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Vision-based localization of an agent in a map is an important problem in robotics and computer vision. In that context, localization by learning matchable image features is gaining popularity due to recent advances in machine learning.…

Computer Vision and Pattern Recognition · Computer Science 2020-03-24 Janine Thoma , Danda Pani Paudel , Ajad Chhatkuli , Luc Van Gool

In domains like bioinformatics, information retrieval and social network analysis, one can find learning tasks where the goal consists of inferring a ranking of objects, conditioned on a particular target object. We present a general kernel…

Machine Learning · Computer Science 2013-06-11 Tapio Pahikkala , Antti Airola , Michiel Stock , Bernard De Baets , Willem Waegeman

A key goal of unsupervised learning is to go beyond density estimation and sample generation to reveal the structure inherent within observed data. Such structure can be expressed in the pattern of interactions between explanatory latent…

Machine Learning · Computer Science 2022-11-11 Changmin Yu , Hugo Soulat , Neil Burgess , Maneesh Sahani

Relational representation learning transforms relational data into continuous and low-dimensional vector representations. However, vector-based representations fall short in capturing crucial properties of relational data that are complex…

Machine Learning · Computer Science 2024-09-25 Bo Xiong

Real world systems typically feature a variety of different dependency types and topologies that complicate model selection for probabilistic graphical models. We introduce the ensemble-of-forests model, a generalization of the…

Machine Learning · Statistics 2013-12-18 Eirini Arvaniti , Manfred Claassen

In recent years, significant attention in deep learning theory has been devoted to analyzing when models that interpolate their training data can still generalize well to unseen examples. Many insights have been gained from studying models…

Machine Learning · Statistics 2023-10-24 Jacob A. Zavatone-Veth , Cengiz Pehlevan

Relational machine learning studies methods for the statistical analysis of relational, or graph-structured, data. In this paper, we provide a review of how such statistical models can be "trained" on large knowledge graphs, and then used…

Machine Learning · Statistics 2016-11-18 Maximilian Nickel , Kevin Murphy , Volker Tresp , Evgeniy Gabrilovich

We consider the problem of computing dense correspondences between non-rigid shapes with potentially significant partiality. Existing formulations tackle this problem through heavy manifold optimization in the spectral domain, given…

Computer Vision and Pattern Recognition · Computer Science 2023-03-28 Souhaib Attaiki , Gautam Pai , Maks Ovsjanikov

We propose a non-parametric regression methodology, Random Forests on Distance Matrices (RFDM), for detecting genetic variants associated to quantitative phenotypes representing the human brain's structure or function, and obtained using…

Machine Learning · Statistics 2013-09-25 Aaron Sim , Dimosthenis Tsagkrasoulis , Giovanni Montana

Nearest neighbor (kNN) methods have been gaining popularity in recent years in light of advances in hardware and efficiency of algorithms. There is a plethora of methods to choose from today, each with their own advantages and…

Machine Learning · Computer Science 2017-03-01 Daniel Zoran , Balaji Lakshminarayanan , Charles Blundell

In this article, we extend the conventional framework of convolutional-Restricted-Boltzmann-Machine to learn highly abstract features among abitrary number of time related input maps by constructing a layer of multiplicative units, which…

Artificial Intelligence · Computer Science 2017-06-27 Zizhuang Wang

This paper studies how to capture dependency graph structures from real data which may not be Gaussian. Starting from marginal loss functions not necessarily derived from probability distributions, we utilize an additive…

Machine Learning · Statistics 2019-12-03 Yiyuan She , Shao Tang , Qiaoya Zhang

We study the model-checking problem for first- and monadic second-order logic on finite relational structures. The problem of verifying whether a formula of these logics is true on a given structure is considered intractable in general, but…

In domains where transparency and trustworthiness are crucial, such as healthcare, rule-based systems are widely used and often preferred over black-box models for decision support systems due to their inherent interpretability. However, as…

Machine Learning · Computer Science 2025-06-18 Christel Sirocchi , Damiano Verda

In this paper we examine a novel addition to the known methods for learning Bayesian networks from data that improves the quality of the learned networks. Our approach explicitly represents and learns the local structure in the conditional…

Artificial Intelligence · Computer Science 2013-02-18 Nir Friedman , Moises Goldszmidt

An important characteristic of neural networks is their ability to learn representations of the input data with effective features for prediction, which is believed to be a key factor to their superior empirical performance. To better…

Machine Learning · Computer Science 2022-06-06 Zhenmei Shi , Junyi Wei , Yingyu Liang

Graph neural networks use relational information as an inductive bias to enhance prediction performance. Not rarely, task-relevant relations are unknown and graph structure learning approaches have been proposed to learn them from data.…

Machine Learning · Computer Science 2025-05-29 Alessandro Manenti , Daniele Zambon , Cesare Alippi

Representation learning, and interpreting learned representations, are key areas of focus in machine learning and neuroscience. Both fields generally use representations as a means to understand or improve a system's computations. In this…

Machine Learning · Computer Science 2024-09-24 Andrew Kyle Lampinen , Stephanie C. Y. Chan , Katherine Hermann

Deep neural networks have gained tremendous success in a broad range of machine learning tasks due to its remarkable capability to learn semantic-rich features from high-dimensional data. However, they often require large-scale labelled…

Computer Vision and Pattern Recognition · Computer Science 2020-07-21 Hu Wang , Guansong Pang , Chunhua Shen , Congbo Ma

Heralded by the initial success in speech recognition and image classification, learning-based approaches with neural networks, commonly referred to as deep learning, have spread across various fields. A primitive form of a neural network…

Robotics · Computer Science 2024-09-02 Takuma Yoneda
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