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Dataset distillation (DD) aims to minimize the time and memory consumption needed for training deep neural networks on large datasets, by creating a smaller synthetic dataset that has similar performance to that of the full real dataset.…

Computer Vision and Pattern Recognition · Computer Science 2025-05-20 Xinhao Zhong , Bin Chen , Hao Fang , Xulin Gu , Shu-Tao Xia , En-Hui Yang

Partial differential equations (PDEs) with multiple scales or those defined over sufficiently large domains arise in various areas of science and engineering and often present problems when approximating the solutions numerically. Machine…

Numerical Analysis · Mathematics 2024-05-27 Eddel Elí Ojeda Avilés , Daniel Olmos-Liceaga , Jae-Hun Jung

Multimodal data is a precious asset enabling a variety of downstream tasks in machine learning. However, real-world data collected across different modalities is often not paired, which is a significant challenge to learn a joint…

Machine Learning · Computer Science 2025-08-11 Mustapha Bounoua , Giulio Franzese , Pietro Michiardi

The aim of survey statistics is to produce estimates with a minimal bias and a corresponding acceptable variance given a specific budget, preferable with a minor response burden for the participants. In recent years, considerable efforts…

Methodology · Statistics 2026-04-02 Martin Hyllienmark , Gustaf Strandell

We propose a new framework for efficiently sampling from complex probability distributions using a combination of normalizing flows and elliptical slice sampling (Murray et al., 2010). The central idea is to learn a diffeomorphism, through…

Methodology · Statistics 2023-03-28 Alberto Cabezas , Christopher Nemeth

We study the problem of finding efficient sampling policies in an edge-based feedback system, where sensor samples are offloaded to a back-end server that processes them and generates feedback to a user. Sampling the system at maximum…

Information Theory · Computer Science 2023-02-07 Vishnu Narayanan Moothedath , Jaya Prakash Champati , James Gross

Recent applications pose requirements of both cross-domain knowledge transfer and model compression to machine learning models due to insufficient training data and limited computational resources. In this paper, we propose a new knowledge…

Computer Vision and Pattern Recognition · Computer Science 2021-05-03 Zhiyuan Wu , Yu Jiang , Minghao Zhao , Chupeng Cui , Zongmin Yang , Xinhui Xue , Hong Qi

Novel Markov Chain Monte Carlo (MCMC) methods have enabled the generation of large ensembles of redistricting plans through graph partitioning. However, existing algorithms such as Reversible Recombination (RevReCom) and Metropolized Forest…

Data Structures and Algorithms · Computer Science 2025-10-28 Atticus McWhorter , Daryl DeFord

Segmentation of microscopy images constitutes an ill-posed inverse problem due to measurement noise, weak object boundaries, and limited labeled data. Although deep neural networks provide flexible nonparametric estimators, unconstrained…

Computer Vision and Pattern Recognition · Computer Science 2026-02-03 Seema K. Poudel , Sunny K. Khadka

Contrastive Learning and Masked Image Modelling have demonstrated exceptional performance on self-supervised representation learning, where Momentum Contrast (i.e., MoCo) and Masked AutoEncoder (i.e., MAE) are the state-of-the-art,…

Computer Vision and Pattern Recognition · Computer Science 2023-02-07 Yuchong Yao , Nandakishor Desai , Marimuthu Palaniswami

We propose a new unsupervised anomaly detection method based on the sliced-Wasserstein distance for training data selection in machine learning approaches. Our filtering technique is interesting for decision-making pipelines deploying…

Machine Learning · Computer Science 2025-04-18 Julien Pallage , Antoine Lesage-Landry

Detecting Out-of-Distribution (OOD) samples in real world visual applications like classification or object detection has become a necessary precondition in today's deployment of Deep Learning systems. Many techniques have been proposed, of…

Computer Vision and Pattern Recognition · Computer Science 2022-08-24 Abhishek Joshi , Sathish Chalasani , Kiran Nanjunda Iyer

Compressive sensing (CS) reconstructs images from sub-Nyquist measurements by solving a sparsity-regularized inverse problem. Traditional CS solvers use iterative optimizers with hand crafted sparsifiers, while early data-driven methods…

Computer Vision and Pattern Recognition · Computer Science 2023-06-02 Pamuditha Somarathne , Tharindu Wickremasinghe , Amashi Niwarthana , A. Thieshanthan , Chamira U. S. Edussooriya , Dushan N. Wadduwage

This letter considers optimizing user association in a heterogeneous network via utility maximization, which is a combinatorial optimization problem due to integer constraints. Different from existing solutions based on convex optimization,…

Information Theory · Computer Science 2018-06-12 Xietian Huang , Wei Xu , Guo Xie , Shi Jin , Xiaohu You

We present a novel energy-based numerical analysis of semilinear diffusion-reaction boundary value problems. Based on a suitable variational setting, the proposed computational scheme can be seen as an energy minimisation approach. More…

Numerical Analysis · Mathematics 2022-02-16 Mario Amrein , Pascal Heid , Thomas P. Wihler

We present a novel approach to knowledge transfer in model-based reinforcement learning, addressing the critical challenge of deploying large world models in resource-constrained environments. Our method efficiently distills a high-capacity…

Machine Learning · Computer Science 2025-07-03 Dmytro Kuzmenko , Nadiya Shvai

The Sliced Wasserstein (SW) distance has become a popular alternative to the Wasserstein distance for comparing probability measures. Widespread applications include image processing, domain adaptation and generative modelling, where it is…

Machine Learning · Statistics 2025-05-15 Eloi Tanguy , Rémi Flamary , Julie Delon

We introduce the Mutual Information Machine (MIM), a probabilistic auto-encoder for learning joint distributions over observations and latent variables. MIM reflects three design principles: 1) low divergence, to encourage the encoder and…

Machine Learning · Computer Science 2020-02-24 Micha Livne , Kevin Swersky , David J. Fleet

Energy-Based Models (EBMs) offer a versatile framework for modeling complex data distributions. However, training and sampling from EBMs continue to pose significant challenges. The widely-used Denoising Score Matching (DSM) method for…

Machine Learning · Statistics 2024-03-20 Mingtian Zhang , Alex Hawkins-Hooker , Brooks Paige , David Barber

SDE-based methods such as denoising diffusion probabilistic models (DDPMs) have shown remarkable success in real-world sample generation tasks. Prior analyses of DDPMs have been focused on the exponential Euler discretization, showing…

Machine Learning · Computer Science 2025-11-10 Matthew S. Zhang , Stephen Huan , Jerry Huang , Nicholas M. Boffi , Sitan Chen , Sinho Chewi
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