26 citations · 44 across the 12 of their papers we have counts for
12 papers
Confidence-Modulated Speculative Decoding for Large Language Models
Jaydip Sen, Subhasis Dasgupta, Hetvi Waghela
Speculative decoding has emerged as an effective approach for accelerating autoregressive inference by parallelizing token generation through a draft-then-verify paradigm. However,…
Multi-Amateur Contrastive Decoding for Text Generation
Jaydip Sen, Subhasis Dasgupta, Hetvi Waghela
Contrastive Decoding (CD) has emerged as an effective inference-time strategy for enhancing open-ended text generation by exploiting the divergence in output probabilities between…
Adversarial Text Generation with Dynamic Contextual Perturbation
Hetvi Waghela, Jaydip Sen, Sneha Rakshit +1
Adversarial attacks on Natural Language Processing (NLP) models expose vulnerabilities by introducing subtle perturbations to input text, often leading to misclassification while m…
Context-Enhanced Contrastive Search for Improved LLM Text Generation
Jaydip Sen, Rohit Pandey, Hetvi Waghela
Recently, Large Language Models (LLMs) have demonstrated remarkable advancements in Natural Language Processing (NLP). However, generating high-quality text that balances coherence…
Adversarial Robustness through Dynamic Ensemble Learning
Hetvi Waghela, Jaydip Sen, Sneha Rakshit
Adversarial attacks pose a significant threat to the reliability of pre-trained language models (PLMs) such as GPT, BERT, RoBERTa, and T5. This paper presents Adversarial Robustnes…
Robust Image Classification: Defensive Strategies against FGSM and PGD Adversarial Attacks
Hetvi Waghela, Jaydip Sen, Sneha Rakshit
Adversarial attacks, particularly the Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD) pose significant threats to the robustness of deep learning models in im…
Privacy in Federated Learning
Jaydip Sen, Hetvi Waghela, Sneha Rakshit
Federated Learning (FL) represents a significant advancement in distributed machine learning, enabling multiple participants to collaboratively train models without sharing raw dat…
Enhancing Adversarial Text Attacks on BERT Models with Projected Gradient Descent
Hetvi Waghela, Jaydip Sen, Sneha Rakshit
Adversarial attacks against deep learning models represent a major threat to the security and reliability of natural language processing (NLP) systems. In this paper, we propose a…
Exploring Sectoral Profitability in the Indian Stock Market Using Deep Learning
Jaydip Sen, Hetvi Waghela, Sneha Rakshit
This paper explores using a deep learning Long Short-Term Memory (LSTM) model for accurate stock price prediction and its implications for portfolio design. Despite the efficient m…
Saliency Attention and Semantic Similarity-Driven Adversarial Perturbation
Hetvi Waghela, Jaydip Sen, Sneha Rakshit
In this paper, we introduce an enhanced textual adversarial attack method, known as Saliency Attention and Semantic Similarity driven adversarial Perturbation (SASSP). The proposed…
Generative AI-Based Text Generation Methods Using Pre-Trained GPT-2 Model
Rohit Pandey, Hetvi Waghela, Sneha Rakshit +5
This work delved into the realm of automatic text generation, exploring a variety of techniques ranging from traditional deterministic approaches to more modern stochastic methods.…
A Modified Word Saliency-Based Adversarial Attack on Text Classification Models
Hetvi Waghela, Sneha Rakshit, Jaydip Sen
This paper introduces a novel adversarial attack method targeting text classification models, termed the Modified Word Saliency-based Adversarial At-tack (MWSAA). The technique bui…