning by Modeling a Distribution over Policies
Disha Shrivastava, Eeshan Gunesh Dhekane, Riashat Islam
Exploration and adaptation to new tasks in a transfer learning setup is a central challenge in reinforcement learning. In this work, we build on the idea of modeling a distribution…