20 citations · 26 across the 20 of their papers we have counts for
5 papers · 1 filter
Coarse composition suffices: tabular in-context learning for multi-activity antimicrobial peptide profiling
Anuj Pal, Raunak Kumar, Dhruvi Solanki +3
Antimicrobial peptides (AMPs) often act against multiple pathogen classes, making multi-label activity prediction a more realistic screening target than binary antimicrobial classi…
Rethinking Neural Width for Alternating Current Optimal Power Flow Proxies
Dhruvi Khandelwal, Anurag Basistha, Ayushi Jolotia +1
Deep learning proxies for Alternating Current Optimal Power Flow (ACOPF) lack systematic methods for determining architectural size. This paper conducts a constructive thought expe…
Small LLMs with Expert Blocks Are Good Enough for Hyperparamter Tuning
Om Naphade, Saksham Bansal, Parikshit Pareek
Hyper-parameter Tuning (HPT) is a necessary step in machine learning (ML) pipelines but becomes computationally expensive and opaque with larger models. Recently, Large Language Mo…
Optimization Proxies using Limited Labeled Data and Training Time -- A Semi-Supervised Bayesian Neural Network Approach
Parikshit Pareek, Abhijith Jayakumar, Kaarthik Sundar +2
Constrained optimization problems arise in various engineering systems such as inventory management and power grids. Standard deep neural network (DNN) based machine learning proxi…
Data-Efficient Strategies for Probabilistic Voltage Envelopes under Network Contingencies
Parikshit Pareek, Deepjyoti Deka, Sidhant Misra
This work presents an efficient data-driven method to construct probabilistic voltage envelopes (PVE) using power flow learning in grids with network contingencies. First, a networ…