On Spectral Clustering: Analysis and an algorithm
One line of analysis makes the link to spectral graph partitioning, in which the sec-ond eigenvector of a graph's Laplacian is used to define a semi-optimal cut. Here, the eigenvector is seen as a solving a relaxation of an NP-hard discrete graph parti ...
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Generative Adversarial Imitation Learning
proceedings.neurips.ccnetworks [8], a technique from the deep learning community that has led to recent successes in modeling distributions of natural images: our algorithm harnesses generative adversarial training to fit distributions of states and actions defining expert behavior. We test our algorithm in Section 6, where
Prototypical Networks for Few-shot Learning
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Bootstrap Your Own Latent A New Approach to Self ...
proceedings.neurips.ccmining strategies [14, 15] to retrieve the nega-tive pairs. In addition, their performance criti-cally depends on the choice of image augmenta- ... to prevent collapsing while preserving high performance. To prevent collapse, a straightforward solution …
Spatial Transformer Networks - NeurIPS
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Semi-supervised Learning with Deep Generative Models
proceedings.neurips.ccapproximately invariant to local perturbations along the manifold. The idea of manifold learning ... We show for the first time how variational inference can be brought to bear upon the prob- ... probabilities are formed by a non-linear transformation, with parameters , of a set of latent vari-ables z. This non-linear transformation is ...
Unsupervised Learning of Visual Features by Contrasting ...
proceedings.neurips.ccpseudo-labels to learn visual representations. This method scales to large uncurated dataset and can be used for pre-training of supervised networks [7]. However, their formulation is not principled and recently, Asano et al. [2] show how to cast the pseudo-label assignment problem as an instance of the optimal transport problem.
PyTorch: An Imperative Style, High-Performance Deep ...
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Visualizing the Loss Landscape of Neural Nets
proceedings.neurips.cctask that is hard in theory, but sometimes easy in practice. Despite the NP-hardness of training general neural loss functions [3], simple gradient methods often find global minimizers (parameter configurations with zero or near-zero training loss), even when data and labels are randomized before training [43].
InfoGAN: Interpretable Representation Learning by ...
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