5 citations · 11 across the 14 of their papers we have counts for
7 papers · 1 filter
Towards Fair and Calibrated Models
Anand Brahmbhatt, Vipul Rathore, Mausam +1
Recent literature has seen a significant focus on building machine learning models with specific properties such as fairness, i.e., being non-biased with respect to a given set of…
Neural Models for Output-Space Invariance in Combinatorial Problems
Yatin Nandwani, Vidit Jain, Mausam +1
Recently many neural models have been proposed to solve combinatorial puzzles by implicitly learning underlying constraints using their solved instances, such as sudoku or graph co…
Towards an Interpretable Latent Space in Structured Models for Video Prediction
Rushil Gupta, Vishal Sharma, Yash Jain +3
We focus on the task of future frame prediction in video governed by underlying physical dynamics. We work with models which are object-centric, i.e., explicitly work with object r…
ScRAE: Deterministic Regularized Autoencoders with Flexible Priors for Clustering Single-cell Gene Expression Data
Arnab Kumar Mondal, Himanshu Asnani, Parag Singla +1
Clustering single-cell RNA sequence (scRNA-seq) data poses statistical and computational challenges due to their high-dimensionality and data-sparsity, also known as `dropout' even…
Neural Learning of One-of-Many Solutions for Combinatorial Problems in Structured Output Spaces
Yatin Nandwani, Deepanshu Jindal, Mausam +1
Recent research has proposed neural architectures for solving combinatorial problems in structured output spaces. In many such problems, there may exist multiple solutions for a gi…
To Regularize or Not To Regularize? The Bias Variance Trade-off in Regularized AEs
Arnab Kumar Mondal, Himanshu Asnani, Parag Singla +1
Regularized Auto-Encoders (RAEs) form a rich class of neural generative models. They effectively model the joint-distribution between the data and the latent space using an Encoder…