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Demonstration of quantum advantage for classical machine learning tasks remains a central goal for quantum technologies and artificial intelligence. Two major bottlenecks to this goal are the high dimensionality of practical datasets and…

Quantum Physics · Physics 2025-10-16 Mohammad Sharifian , Abolfazl Bayat

Large-scale supervised classification algorithms, especially those based on deep convolutional neural networks (DCNNs), require vast amounts of training data to achieve state-of-the-art performance. Decreasing this data requirement would…

Computer Vision and Pattern Recognition · Computer Science 2016-06-15 Maya Kabkab , Azadeh Alavi , Rama Chellappa

We introduce a new method for training deep Boltzmann machines jointly. Prior methods of training DBMs require an initial learning pass that trains the model greedily, one layer at a time, or do not perform well on classification tasks. In…

Machine Learning · Statistics 2013-05-02 Ian J. Goodfellow , Aaron Courville , Yoshua Bengio

We introduce Boolformer, a Transformer-based model trained to perform end-to-end symbolic regression of Boolean functions. First, we show that it can predict compact formulas for complex functions not seen during training, given their full…

Machine Learning · Computer Science 2025-07-18 Stéphane d'Ascoli , Arthur Renard , Vassilis Papadopoulos , Samy Bengio , Josh Susskind , Emmanuel Abbé

Physics increasingly uses Bayesian techniques for systematic data analysis and model-to-data comparison. This paper describes how these methods can be implemented to answer questions of relevance to teaching laboratories. It demonstrates…

Physics Education · Physics 2022-07-21 Matthew Heffernan

We call a learner super-teachable if a teacher can trim down an iid training set while making the learner learn even better. We provide sharp super-teaching guarantees on two learners: the maximum likelihood estimator for the mean of a…

Machine Learning · Statistics 2018-02-27 Yuzhe Ma , Robert Nowak , Philippe Rigollet , Xuezhou Zhang , Xiaojin Zhu

Continual learning aims to enable a single model to learn a sequence of tasks without catastrophic forgetting. Top-performing methods usually require a rehearsal buffer to store past pristine examples for experience replay, which, however,…

We present a method to simplify expressions in the context of an equational theory. The basic ideas and concepts of the method have been presented previously elsewhere but here we tackle the difficult task of making it efficient in…

Logic in Computer Science · Computer Science 2020-03-16 Baudouin Le Charlier

This chapter introduces the main ideas and the most important methods for representing the electronic wavefunction through machine learning models. The wavefunction of a N-electron system is an incredibly complicated mathematical object,…

Chemical Physics · Physics 2024-04-30 Stefano Battaglia

In order for a robot to be a generalist that can perform a wide range of jobs, it must be able to acquire a wide variety of skills quickly and efficiently in complex unstructured environments. High-capacity models such as deep neural…

Machine Learning · Computer Science 2017-09-15 Chelsea Finn , Tianhe Yu , Tianhao Zhang , Pieter Abbeel , Sergey Levine

We establish a regular sampling theory in the range of the analysis operator of a continuous frame having a unitary structure. The unitary structure is related with a unitary representation of a locally compact abelian group on a separable…

Functional Analysis · Mathematics 2020-11-11 Antonio G. García

Curriculum Learning for Reinforcement Learning is an increasingly popular technique that involves training an agent on a sequence of intermediate tasks, called a Curriculum, to increase the agent's performance and learning speed. This paper…

Machine Learning · Computer Science 2021-11-02 Andrea Bassich , Francesco Foglino , Matteo Leonetti , Daniel Kudenko

We present some variations affecting the association measure and thresholding on a technique for learning Selectional Restrictions from on-line corpora. It uses a wide-coverage noun taxonomy and a statistical measure to generalize the…

cmp-lg · Computer Science 2016-08-31 Francesc Ribas

The circuits framework in mechanistic interpretability aims to identify causally important sparse subgraphs of model components, typically evaluated by measuring necessity and sufficiency. We measure circuit reuse, the proportion of…

Computation and Language · Computer Science 2026-05-12 Michael Li , Nishant Subramani

We derive a family of loss functions to train models in the presence of sampling bias. Examples are when the prevalence of a pathology differs from its sampling rate in the training dataset, or when a machine learning practioner rebalances…

Strategic classification, i.e. classification under possible strategic manipulations of features, has received a lot of attention from both the machine learning and the game theory community. Most works focus on analysing properties of the…

Machine Learning · Computer Science 2022-03-28 Tosca Lechner , Ruth Urner

This paper provides a general framework to study the effect of sampling properties of training data on the generalization error of the learned machine learning (ML) models. Specifically, we propose a new spectral analysis of the…

Machine Learning · Computer Science 2019-06-11 Bhavya Kailkhura , Jayaraman J. Thiagarajan , Qunwei Li , Peer-Timo Bremer

A robot deployed in a home over long stretches of time faces a true lifelong learning problem. As it seeks to provide assistance to its users, the robot should leverage any accumulated experience to improve its own knowledge and…

Robotics · Computer Science 2023-11-07 Jorge Mendez-Mendez , Leslie Pack Kaelbling , Tomás Lozano-Pérez

Continual learning aims to learn continuously from a stream of tasks and data in an online-learning fashion, being capable of exploiting what was learned previously to improve current and future tasks while still being able to perform well…

Machine Learning · Computer Science 2020-07-31 Quang Pham , Doyen Sahoo , Chenghao Liu , Steven C. H Hoi

Beyond the great cognitive powers showcased by language models, it is crucial to scrutinize whether their reasoning capabilities stem from strong generalization or merely exposure to relevant data. As opposed to constructing increasingly…

Computation and Language · Computer Science 2024-01-02 Hongqiu Wu , Linfeng Liu , Hai Zhao , Min Zhang
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