1 code implementation • 30 Oct 2022 • Shengbang Tong, Xili Dai, Yubei Chen, Mingyang Li, Zengyi Li, Brent Yi, Yann Lecun, Yi Ma
This paper proposes an unsupervised method for learning a unified representation that serves both discriminative and generative purposes.
no code implementations • 17 Jun 2022 • Yubei Chen, Adrien Bardes, Zengyi Li, Yann Lecun
Even with 32x32 patch representation, BagSSL achieves 62% top-1 linear probing accuracy on ImageNet.
1 code implementation • 24 Jan 2022 • Zengyi Li, Yubei Chen, Yann Lecun, Friedrich T. Sommer
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.
1 code implementation • 7 Oct 2020 • Zengyi Li, Yubei Chen, Friedrich T. Sommer
However, in the continuous case, unfavorable geometry of the target distribution can greatly limit the efficiency of MCMC methods.
no code implementations • 4 May 2020 • Zengyi Li, Friedrich T. Sommer
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).
2 code implementations • 17 Oct 2019 • Zengyi Li, Yubei Chen, Friedrich T. Sommer
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.
no code implementations • 25 Sep 2019 • Zengyi Li, Yubei Chen, Friedrich T. Sommer
Energy based models outputs unmormalized log-probability values given datasamples.