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Found 7 papers, 4 papers with code
Unsupervised Learning of Structured Representations via Closed-Loop Transcription
This paper proposes an unsupervised method for learning a unified representation that serves both discriminative and generative purposes.
Bag of Image Patch Embedding Behind the Success of Self-Supervised Learning
Even with 32x32 patch representation, BagSSL achieves 62% top-1 linear probing accuracy on ImageNet.
Neural Manifold Clustering and Embedding
We argue that achieving manifold clustering with neural networks requires two essential ingredients: a domain-specific constraint that ensures the identification of the manifolds, and a learning algorithm for embedding each manifold to a linear subspace in the feature space.
A Neural Network MCMC sampler that maximizes Proposal Entropy
However, in the continuous case, unfavorable geometry of the target distribution can greatly limit the efficiency of MCMC methods.
Complex Amplitude-Phase Boltzmann Machines
We extend the framework of Boltzmann machines to a network of complex-valued neurons with variable amplitudes, referred to as Complex Amplitude-Phase Boltzmann machine (CAP-BM).
Learning Energy-Based Models in High-Dimensional Spaces with Multi-scale Denoising Score Matching
Recently, \citet{song2019generative} have shown that a generative model trained by denoising score matching accomplishes excellent sample synthesis, when trained with data samples corrupted with multiple levels of noise.
Annealed Denoising score matching: learning Energy based model in high-dimensional spaces
Energy based models outputs unmormalized log-probability values given datasamples.
