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The theme of the first two sections, is to prepare the framework of how from a ``complicated'' family of so called index models $I \in K_1$ we build many and/or complicated structures in a class $K_2$. The index models are…

Logic · Mathematics 2023-05-19 Saharon Shelah

This paper argues that training GANs on local and non-local dependencies in speech data offers insights into how deep neural networks discretize continuous data and how symbolic-like rule-based morphophonological processes emerge in a deep…

Computation and Language · Computer Science 2021-09-14 Gašper Beguš

Food is significant to human daily life. In this paper, we are interested in learning structural representations for lengthy recipes, that can benefit the recipe generation and food cross-modal retrieval tasks. Different from the common…

Computer Vision and Pattern Recognition · Computer Science 2022-08-02 Hao Wang , Guosheng Lin , Steven C. H. Hoi , Chunyan Miao

The graph structure is a commonly used data storage mode, and it turns out that the low-dimensional embedded representation of nodes in the graph is extremely useful in various typical tasks, such as node classification, link prediction ,…

Social and Information Networks · Computer Science 2020-08-03 Xing Li , Wei Wei , Xiangnan Feng , Xue Liu , Zhiming Zheng

Machine learning systems often do not share the same inductive biases as humans and, as a result, extrapolate or generalize in ways that are inconsistent with our expectations. The trade-off between exemplar- and rule-based generalization…

Machine Learning · Computer Science 2022-06-20 Ishita Dasgupta , Erin Grant , Thomas L. Griffiths

We propose a novel approach to enhance the discriminability of Convolutional Neural Networks (CNN). The key idea is to build a tree structure that could progressively learn fine-grained features to distinguish a subset of classes, by…

Computer Vision and Pattern Recognition · Computer Science 2017-09-25 Zhenhua Wang , Xingxing Wang , Gang Wang

We present Neural Random Forest Imitation - a novel approach for transforming random forests into neural networks. Existing methods propose a direct mapping and produce very inefficient architectures. In this work, we introduce an imitation…

Machine Learning · Computer Science 2024-04-05 Christoph Reinders , Bodo Rosenhahn

Endowing robots with human-like physical reasoning abilities remains challenging. We argue that existing methods often disregard spatio-temporal relations and by using Graph Neural Networks (GNNs) that incorporate a relational inductive…

Machine Learning · Computer Science 2019-10-24 Fabio Ferreira , Lin Shao , Tamim Asfour , Jeannette Bohg

We propose a novel methodology, forest floor, to visualize and interpret random forest (RF) models. RF is a popular and useful tool for non-linear multi-variate classification and regression, which yields a good trade-off between robustness…

Machine Learning · Statistics 2016-07-05 Soeren H. Welling , Hanne H. F. Refsgaard , Per B. Brockhoff , Line H. Clemmensen

The nearest neighbor rule is a classic yet essential classification model, particularly in problems where the supervising information is given by pairwise dissimilarities and the embedding function are not easily obtained. Prototype…

Machine Learning · Computer Science 2018-01-09 Shin Ando

How do neural language models acquire a language's structure when trained for next-token prediction? We address this question by deriving theoretical scaling laws for neural network performance on synthetic datasets generated by the Random…

Machine Learning · Computer Science 2025-05-13 Francesco Cagnetta , Alessandro Favero , Antonio Sclocchi , Matthieu Wyart

We leverage probabilistic models of neural representations to investigate how residual networks fit classes. To this end, we estimate class-conditional density models for representations learned by deep ResNets. We then use these models to…

Machine Learning · Computer Science 2022-12-02 Michał Jamroż , Marcin Kurdziel

We study the problem of learning multivariate dependencies in nonparametric and high-dimensional settings. This includes but is not limited to graphical models. Our approach effectively combines several features that are missing from…

Statistics Theory · Mathematics 2026-03-31 Arash A. Amini , Bryon Aragam , Qing Zhou

Federated learning allows clients to collaboratively learn statistical models while keeping their data local. Federated learning was originally used to train a unique global model to be served to all clients, but this approach might be…

Machine Learning · Computer Science 2022-06-20 Othmane Marfoq , Giovanni Neglia , Laetitia Kameni , Richard Vidal

Conditional Random Field (CRF) and recurrent neural models have achieved success in structured prediction. More recently, there is a marriage of CRF and recurrent neural models, so that we can gain from both non-linear dense features and…

Computation and Language · Computer Science 2016-11-15 Shuming Ma , Xu Sun

Feature Learning aims to extract relevant information contained in data sets in an automated fashion. It is driving force behind the current deep learning trend, a set of methods that have had widespread empirical success. What is lacking…

Machine Learning · Statistics 2015-04-02 Brendan van Rooyen , Robert C. Williamson

We propose a method for learning Markov network structures for continuous data without invoking any assumptions about the distribution of the variables. The method makes use of previous work on a non-parametric estimator for mutual…

Machine Learning · Computer Science 2017-08-09 Janne Leppä-aho , Santeri Räisänen , Xiao Yang , Teemu Roos

There has been increasing attention on planning model learning in classical planning. Most existing approaches, however, focus on learning planning models from structured data in symbolic representations. It is often difficult to obtain…

Machine Learning · Computer Science 2022-11-30 Kebing Jin , Zhanhao Xiao , Hankui Hankz Zhuo , Hai Wan , Jiaran Cai

Undirected graphical models are widely used to model the conditional independence structure of vector-valued data. However, in many modern applications, for example those involving EEG and fMRI data, observations are more appropriately…

Machine Learning · Statistics 2024-01-29 Boxin Zhao , Percy S. Zhai , Y. Samuel Wang , Mladen Kolar

A Random Graph is a random object which take its values in the space of graphs. We take advantage of the expressibility of graphs in order to model the uncertainty about the existence of causal relationships within a given set of variables.…

Artificial Intelligence · Computer Science 2026-04-30 Mauricio Gonzalez-Soto , Ivan R. Feliciano-Avelino , L. Enrique Sucar , Hugo J. Escalante Balderas