activity
20172024
most citedThe Dark Side of Dataset Scaling: Evaluating Racial Classification in Multimodal Models

22 citations · 67 across the 11 of their papers we have counts for

collaborators

21 papers

cs.CY202422 cited

The Dark Side of Dataset Scaling: Evaluating Racial Classification in Multimodal Models

Abeba Birhane, Sepehr Dehdashtian, Vinay Uday Prabhu +1

Scale the model, scale the data, scale the GPU farms is the reigning sentiment in the world of generative AI today. While model scaling has been extensively studied, data scaling a…

cs.LG20223 cited

Physics Informed Neural Network for Dynamic Stress Prediction

Hamed Bolandi, Gautam Sreekumar, Xuyang Li +2

Structural failures are often caused by catastrophic events such as earthquakes and winds. As a result, it is crucial to predict dynamic stress distributions during highly disrupti…

cs.CV2022

Do learned representations respect causal relationships?

Lan Wang, Vishnu Naresh Boddeti

Data often has many semantic attributes that are causally associated with each other. But do attribute-specific learned representations of data also respect the same causal relatio…

cs.CV2021

3DFaceFill: An Analysis-By-Synthesis Approach to Face Completion

Rahul Dey, Vishnu Boddeti

Existing face completion solutions are primarily driven by end-to-end models that directly generate 2D completions of 2D masked faces. By having to implicitly account for geometric…

cs.LG2021

Adversarial Representation Learning With Closed-Form Solvers

Bashir Sadeghi, Lan Wang, Vishnu Naresh Boddeti

Adversarial representation learning aims to learn data representations for a target task while removing unwanted sensitive information at the same time. Existing methods learn mode…

cs.CV2021

Spatially-Adaptive Image Restoration using Distortion-Guided Networks

Kuldeep Purohit, Maitreya Suin, A. N. Rajagopalan +1

We present a general learning-based solution for restoring images suffering from spatially-varying degradations. Prior approaches are typically degradation-specific and employ the…