Sparse data is fundamental to scientific simulations in biology and physics, from single-cell gene expression to particle calorimetry, where exact zeros encode physical absence rather than weak signal. However, existing diffusion models lack the physical rigor to faithfully represent this sparsity. This work introduces Sparse Data Diffusion (SDD), a generative method that explicitly models exact zeros via Sparsity Bits, unifying efficient ML generation with physically grounded sparsity handling. Empirical validation in particle physics and single-cell biology demonstrates that SDD achieves higher fidelity than baseline methods in capturing sparse patterns critical for scientific analysis, advancing scalable and physically faithful simulation.
Sparse and nonparametric estimation of equations governing dynamical systems with applications to biology
Department of Information Engineering, University of Padova（信息工程系，帕多瓦大学）；Department of Pathology, Cambridge University（病理学系，剑桥大学）；Department of Applied Mathematics, University of Washington（应用数学系，华盛顿大学）；Department of Pharmacology, University of North Carolina at Chapel Hill（药理学系，北卡罗来纳大学教堂山分校）
This paper deals with unsupervised clustering with feature selection. The problem is to estimate both labels and a sparse projection matrix of weights. To address this combinatorial non-convex problem maintaining a strict control on the sparsity of the matrix of weights, we propose an alternating minimization of the Frobenius norm criterion. We provide a new efficient algorithm named K-sparse which alternates k-means with projection-gradient minimization. The projection-gradient step is a method of splitting type, with exact projection on the $\ell^1$ ball to promote sparsity. The convergence …
École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）；School of Engineering（工程学院）
Potential societal and environmental effects such as the rapidly increasing resource use and the associated environmental impact, reproducibility issues, and exclusivity, the privatization of ML research leading to a public research brain-drain, a narrowing of the research effort caused by a focus on deep learning, and the introduction of biases through a lack of sociodemographic diversity in data and personnel caused by recent developments in machine learning are a current topic of discussion and scientific publications. However, these discussions and publications focus mainly on computer sci…
Artificial neural networks took a lot of inspiration from their biological counterparts in becoming our best machine perceptual systems. This work summarizes some of that history and incorporates modern theoretical neuroscience into experiments with artificial neural networks from the field of deep learning. Specifically, iterative magnitude pruning is used to train sparsely connected networks with 33x fewer weights without loss in performance. These are used to test and ultimately reject the hypothesis that weight sparsity alone improves image noise robustness. Recent work mitigated catastrop…
Symmetry-Informed Geometric Representation for Molecules, Proteins, and Crystalline Materials
Mila - Québec Artificial Intelligence Institute（米拉-魁北克人工智能研究所）；Université de Montréal（蒙特利尔大学）；University of Chinese Academy of Sciences（中国科学院大学）；Carnegie Mellon University（卡内基梅隆大学）；University of Toronto（多伦多大学）；University of California, Berkeley（加州大学伯克利分校）；Massachusetts Institute of Technology（麻省理工学院）；California Institute of Technology（加州理工学院）；National Research Council Canada（加拿大国家研究委员会）；University of Ottawa（渥太华大学）；HEC Montréal（蒙特利尔高等商学院）
Probing and steering biology across Boltz-1s trunk-diffusion boundary
University of Oxford（牛津大学）；Evolvere Biosciences（Evolvere生物科学公司）；Botnar Research Centre（博特纳研究中心）；Kavli Institute for Nanoscience Discovery, University of Oxford（牛津大学卡夫利纳米科学发现研究所）；Department of Chemistry, University of Oxford（牛津大学化学系）
