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From the 1 of 68 linked papers with an AI index.

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20242026
most citedA Library for Learning Neural Operators

6 citations · 7 across the 19 of their papers we have counts for

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Showing 2025Show all

33 papers · 1 filter

eess.IV2025

Resolution-Agnostic Neural Operators for Multi-Rate Sparse-View CT

Aujasvit Datta, Jiayun Wang, Asad Aali +2

Sparse-view Computed Tomography (CT) reconstructs images from a limited number of X-ray projections to reduce radiation and scanning time, which is an ill-posed inverse problem. Ex…

cs.LG2025

Self Distillation Fine-Tuning of Protein Language Models Improves Versatility in Protein Design

Amin Tavakoli, Raswanth Murugan, Ozan Gokdemir +3

Supervised fine-tuning (SFT) is a standard approach for adapting large language models to specialized domains, yet its application to protein sequence modeling and protein language…

astro-ph.HE2025

From Black Hole to Galaxy: Neural Operator: Framework for Accretion and Feedback Dynamics

Nihaal Bhojwani, Chuwei Wang, Hai-Yang Wang +3

Modeling how supermassive black holes co-evolve with their host galaxies is notoriously hard because the relevant physics spans nine orders of magnitude in scale-from milliparsecs…

quant-ph2025

GPU-accelerated Effective Hamiltonian Calculator

Abhishek Chakraborty, Taylor L. Patti, Brucek Khailany +2

Effective Hamiltonian calculations for large quantum systems can be both analytically intractable and numerically expensive using standard techniques. In this manuscript, we presen…

cs.CV2025

Generating Natural-Language Surgical Feedback: From Structured Representation to Domain-Grounded Evaluation

Firdavs Nasriddinov, Rafal Kocielnik, Anima Anandkumar +1

High-quality intraoperative feedback from a surgical trainer is pivotal for improving trainee performance and long-term skill acquisition. Automating natural, trainer-style feedbac…

cs.LG2025

EcoSpa: Efficient Transformer Training with Coupled Sparsity

Jinqi Xiao, Cheng Luo, Lingyi Huang +8

Transformers have become the backbone of modern AI, yet their high computational demands pose critical system challenges. While sparse training offers efficiency gains, existing me…