Scientific Machine Learning
ML for scientific computing and discovery
Top Papers
Fast Machine Unlearning without Retraining through Selective Synaptic Dampening
Jack Foster, Stefan Schoepf, Alexandra Brintrup
MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering
Jun Shern Chan, Neil Chowdhury, Oliver Jaffe et al.
Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction
Xiang Fu, Brandon Wood, Luis Barroso-Luque et al.
MAVIS: Mathematical Visual Instruction Tuning with an Automatic Data Engine
Renrui Zhang, Xinyu Wei, Dongzhi Jiang et al.
Scaling Laws for Data Filtering— Data Curation cannot be Compute Agnostic
Sachin Goyal, Pratyush Maini, Zachary Lipton et al.
SWE-smith: Scaling Data for Software Engineering Agents
John Yang, Kilian Lieret, Carlos Jimenez et al.
DSBench: How Far Are Data Science Agents from Becoming Data Science Experts?
Liqiang Jing, Zhehui Huang, Xiaoyang Wang et al.
CycleResearcher: Improving Automated Research via Automated Review
Yixuan Weng, Minjun Zhu, Guangsheng Bao et al.
LLM-SR: Scientific Equation Discovery via Programming with Large Language Models
Parshin Shojaee, Kazem Meidani, Shashank Gupta et al.
MuSc: Zero-Shot Industrial Anomaly Classification and Segmentation with Mutual Scoring of the Unlabeled Images
Xurui Li, Ziming Huang, Feng Xue et al.
STI-Bench: Are MLLMs Ready for Precise Spatial-Temporal World Understanding?
Yun Li, Yiming Zhang, Tao Lin et al.
DiscoveryBench: Towards Data-Driven Discovery with Large Language Models
Bodhisattwa Prasad Majumder, Harshit Surana, Dhruv Agarwal et al.
Scaling Wearable Foundation Models
Girish Narayanswamy, Xin Liu, Kumar Ayush et al.
The dark side of the forces: assessing non-conservative force models for atomistic machine learning
Filippo Bigi, Marcel Langer, Michele Ceriotti
From Mechanistic Interpretability to Mechanistic Biology: Training, Evaluating, and Interpreting Sparse Autoencoders on Protein Language Models
Etowah Adams, Liam Bai, Minji Lee et al.
Machine Unlearning Fails to Remove Data Poisoning Attacks
Martin Pawelczyk, Jimmy Di, Yiwei Lu et al.
MathCoder2: Better Math Reasoning from Continued Pretraining on Model-translated Mathematical Code
Zimu Lu, Aojun Zhou, Ke Wang et al.
HyperFast: Instant Classification for Tabular Data
David Bonet, Daniel Mas Montserrat, Xavier Giró-i-Nieto et al.
Learning to design protein-protein interactions with enhanced generalization
Anton Bushuiev, Roman Bushuiev, Petr Kouba et al.
ClinicalLab: Aligning Agents for Multi-Departmental Clinical Diagnostics in the Real World
Weixiang Yan, Haitian Liu, Tengxiao Wu et al.
Towards Scalable Exact Machine Unlearning Using Parameter-Efficient Fine-Tuning
Somnath Basu Roy Chowdhury, Krzysztof Choromanski, Arijit Sehanobish et al.
Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians
Ishan Amin, Sanjeev Raja, Aditi Krishnapriyan
Adaptive Message Passing: A General Framework to Mitigate Oversmoothing, Oversquashing, and Underreaching
Federico Errica, Henrik Christiansen, Viktor Zaverkin et al.
Benchmarking Predictive Coding Networks -- Made Simple
Luca Pinchetti, Chang Qi, Oleh Lokshyn et al.
Learning Interpretable Hierarchical Dynamical Systems Models from Time Series Data
Manuel Brenner, Elias Weber, Georgia Koppe et al.
