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Datasets have played a foundational role in the advancement of machine learning research. They form the basis for the models we design and deploy, as well as our primary medium for benchmarking and evaluation. Furthermore, the ways in which…

Machine Learning · Computer Science 2021-11-16 Amandalynne Paullada , Inioluwa Deborah Raji , Emily M. Bender , Emily Denton , Alex Hanna

Deep learning (DL), a new-generation of artificial neural network research, has transformed industries, daily lives and various scientific disciplines in recent years. DL represents significant progress in the ability of neural networks to…

Machine Learning · Statistics 2018-09-05 Chaopeng Shen

Machine learning encompasses a set of tools and algorithms which are now becoming popular in almost all scientific and technological fields. This is true for molecular dynamics as well, where machine learning offers promises of extracting…

The recent progresses in Machine Learning opened the door to actual applications of learning algorithms but also to new research directions both in the field of Machine Learning directly and, at the edges with other disciplines. The case…

Disordered Systems and Neural Networks · Physics 2023-07-17 Aurélien Decelle

We discuss the emerging advances and opportunities at the intersection of machine learning (ML) and climate physics, highlighting the use of ML techniques, including supervised, unsupervised, and equation discovery, to accelerate climate…

Atmospheric and Oceanic Physics · Physics 2024-08-20 Ching-Yao Lai , Pedram Hassanzadeh , Aditi Sheshadri , Maike Sonnewald , Raffaele Ferrari , Venkatramani Balaji

Modeling complex physical dynamics is a fundamental task in science and engineering. Traditional physics-based models are sample efficient, and interpretable but often rely on rigid assumptions. Furthermore, direct numerical approximation…

Machine Learning · Computer Science 2023-03-02 Rui Wang , Rose Yu

Integrating machine learning into reactive chemistry, materials discovery, and drug design is revolutionizing the development of novel molecules and materials. Machine Learning Interatomic Potentials (MLIPs) accurately predict energies and…

Chemical Physics · Physics 2025-07-04 Austin Rodriguez , Justin S. Smith , Jose L. Mendoza-Cortes

Machine learning (ML) has emerged as a pervasive tool in science, engineering, and beyond. Its success has also led to several synergies with molecular dynamics (MD) simulations, which we use to identify and characterize the major…

Biomolecules · Quantitative Biology 2022-05-09 Christopher Kolloff , Simon Olsson

Smarter applications are making better use of the insights gleaned from data, having an impact on every industry and research discipline. At the core of this revolution lies the tools and the methods that are driving it, from processing the…

Machine Learning · Computer Science 2020-04-01 Sebastian Raschka , Joshua Patterson , Corey Nolet

Understanding the reasons for the success of deep neural networks trained using stochastic gradient-based methods is a key open problem for the nascent theory of deep learning. The types of data where these networks are most successful,…

Machine Learning · Statistics 2020-12-04 Sebastian Goldt , Marc Mézard , Florent Krzakala , Lenka Zdeborová

Combining computational technologies and humanities is an ongoing effort aimed at making resources such as texts, images, audio, video, and other artifacts digitally available, searchable, and analyzable. In recent years, deep neural…

Machine Learning · Computer Science 2023-08-01 Omri Suissa , Avshalom Elmalech , Maayan Zhitomirsky-Geffet

Machine learning (ML) techniques have been demonstrated to improve the accuracy and efficiency of anomaly detection (AD) when compared to conventional methods. This has led to the adoption of ML for data quality monitoring (DQM) use cases…

Depending on the point of view, modern machine learning is either providing an unprecedented boost to the numerical methods of particle physics, or it is transforming the way we do science with vast amounts of complex data. In any case, it…

High Energy Physics - Phenomenology · Physics 2025-04-25 Tilman Plehn , Anja Butter , Barry Dillon , Theo Heimel , Claudius Krause , Ramon Winterhalder

Deep neural networks ("deep learning") have emerged as a technology of choice to tackle problems in natural language processing, computer vision, speech recognition and gameplay, and in just a few years has led to superhuman level…

Computational Physics · Physics 2020-05-05 Rama K. Vasudevan , Maxim Ziatdinov , Lukas Vlcek , Sergei V. Kalinin

Dusty plasma is a mixture of ions, electrons, and macroscopic charged particles that is commonly found in space and planetary environments. The particles interact through Coulomb forces mediated by the surrounding plasma, and as a result,…

Plasma Physics · Physics 2025-04-11 Wentao Yu , Eslam Abdelaleem , Ilya Nemenman , Justin C. Burton

Experimental Particle Physics has been at the forefront of analyzing the worlds largest datasets for decades. The HEP community was the first to develop suitable software and computing tools for this task. In recent times, new toolkits and…

Distributed, Parallel, and Cluster Computing · Computer Science 2017-11-23 Oliver Gutsche , Matteo Cremonesi , Peter Elmer , Bo Jayatilaka , Jim Kowalkowski , Jim Pivarski , Saba Sehrish , Cristina Mantilla Surez , Alexey Svyatkovskiy , Nhan Tran

Social media platforms provide valuable insights into mental health trends by capturing user-generated discussions on conditions such as depression, anxiety, and suicidal ideation. Machine learning (ML) and deep learning (DL) models have…

Computation and Language · Computer Science 2025-04-28 Zhanyi Ding , Zhongyan Wang , Yeyubei Zhang , Yuchen Cao , Yunchong Liu , Xiaorui Shen , Yexin Tian , Jianglai Dai

Deep neural networks (DNNs) exploit many layers and a large number of parameters to achieve excellent performance. The training process of DNN models generally handles large-scale input data with many sparse features, which incurs high…

Distributed, Parallel, and Cluster Computing · Computer Science 2023-06-08 Ji Liu , Zhihua Wu , Dianhai Yu , Yanjun Ma , Danlei Feng , Minxu Zhang , Xinxuan Wu , Xuefeng Yao , Dejing Dou

By leveraging neural networks, the emerging field of scientific machine learning (SciML) offers novel approaches to address complex problems governed by partial differential equations (PDEs). In practical applications, challenges arise due…

Machine Learning · Computer Science 2024-10-18 Handi Zhang , Langchen Liu , Lu Lu
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