About
Hi, I am Samyadeep Basu, a 3rd year CS PhD student at UMD, College Park (2022 - Present). I work with Soheil Feizi in the Center for Machine Learning. My research focus is reliable deep learning encompassing methods to learn from data with limited supervision (few-shot learning), model interpretability (how to understand decision making process of deep networks), robustness and more recently vision+language. Previously I received my MS from UMD in 2020 and then spent close to two years at Microsoft AI in the ML rotation program. During my stint at Microsoft AI, I worked with the Language Science Team at Azure AI and MSAI where I researched, developed and deployed large-scale language models for various scenarios. My latest CV can be accessed at CV-Link.
News
(April 2024): Excited to be rejoining Adobe Research for a summer internship to work on small language models!
(February, March 2024): (i) Started an internship at Microsoft Research working on interpretability for multimodal language models! (ii) Submitted 1 paper to ICML and 2 papers to ECCV 2024!
(January 2024) : Paper on diffusion model interpretability accepted to ICLR 2024!
(December 2023) : Paper on few-shot finetuning for vision models accepted to AAAI 2024!
(August-October 2023): Paper on surgical fine-tuning for “small” language models accepted to EMNLP 2023!
(July 2023): Excited to announce two new projects : (i) Improving CLIP using knowledge distillation from diffusion models; (ii) Benchmark for text-guided image editing methods!
(June 2023): Started internship at Adobe Research! Working on interpretability + model editing for text-to-image generative models!
(April 2023): Our new pre-print on PEFT modules for few-shot fine-tuning is on arXiv!
(Jan 2023): Paper on algorithm to design difficult few-shot tasks accepted at ICLR 2023!
(September 2022): Finished an amazing research internship at Microsoft Research working with Daniela Massiceti on few-shot learning!
(Feb 2022): Started my PhD to work on few-shot learning and model interpretability!
(October 2020 - Jan 2022) : Break from Grad School to work at Microsoft AI as an Applied Scientist!
(Feb 2021): Papers on influence functions at ICLR 2021 and ICML 2020!
Publications
On Mechanistic Knowledge Localization in Text-to-Image Models
ICML 2024
We investigate cross-attention layers in text-to-image models on knowledge storage!
Localizing and Editing Knowledge in Text-to-Image Models
ICLR 2024
We propose an interpretability framework + model editing method to ablate concepts from text-to-image models, fast!
Augmenting CLIP with Improved Visio-Linguistic Reasoning
Under Review
We propose a knowledge-distillation technique to improve reasoning abilities in CLIP!
EditVal: Benchmarking Diffusion Based Text-Guided Image Editing Methods
Under Review Code
We propose a new comprehensive benchmark for evaluating diffusion based editing methods!
On Surgical Finetuning for Language Encoders
EMNLP 2023
Method to surgically finetune language encoders with a subset of layers to perform close to full-finetuning!
Strong Baselines for Parameter-Efficient Few-Shot Fine-Tuning
AAAI 2024 Code
We propose two easy-to-implement strong baselines for PEFT which leads to SoTA on MD!
Hard Meta-Dataset++: Towards Understanding Few-shot Performance on Difficult Tasks
ICLR 2023 Code / Media Coverage
We propose a fast algorithm - FastDiffSel which can extract difficult few-shot tasks in a computational efficient way from large vision datasets!
Strategies to Improve Few-Shot Learning for Intent Classification and Slot Filling
NAACL 2022 (SUKI)
Propose empirical strategies to improve few-shot performance for joint intent classification and slot-filling.
Influence Functions in Deep Learning are Fragile
ICLR 2021
End to end analysis of influence functions in deep learning!
On Second-Order Group Influence Functions for Black-Box Predictions
ICML 2020
We propose second-order group influence functions, which are better suited to handle group effects!
**NeurIPS 2020(w)** > We propose an early inversion technique using GANs to do membership inference!