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We propose a new model for supervised learning to rank. In our model, the relevance labels are assumed to follow a categorical distribution whose probabilities are constructed based on a scoring function. We optimize the training objective…

Machine Learning · Computer Science 2020-02-19 Siamak Zamani Dadaneh , Shahin Boluki , Mingyuan Zhou , Xiaoning Qian

Machine learning interatomic force fields are promising for combining high computational efficiency and accuracy in modeling quantum interactions and simulating atomistic dynamics. Active learning methods have been recently developed to…

Computational Physics · Physics 2023-03-07 Yu Xie , Jonathan Vandermause , Senja Ramakers , Nakib H. Protik , Anders Johansson , Boris Kozinsky

In aspect-based sentiment analysis (ABSA), many neural models are equipped with an attention mechanism to quantify the contribution of each context word to sentiment prediction. However, such a mechanism suffers from one drawback: only a…

Computation and Language · Computer Science 2021-03-08 Jinsong Su , Jialong Tang , Hui Jiang , Ziyao Lu , Yubin Ge , Linfeng Song , Deyi Xiong , Le Sun , Jiebo Luo

Statistical shape modeling (SSM) is an enabling quantitative tool to study anatomical shapes in various medical applications. However, directly using 3D images in these applications still has a long way to go. Recent deep learning methods…

Computer Vision and Pattern Recognition · Computer Science 2023-10-04 Abu Zahid Bin Aziz , Jadie Adams , Shireen Elhabian

Sharpness-aware Minimization (SAM) has been proposed recently to improve model generalization ability. However, SAM calculates the gradient twice in each optimization step, thereby doubling the computation costs compared to stochastic…

Computer Vision and Pattern Recognition · Computer Science 2024-03-15 Jiaxin Deng , Junbiao Pang , Baochang Zhang , Tian Wang

Understanding the internal representations of large language models is crucial for ensuring their reliability and safety, with sparse autoencoders (SAEs) emerging as a promising interpretability approach. However, current SAE training…

Machine Learning · Computer Science 2025-10-13 T. Ed Li , Junyu Ren

Not being able to understand and predict the behavior of deep learning systems makes it hard to decide what architecture and algorithm to use for a given problem. In science and engineering, modeling is a methodology used to understand…

Machine Learning · Computer Science 2023-09-15 Michael Y. Li , Erin Grant , Thomas L. Griffiths

Various gradient compression schemes have been proposed to mitigate the communication cost in distributed training of large scale machine learning models. Sign-based methods, such as signSGD, have recently been gaining popularity because of…

Optimization and Control · Mathematics 2021-06-25 Mher Safaryan , Peter Richtárik

Image-level weakly supervised semantic segmentation (WSSS) relies on class activation maps (CAMs) for pseudo labels generation. As CAMs only highlight the most discriminative regions of objects, the generated pseudo labels are usually…

Computer Vision and Pattern Recognition · Computer Science 2021-10-28 Weixuan Sun , Jing Zhang , Nick Barnes

This work proposes a semantic segmentation network that produces high-quality uncertainty estimates in a single forward pass. We exploit general representations from foundation models and unlabelled datasets through a Masked Image Modeling…

Computer Vision and Pattern Recognition · Computer Science 2024-02-28 David S. W. Williams , Matthew Gadd , Paul Newman , Daniele De Martini

Neural network based generative models with discriminative components are a powerful approach for semi-supervised learning. However, these techniques a) cannot account for model uncertainty in the estimation of the model's discriminative…

Machine Learning · Statistics 2017-06-30 Jonathan Gordon , José Miguel Hernández-Lobato

Gaussian processes offer a flexible kernel method for regression. While Gaussian processes have many useful theoretical properties and have proven practically useful, they suffer from poor scaling in the number of observations. In…

Machine Learning · Statistics 2021-08-26 Nick Terry , Youngjun Choe

Stochastic kinetic models are ubiquitous in physics, yet inferring their parameters from experimental data remains challenging. In deterministic models, parameter inference often relies on gradients, as they can be obtained efficiently…

Computational Physics · Physics 2026-04-03 Ludwig Burger , Annalena Kofler , Lukas Heinrich , Ulrich Gerland

This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. Numerous renowned algorithms for tackling the compressed sensing problem…

Information Theory · Computer Science 2026-03-11 Xu Zhu , Yufei Ma , Xiaoguang Li , Tiejun Li

Continual learning in neural networks aims to learn new tasks without forgetting old tasks. Sequential function-space variational inference (SFSVI) uses a Gaussian variational distribution to approximate the distribution of the outputs of…

Machine Learning · Computer Science 2025-05-28 Menghao Waiyan William Zhu , Pengcheng Hao , Ercan Engin Kuruoğlu

We propose a simple method that combines neural networks and Gaussian processes. The proposed method can estimate the uncertainty of outputs and flexibly adjust target functions where training data exist, which are advantages of Gaussian…

Machine Learning · Statistics 2017-07-20 Tomoharu Iwata , Zoubin Ghahramani

Level set estimation (LSE) is the problem of identifying regions where an unknown function takes values above or below a specified threshold. Active sampling strategies for efficient LSE have primarily been studied in continuous-valued…

Machine Learning · Statistics 2022-03-21 Benjamin Letham , Phillip Guan , Chase Tymms , Eytan Bakshy , Michael Shvartsman

We develop a Bayesian inference method for discretely-observed stochastic differential equations (SDEs). Inference is challenging for most SDEs, due to the analytical intractability of the likelihood function. Nevertheless, forward…

Methodology · Statistics 2024-11-08 Petar Jovanovski , Andrew Golightly , Umberto Picchini

Deep learning architectures have made significant progress in terms of performance in many research areas. The automatic speech recognition (ASR) field has thus benefited from these scientific and technological advances, particularly for…

Sound · Computer Science 2024-03-01 Quentin Raymondaud , Mickael Rouvier , Richard Dufour

We address the problem of learning an unknown smooth function and its derivatives from noisy pointwise evaluations under the supremum norm. While classical nonparametric regression provides a strong theoretical foundation, traditional…

Machine Learning · Computer Science 2026-03-10 Davide Maran , Marcello Restelli
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