AI Research Agents for Machine Learning: Search, Exploration, and Generalization in MLE-bench
Edan Toledo, Karen Hambardzumyan, Martin Josifoski et al.
Learning MDL Logic Programs from Noisy Data
Céline Hocquette, Andreas Niskanen, Matti Järvisalo et al.
Apollo-MILP: An Alternating Prediction-Correction Neural Solving Framework for Mixed-Integer Linear Programming
Haoyang Liu, Jie Wang, Zijie Geng et al.
BatteryML: An Open-source Platform for Machine Learning on Battery Degradation
Han Zhang, Xiaofan Gui, Shun Zheng et al.
LiveXiv - A Multi-Modal live benchmark based on Arxiv papers content
Nimrod Shabtay, Felipe Maia Polo, Sivan Doveh et al.
Adaptive Self-improvement LLM Agentic System for ML Library Development
Genghan Zhang, Weixin Liang, Olivia Hsu et al.
The ML.ENERGY Benchmark: Toward Automated Inference Energy Measurement and Optimization
Jae-Won Chung, Jeff J. Ma, Ruofan Wu et al.
MindLLM: A Subject-Agnostic and Versatile Model for fMRI-to-text Decoding
Weikang Qiu, Zheng Huang, Haoyu Hu et al.
Fast training and sampling of Restricted Boltzmann Machines
Nicolas BEREUX, Aurélien Decelle, Cyril Furtlehner et al.
Deep Nonlinear Sufficient Dimension Reduction
Yinfeng Chen, Yuling Jiao, Rui Qiu et al.
Neural Auto-designer for Enhanced Quantum Kernels
Cong Lei, Yuxuan Du, Peng Mi et al.
Bridging the Semantic Latent Space between Brain and Machine: Similarity Is All You Need
Jiaxuan Chen, Yu Qi, Yueming Wang et al.
On Harmonizing Implicit Subpopulations
Feng Hong, Jiangchao Yao, YUEMING LYU et al.
Data-Juicer Sandbox: A Feedback-Driven Suite for Multimodal Data-Model Co-development
Daoyuan Chen, Haibin Wang, Yilun Huang et al.
FlashMD: long-stride, universal prediction of molecular dynamics
Filippo Bigi, Sanggyu Chong, Agustinus Kristiadi et al.
Learning-Augmented Search Data Structures
Chunkai Fu, Brandon G. Nguyen, Jung Seo et al.
Causal Discovery from Conditionally Stationary Time Series
Carles Balsells-Rodas, Xavier Sumba, Tanmayee Narendra et al.
Position: The Artificial Intelligence and Machine Learning Community Should Adopt a More Transparent and Regulated Peer Review Process
Jing Yang
ML-SemReg: Boosting Point Cloud Registration with Multi-level Semantic Consistency
Shaocheng Yan, Pengcheng Shi, Jiayuan Li
Learning a Neural Solver for Parametric PDEs to Enhance Physics-Informed Methods
Lise Le Boudec, Emmanuel de Bézenac, Louis Serrano et al.
Data-Juicer 2.0: Cloud-Scale Adaptive Data Processing for and with Foundation Models
Daoyuan Chen, Yilun Huang, Xuchen Pan et al.
Scalable Bayesian Learning with posteriors
Samuel Duffield, Kaelan Donatella, Johnathan Chiu et al.
PINNsAgent: Automated PDE Surrogation with Large Language Models
Qingpo Wuwu, Chonghan Gao, Tianyu Chen et al.
Understanding Generalization in Quantum Machine Learning with Margins
TAK HUR, Daniel Kyungdeock Park
DOLPHIN: A Programmable Framework for Scalable Neurosymbolic Learning
Aaditya Naik, Jason Liu, Claire Wang et al.
In-Context Learning of Stochastic Differential Equations with Foundation Inference Models
Patrick Seifner, Kostadin Cvejoski, David Berghaus et al.
