15 citations · 51 across the 8 of their papers we have counts for
9 papers
Distilling Knowledge from Text-to-Image Generative Models Improves Visio-Linguistic Reasoning in CLIP
Samyadeep Basu, Shell Xu Hu, Maziar Sanjabi +2
Image-text contrastive models like CLIP have wide applications in zero-shot classification, image-text retrieval, and transfer learning. However, they often struggle on composition…
Strong Baselines for Parameter Efficient Few-Shot Fine-tuning
Samyadeep Basu, Daniela Massiceti, Shell Xu Hu +1
Few-shot classification (FSC) entails learning novel classes given only a few examples per class after a pre-training (or meta-training) phase on a set of base classes. Recent work…
Topic Segmentation in the Wild: Towards Segmentation of Semi-structured & Unstructured Chats
Reshmi Ghosh, Harjeet Singh Kajal, Sharanya Kamath +3
Breaking down a document or a conversation into multiple contiguous segments based on its semantic structure is an important and challenging problem in NLP, which can assist many d…
On Hard Episodes in Meta-Learning
Samyadeep Basu, Amr Sharaf, Nicolo Fusi +1
Existing meta-learners primarily focus on improving the average task accuracy across multiple episodes. Different episodes, however, may vary in hardness and quality leading to a w…
Semi-Supervised Few-Shot Intent Classification and Slot Filling
Samyadeep Basu, Karine lp Kiun Chong, Amr Sharaf +7
Intent classification (IC) and slot filling (SF) are two fundamental tasks in modern Natural Language Understanding (NLU) systems. Collecting and annotating large amounts of data t…
Influence Functions in Deep Learning Are Fragile
Samyadeep Basu, Philip Pope, Soheil Feizi
Influence functions approximate the effect of training samples in test-time predictions and have a wide variety of applications in machine learning interpretability and uncertainty…