4 citations · 5 across the 6 of their papers we have counts for
6 papers
Uncovering Physical Drivers of Dark Matter Halo Structures with Auxiliary-Variable-Guided Generative Models
Arkaprabha Ganguli, Anirban Samaddar, Florian Kéruzoré +4
Deep generative models (DGMs) compress high-dimensional data but often entangle distinct physical factors in their latent spaces. We present an auxiliary-variable-guided framework…
Multi-task Modeling for Engineering Applications with Sparse Data
Yigitcan Comlek, R. Murali Krishnan, Sandipp Krishnan Ravi +7
Modern engineering and scientific workflows often require simultaneous predictions across related tasks and fidelity levels, where high-fidelity data is scarce and expensive, while…
LExI: Layer-Adaptive Active Experts for Efficient MoE Model Inference
Krishna Teja Chitty-Venkata, Sandeep Madireddy, Murali Emani +1
Mixture-of-Experts (MoE) models scale efficiently by activating only a subset of experts per token, offering a computationally sparse alternative to dense architectures. While prio…
AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons
Shaona Ghosh, Heather Frase, Adina Williams +99
The rapid advancement and deployment of AI systems have created an urgent need for standard safety-evaluation frameworks. This paper introduces AILuminate v1.0, the first comprehen…
AstroMLab 1: Who Wins Astronomy Jeopardy!?
Yuan-Sen Ting, Tuan Dung Nguyen, Tirthankar Ghosal +8
We present a comprehensive evaluation of proprietary and open-weights large language models using the first astronomy-specific benchmarking dataset. This dataset comprises 4,425 mu…
Physical Benchmarking for AI-Generated Cosmic Web
Xiaofeng Dong, Nesar Ramachandra, Salman Habib +3
The potential of deep learning based image-to-image translations has recently drawn a lot of attention; one intriguing possibility is that of generating cosmological predictions wi…