SymMaP: Improving Computational Efficiency in Linear Solvers through Symbolic Preconditioning
Hong Wang, Jie Wang, Minghao Ma et al.
Scaling Physical Reasoning with the PHYSICS Dataset
Shenghe Zheng, Qianjia Cheng, Junchi Yao et al.
X-Hacking: The Threat of Misguided AutoML
Rahul Sharma, Sumantrak Mukherjee, Andrea Šipka et al.
MLE-Dojo: Interactive Environments for Empowering LLM Agents in Machine Learning Engineering
Rushi Qiang, Yuchen Zhuang, Yinghao Li et al.
Towards Establishing Guaranteed Error for Learned Database Operations
Sepanta Zeighami, Cyrus Shahabi
An LLM-Empowered Adaptive Evolutionary Algorithm for Multi-Component Deep Learning Systems
Haoxiang Tian, Xingshuo Han, Guoquan Wu et al.
Online Continuous Generalized Category Discovery
Keon-Hee Park, Hakyung Lee, Kyungwoo Song et al.
Epistemic Monte Carlo Tree Search
Yaniv Oren, Viliam Vadocz, Matthijs T. J. Spaan et al.
SnowMaster: Comprehensive Real-world Image Desnowing via MLLM with Multi-Model Feedback Optimization
Jianyu LAI, Sixiang Chen, yunlong lin et al.
Causal-StoNet: Causal Inference for High-Dimensional Complex Data
Yaxin Fang, Faming Liang
Open-Insect: Benchmarking Open-Set Recognition of Novel Species in Biodiversity Monitoring
Yuyan Chen, Nico Lang, B. Schmidt et al.
MLRC-Bench: Can Language Agents Solve Machine Learning Research Challenges?
Yunxiang Zhang, Muhammad Khalifa, Shitanshu Bhushan et al.
Microcanonical Langevin Ensembles: Advancing the Sampling of Bayesian Neural Networks
Emanuel Sommer, Jakob Robnik, Giorgi Nozadze et al.
ECD: A Machine Learning Benchmark for Predicting Enhanced-Precision Electronic Charge Density in Crystalline Inorganic Materials
Pin Chen, Zexin Xu, Qing Mo et al.
No Equations Needed: Learning System Dynamics Without Relying on Closed-Form ODEs
Krzysztof Kacprzyk, Mihaela van der Schaar
AutoDiscovery: Open-ended Scientific Discovery via Bayesian Surprise
Dhruv Agarwal, Bodhisattwa Prasad Majumder, Reece Adamson et al.
PhysGym: Benchmarking LLMs in Interactive Physics Discovery with Controlled Priors
Yimeng Chen, Piotr Piękos, Mateusz Ostaszewski et al.
Common Task Framework For a Critical Evaluation of Scientific Machine Learning Algorithms
Philippe Wyder, Judah Goldfeder, Alexey Yermakov et al.
AstroVisBench: A Code Benchmark for Scientific Computing and Visualization in Astronomy
Sebastian Joseph, Syed M. Husain, Stella Offner et al.
CosmoBench: A Multiscale, Multiview, Multitask Cosmology Benchmark for Geometric Deep Learning
Teresa Huang, Richard Stiskalek, Jun-Young Lee et al.
Prices, Bids, Values: One ML-Powered Combinatorial Auction to Rule Them All
Ermis Soumalias, Jakob Heiss, Jakob Weissteiner et al.
AutoSciDACT: Automated Scientific Discovery through Contrastive Embedding and Hypothesis Testing
Sam Bright-Thonney, Christina Reissel, Gaia Grosso et al.
CarbonSense: A Multimodal Dataset and Baseline for Carbon Flux Modelling
Matthew Fortier, Mats L. Richter, Oliver Sonnentag et al.
The Catechol Benchmark: Time-series Solvent Selection Data for Few-shot Machine Learning
Toby Boyne, Juan Campos, Rebecca Langdon et al.
