5 citations · 5 across the 7 of their papers we have counts for
7 papers
Beyond Static Costs: Learning-Dynamics Aware Loss Functions for Long-Tailed Classification
Varad Shinde, Nikhil Kumar Shrey, Magesh Rajasekaran +5
Deep learning models in computer vision face significant challenges when trained on long-tailed datasets, where a few majority classes dominate while many minority classes are seve…
Benchmarking Artificial Intelligence Models for Daily Coastal Hypoxia Forecasting
Magesh Rajasekaran, Md Saiful Sajol, Chris Alvin +3
Coastal hypoxia, especially in the northern part of Gulf of Mexico, presents a persistent ecological and economic concern. Seasonal models offer coarse forecasts that miss the fine…
COMBOOD: A Semiparametric Approach for Detecting Out-of-distribution Data for Image Classification
Magesh Rajasekaran, Md Saiful Islam Sajol, Frej Berglind +2
Identifying out-of-distribution (OOD) data at inference time is crucial for many machine learning applications, especially for automation. We present a novel unsupervised semi-para…
A Multimodal Human Protein Embeddings Database: DeepDrug Protein Embeddings Bank (DPEB)
Md Saiful Islam Sajol, Magesh Rajasekaran, Hayden Gemeinhardt +3
Computationally predicting protein-protein interactions (PPIs) is challenging due to the lack of integrated, multimodal protein representations. DPEB is a curated collection of 22,…
Semi-Supervised Deep Domain Adaptation for Predicting Solar Power Across Different Locations
Md Shazid Islam, A S M Jahid Hasan, Md Saydur Rahman +1
Accurate solar generation prediction is essential for proper estimation of renewable energy resources across diverse geographic locations. However, geographical and weather feature…
Location Agnostic Source-Free Domain Adaptive Learning to Predict Solar Power Generation
Md Shazid Islam, A S M Jahid Hasan, Md Saydur Rahman +3
The prediction of solar power generation is a challenging task due to its dependence on climatic characteristics that exhibit spatial and temporal variability. The performance of a…
XOOD: Extreme Value Based Out-Of-Distribution Detection For Image Classification
Frej Berglind, Haron Temam, Supratik Mukhopadhyay +4
Detecting out-of-distribution (OOD) data at inference time is crucial for many applications of machine learning. We present XOOD: a novel extreme value-based OOD detection framewor…