10 citations · 15 across the 18 of their papers we have counts for
18 papers
Coverage-Driven KV Cache Eviction for Efficient and Improved Inference of LLM
Shuvendu Roy, Mengyao Zhai, Hossein Hajimirsadeghi +1
Large language models (LLMs) excel at complex tasks like question answering and summarization, thanks to their ability to handle long-context inputs. However, deploying LLMs is cos…
You Need Reasoning to Learn Reasoning: The Limitations of Label-Free RL in Weak Base Models
Shuvendu Roy, Hossein Hajimirsadeghi, Mengyao Zhai +1
Recent advances in large language models have demonstrated the promise of unsupervised reinforcement learning (RL) methods for enhancing reasoning capabilities without external sup…
Advancing Medical Representation Learning Through High-Quality Data
Negin Baghbanzadeh, Adibvafa Fallahpour, Yasaman Parhizkar +8
Despite the growing scale of medical Vision-Language datasets, the impact of dataset quality on model performance remains under-explored. We introduce Open-PMC, a high-quality medi…
A Shared Encoder Approach to Multimodal Representation Learning
Shuvendu Roy, Franklin Ogidi, Ali Etemad +2
Multimodal representation learning has demonstrated remarkable potential in enabling models to process and integrate diverse data modalities, such as text and images, for improved…
Task-agnostic Prompt Compression with Context-aware Sentence Embedding and Reward-guided Task Descriptor
Barys Liskavets, Shuvendu Roy, Maxim Ushakov +3
The rise of Large Language Models (LLMs) has led to significant interest in prompt compression, a technique aimed at reducing the length of input prompts while preserving critical…
SelfPrompt: Confidence-Aware Semi-Supervised Tuning for Robust Vision-Language Model Adaptation
Shuvendu Roy, Ali Etemad
We present SelfPrompt, a novel prompt-tuning approach for vision-language models (VLMs) in a semi-supervised learning setup. Existing methods for tuning VLMs in semi-supervised set…
Prompt Compression with Context-Aware Sentence Encoding for Fast and Improved LLM Inference
Barys Liskavets, Maxim Ushakov, Shuvendu Roy +3
Large language models (LLMs) have triggered a new stream of research focusing on compressing the context length to reduce the computational cost while ensuring the retention of hel…
Benchmarking Vision-Language Contrastive Methods for Medical Representation Learning
Shuvendu Roy, Yasaman Parhizkar, Franklin Ogidi +5
We perform a comprehensive benchmarking of contrastive frameworks for learning multimodal representations in the medical domain. Through this study, we aim to answer the following…
Consistency-Guided Asynchronous Contrastive Tuning for Few-Shot Class-Incremental Tuning of Foundation Models
Shuvendu Roy, Elham Dolatabadi, Arash Afkanpour +1
We propose Consistency-guided Asynchronous Contrastive Tuning (CoACT), a novel method for continuously tuning foundation models to learn new classes in few-shot settings. CoACT con…
A Bag of Tricks for Few-Shot Class-Incremental Learning
Shuvendu Roy, Chunjong Park, Aldi Fahrezi +1
We present a bag of tricks framework for few-shot class-incremental learning (FSCIL), which is a challenging form of continual learning that involves continuous adaptation to new t…
Contrastive Learning of View-Invariant Representations for Facial Expressions Recognition
Shuvendu Roy, Ali Etemad
Although there has been much progress in the area of facial expression recognition (FER), most existing methods suffer when presented with images that have been captured from viewi…
Active Learning with Contrastive Pre-training for Facial Expression Recognition
Shuvendu Roy, Ali Etemad
Deep learning has played a significant role in the success of facial expression recognition (FER), thanks to large models and vast amounts of labelled data. However, obtaining labe…
Exploring the Boundaries of Semi-Supervised Facial Expression Recognition using In-Distribution, Out-of-Distribution, and Unconstrained Data
Shuvendu Roy, Ali Etemad
Deep learning-based methods have been the key driving force behind much of the recent success of facial expression recognition (FER) systems. However, the need for large amounts of…
Scaling Up Semi-supervised Learning with Unconstrained Unlabelled Data
Shuvendu Roy, Ali Etemad
We propose UnMixMatch, a semi-supervised learning framework which can learn effective representations from unconstrained unlabelled data in order to scale up performance. Most exis…
Consistency-guided Prompt Learning for Vision-Language Models
Shuvendu Roy, Ali Etemad
We propose Consistency-guided Prompt learning (CoPrompt), a new fine-tuning method for vision-language models. Our approach improves the generalization of large foundation models w…
Impact of Strategic Sampling and Supervision Policies on Semi-supervised Learning
Shuvendu Roy, Ali Etemad
In semi-supervised representation learning frameworks, when the number of labelled data is very scarce, the quality and representativeness of these samples become increasingly impo…
Temporal Contrastive Learning with Curriculum
Shuvendu Roy, Ali Etemad
We present ConCur, a contrastive video representation learning method that uses curriculum learning to impose a dynamic sampling strategy in contrastive training. More specifically…
Analysis of Semi-Supervised Methods for Facial Expression Recognition
Shuvendu Roy, Ali Etemad
Training deep neural networks for image recognition often requires large-scale human annotated data. To reduce the reliance of deep neural solutions on labeled data, state-of-the-a…