3 citations · 5 across the 7 of their papers we have counts for
7 papers
Depth-Aware Sensitivity Analysis of Mixture-of-Experts Models via Magnitude-Based Expert Masking
Pradeep Kumar Sharma, Shantanu Godbole, Hritvik Shrivastava
Mixture-of-Experts (MoE) architectures scale large language models (LLMs) while preserving computational efficiency through sparse activation. Despite their widespread adoption, th…
Attention Mechanism and Heuristic Approach: Context-Aware File Ranking Using Multi-Head Self-Attention
Pradeep Kumar Sharma, Shantanu Godbole, Sarada Prasad Jena +1
The identification and ranking of impacted files within software reposi-tories is a key challenge in change impact analysis. Existing deterministic approaches that combine heuristi…
Production-Grade Local LLM Inference on Apple Silicon: A Comparative Study of MLX, MLC-LLM, Ollama, llama.cpp, and PyTorch MPS
Varun Rajesh, Om Jodhpurkar, Pooja Anbuselvan +5
We present a systematic, empirical evaluation of five local large language model (LLM) runtimes on Apple Silicon: MLX, MLC-LLM, llama.cpp, Ollama, and PyTorch MPS. Experiments were…
From Multiple-Choice to Extractive QA: A Case Study for English and Arabic
Teresa Lynn, Malik H. Altakrori, Samar Mohamed Magdy +11
The rapid evolution of Natural Language Processing (NLP) has favoured major languages such as English, leaving a significant gap for many others due to limited resources. This is e…
Supply chain emission estimation using large language models
Ayush Jain, Manikandan Padmanaban, Jagabondhu Hazra +2
Large enterprises face a crucial imperative to achieve the Sustainable Development Goals (SDGs), especially goal 13, which focuses on combating climate change and its impacts. To m…
A Framework for Crop Price Forecasting in Emerging Economies by Analyzing the Quality of Time-series Data
Ayush Jain, Smit Marvaniya, Shantanu Godbole +1
Accuracy of crop price forecasting techniques is important because it enables the supply chain planners and government bodies to take appropriate actions by estimating market facto…
Taxonomy grounded aggregation of classifiers with different label sets
Amrita Saha, Sathish Indurthi, Shantanu Godbole +2
We describe the problem of aggregating the label predictions of diverse classifiers using a class taxonomy. Such a taxonomy may not have been available or referenced when the indiv…