13 papers
Panorama: Fast-Track Nearest Neighbors
Vansh Ramani, Alexis Schlomer, Akash Nayar +3
Approximate Nearest-Neighbor Search (ANNS) pipelines for high-dimensional neural embeddings spend the bulk of their query time in candidate verification, making it the primary bott…
UniFFBench: Evaluating Universal Machine Learning Force Fields Against Experimental Measurements
Sajid Mannan, Vaibhav Bihani, Carmelo Gonzales +5
Universal machine learning force fields (UMLFFs) promise to revolutionize materials science by enabling rapid atomistic simulations across the periodic table. However, their evalua…
SC3: The Multi-Solvent Solubility Challenge and Benchmark
Vansh Ramani, Har Ashish Arora, Dhairya Kuchhal +4
Solubility prediction is a standard benchmark in computational chemistry, yet multi-solvent models which reportedly approach the experimental-noise ceiling (i.e. the aleatoric limi…
GFFMERGE: Efficient Merging of Graph Neural Force Fields and Beyond
Parth Verma, Parv P. Singh, Vipul Garg +3
Graph Neural Networks (GNNs) have revolutionized Neural Force Fields for atomistic simulations, achieving near-quantum accuracy at reduced cost, yet adapting these models to new ch…
Position: Graph Condensation Needs a Reset -- Move Beyond Full-dataset Training and Model-Dependence
Mridul Gupta, Samyak Jain, Vansh Ramani +2
Graph Neural Networks (GNNs) are powerful tools for learning from graph-structured data, but their scalability is increasingly strained by the size of real-world graphs in domains…
Revealing Interpretable Failure Modes of VLMs
Isha Chaudhary, Vedaant V Jain, Kavya Sachdeva +2
Vision-Language Models (VLMs) are increasingly used in safety-critical applications because of their broad reasoning capabilities and ability to generalize with minimal task-specif…