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Learning representations that generalize to novel compositions of known concepts is crucial for bridging the gap between human and machine perception.
The Hungarian method for the assignment problem
H. W. Kuhn · 1955
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Connectionism and cognitive architecture: A critical analysis
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Nonlinear ICA of temporally dependent stationary sources
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Understanding disentangling in β \beta -vae, 2018
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MONet: Unsupervised Scene Decomposition and Representation, January 2019
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Nonlinear ICA using auxiliary variables and generalized contrastive learning
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The incomplete rosetta stone problem: Identifiability results for multi-view nonlinear ICA
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2019
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Pytorch: An imperative style, high-performance deep learning library
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On the binding problem in artificial neural networks
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Space: Unsupervised object-oriented scene representation via spatial attention and decomposition
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Contrastive learning of structured world models
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Ilyes Khemakhem, Ricardo Pio Monti, Diederik P. Kingma, and Aapo Hyvärinen · 2020
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Weakly supervised disentanglement with guarantees
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Visual representation learning does not generalize strongly within the same domain
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Function classes for identifiable nonlinear independent component analysis
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Replay and compositional computation
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Disentanglement via mechanism sparsity regularization: A new principle for nonlinear ica
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