181 citations · 184 across the 10 of their papers we have counts for
10 papers
InPhyRe Discovers: Large Multimodal Models Struggle in Inductive Physical Reasoning
Gautam Sreekumar, Vishnu Naresh Boddeti
Large multimodal models (LMMs) encode physical laws observed during training, such as momentum conservation, as parametric knowledge. It allows LMMs to answer physical reasoning qu…
Incorporating Interventional Independence Improves Robustness against Interventional Distribution Shift
Gautam Sreekumar, Vishnu Naresh Boddeti
We study the problem of learning robust discriminative representations of causally related latent variables given the underlying causal graph and a training set comprising passivel…
CoInD: Enabling Logical Compositions in Diffusion Models
Sachit Gaudi, Gautam Sreekumar, Vishnu Boddeti
How can we learn generative models to sample data with arbitrary logical compositions of statistically independent attributes? The prevailing solution is to sample from distributio…
Compositional World Knowledge leads to High Utility Synthetic data
Sachit Gaudi, Gautam Sreekumar, Vishnu Boddeti
Machine learning systems struggle with robustness, under subpopulation shifts. This problem becomes especially pronounced in scenarios where only a subset of attribute combinations…
OASIS Uncovers: High-Quality T2I Models, Same Old Stereotypes
Sepehr Dehdashtian, Gautam Sreekumar, Vishnu Naresh Boddeti
Images generated by text-to-image (T2I) models often exhibit visual biases and stereotypes of concepts such as culture and profession. Existing quantitative measures of stereotypes…
Spurious Correlations and Where to Find Them
Gautam Sreekumar, Vishnu Naresh Boddeti
Spurious correlations occur when a model learns unreliable features from the data and are a well-known drawback of data-driven learning. Although there are several algorithms propo…
On the Biometric Capacity of Generative Face Models
Vishnu Naresh Boddeti, Gautam Sreekumar, Arun Ross
There has been tremendous progress in generating realistic faces with high fidelity over the past few years. Despite this progress, a crucial question remains unanswered: "Given a…
Neuro-DynaStress: Predicting Dynamic Stress Distributions in Structural Components
Hamed Bolandi, Gautam Sreekumar, Xuyang Li +2
Structural components are typically exposed to dynamic loading, such as earthquakes, wind, and explosions. Structural engineers should be able to conduct real-time analysis in the…
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…
Neural Architecture Transfer
Zhichao Lu, Gautam Sreekumar, Erik Goodman +3
Neural architecture search (NAS) has emerged as a promising avenue for automatically designing task-specific neural networks. Existing NAS approaches require one complete search fo…