A Multiscale Frequency Domain Causal Framework for Enhanced Pathological Analysis
Xiaoyu Cui, Weixing Chen, Jiandong Su
ALINE: Joint Amortization for Bayesian Inference and Active Data Acquisition
Daolang Huang, Xinyi Wen, Ayush Bharti et al.
Towards Source-Free Machine Unlearning
Sk Miraj Ahmed, Umit Basaran, Dripta S. Raychaudhuri et al.
AutoSciLab: A Self-Driving Laboratory for Interpretable Scientific Discovery
Saaketh Desai, Sadhvikas Addamane, Jeffrey Y. Tsao et al.
PyTDC: A multimodal machine learning training, evaluation, and inference platform for biomedical foundation models
Alex Velez-Arce, Marinka Zitnik
ML4CFD Competition: Results and Retrospective Analysis
Mouadh Yagoubi, David Danan, Milad LEYLI ABADI et al.
LC-Opt: Benchmarking Reinforcement Learning and Agentic AI for End-to-End Liquid Cooling Optimization in Data Centers
Avisek Naug, Antonio Guillen-Perez, Vineet Kumar et al.
COGNATE: Acceleration of Sparse Tensor Programs on Emerging Hardware using Transfer Learning
Chamika Sudusinghe, Gerasimos Gerogiannis, Damitha Lenadora et al.
Towards scientific discovery with dictionary learning: Extracting biological concepts from microscopy foundation models
Konstantin Donhauser, Kristina Ulicna, Gemma Moran et al.
ADELA: Accelerating Evolutionary Design of Machine Learning Pipelines with the Accompanying Surrogate Model
Yang Gu, Jian Cao, Hengyu You et al.
Active Measurement: Efficient Estimation at Scale
Max Hamilton, Jinlin Lai, Wenlong Zhao et al.
THE ROBUSTNESS OF DIFFERENTIABLE CAUSAL DISCOVERY IN MISSPECIFIED SCENARIOS
Huiyang Yi, Yanyan He, Duxin Chen et al.
Towards Learning High-Precision Least Squares Algorithms with Sequence Models
Jerry Liu, Jessica Grogan, Owen Dugan et al.
Can Private Machine Learning Be Fair?
Joseph Rance, Filip Svoboda
GlobalTomo: A global dataset for physics-ML seismic wavefield modeling and FWI
Shiqian Li, Zhi Li, Zhancun Mu et al.
AiDE-Q: Synthetic Labeled Datasets Can Enhance Learning Models for Quantum Property Estimation
Xinbiao Wang, Yuxuan Du, Zihan Lou et al.
Understanding Generalization in Physics Informed Models through Affine Variety Dimensions
Takeshi Koshizuka, Issei Sato
ML4CO-Bench-101: Benchmark Machine Learning for Classic Combinatorial Problems on Graphs
Jiale Ma, Wenzheng Pan, Yang Li et al.
Accelerating Legacy Numerical Solvers by Non-intrusive Gradient-based Meta-solving
Sohei Arisaka, Qianxiao Li
MMTU: A Massive Multi-Task Table Understanding and Reasoning Benchmark
Junjie Xing, Yeye He, Mengyu Zhou et al.
Position: Is machine learning good or bad for the natural sciences?
David W. Hogg, Soledad Villar
EDBench: Large-Scale Electron Density Data for Molecular Modeling
Hongxin Xiang, Ke Li, Mingquan Liu et al.
Semantic-KG: Using Knowledge Graphs to Construct Benchmarks for Measuring Semantic Similarity
Qiyao Wei, Edward R Morrell, Lea Goetz et al.
CGBench: Benchmarking Language Model Scientific Reasoning for Clinical Genetics Research
Owen Queen, Harrison Zhang, James Zou
Position: Mission Critical – Satellite Data is a Distinct Modality in Machine Learning
Esther Rolf, Konstantin Klemmer, Caleb Robinson et al.