1 citations · 2 across the 7 of their papers we have counts for
7 papers
Cache What Lasts: Token Retention for Memory-Bounded KV Cache in LLMs
Ngoc Bui, Shubham Sharma, Simran Lamba +2
Memory and computation remain core bottlenecks in long-horizon LLM inference due to the quadratic cost of self-attention and the ever-growing key-value (KV) cache. Existing strateg…
EchoLSTM: A Self-Reflective Recurrent Network for Stabilizing Long-Range Memory
Prasanth K K, Shubham Sharma
Standard Recurrent Neural Networks, including LSTMs, struggle to model long-range dependencies, particularly in sequences containing noisy or misleading information. We propose a n…
EduVidQA: Generating and Evaluating Long-form Answers to Student Questions based on Lecture Videos
Sourjyadip Ray, Shubham Sharma, Somak Aditya +1
As digital platforms redefine educational paradigms, ensuring interactivity remains vital for effective learning. This paper explores using Multimodal Large Language Models (MLLMs)…
Geometric Mixture Classifier (GMC): A Discriminative Per-Class Mixture of Hyperplanes
Prasanth K K, Shubham Sharma
Many real world categories are multimodal, with single classes occupying disjoint regions in feature space. Classical linear models (logistic regression, linear SVM) use a single g…
Challenges and Applications of Large Language Models: A Comparison of GPT and DeepSeek family of models
Shubham Sharma, Sneha Tuli, Narendra Badam
Large Language Models (LLMs) are transforming AI across industries, but their development and deployment remain complex. This survey reviews 16 key challenges in building and using…
The infrastructure powering IBM's Gen AI model development
Talia Gershon, Seetharami Seelam, Brian Belgodere +143
AI Infrastructure plays a key role in the speed and cost-competitiveness of developing and deploying advanced AI models. The current demand for powerful AI infrastructure for model…
The Effect of Data Poisoning on Counterfactual Explanations
André Artelt, Shubham Sharma, Freddy Lecué +1
Counterfactual explanations are a widely used approach for examining the predictions of black-box systems. They can offer the opportunity for computational recourse by suggesting a…