Results 11 to 20 of about 2,210,762 (272)
Toward Causal Representation Learning [PDF]
Proceedings of the IEEE, 2021 ISSN:1558 ...
Bernhard Schölkopf +2 more
exaly +5 more sources
Exploratory State Representation Learning [PDF]
Frontiers in Robotics and AI, 2022 Not having access to compact and meaningful representations is known to significantly increase the complexity of reinforcement learning (RL). For this reason, it can be useful to perform state representation learning (SRL) before tackling RL tasks ...
Astrid Merckling +3 more
doaj +4 more sources
Diffusion-Based Causal Representation Learning
EntropyCausal reasoning can be considered a cornerstone of intelligent systems. Having access to an underlying causal graph comes with the promise of cause–effect estimation and the identification of efficient and safe interventions.
Amir Mohammad Karimi Mamaghan +4 more
doaj +7 more sources
Deep Multimodal Representation Learning: A Survey
IEEE Access, 2019 Multimodal representation learning, which aims to narrow the heterogeneity gap among different modalities, plays an indispensable role in the utilization of ubiquitous multimodal data.
Wenzhong Guo, Jianwen Wang, Shiping Wang
doaj +3 more sources
Distributed Variational Representation Learning [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021 The problem of distributed representation learning is one in which multiple sources of information X1,…, XK are processed separately so as to learn as much information as possible about some ground truth Y. We investigate this problem from information-theoretic grounds, through a generalization of Tishby's centralized Information Bottleneck (IB) method
Aguerri, Inaki Estella +1 more
openaire +3 more sources
Learning Overcomplete Representations [PDF]
Neural Computation, 2000 In an overcomplete basis, the number of basis vectors is greater than the dimensionality of the input, and the representation of an input is not a unique combination of basis vectors. Overcomplete representations have been advocated because they have greater robustness in the presence of noise, can be sparser, and can have greater flexibility in ...
M S, Lewicki, T J, Sejnowski
openaire +2 more sources
Molecular set representation learning
Nature Machine Intelligence, 2023 Computational representation of molecules can take many forms, including graphs, string-encodings of graphs, binary vectors, or learned embeddings in the form of real-valued vectors. These representations are then used in downstream classification and regression tasks using a wide range of machine-learning models.
Maria Boulougouri +2 more
openaire +1 more source
Relation-Guided Representation Learning [PDF]
Neural Networks, 2020 Appear in Neural ...
Zhao Kang +4 more
openaire +3 more sources
Learning representations from dendrograms [PDF]
Machine Learning, 2020 AbstractWe propose unsupervised representation learning and feature extraction from dendrograms. The commonly used Minimax distance measures correspond to building a dendrogram with single linkage criterion, with defining specific forms of a level function and a distance function over that. Therefore, we extend this method to arbitrary dendrograms.
Morteza Haghir Chehreghani +1 more
openaire +3 more sources
IEEE Access, 2021 Machine learning has been widely applied in the fields of biomedicine, computational biology, bioinformatics, image processing, and so on. The performance of machine learning methods mainly relies on feature representation that is the mapping from ...
Feifei Cui +5 more
doaj +1 more source