5 citations · 8 across the 4 of their papers we have counts for
6 papers
Exponential Tilting of Generative Models: Improving Sample Quality by Training and Sampling from Latent Energy
Zhisheng Xiao, Qing Yan, Yali Amit
In this paper, we present a general method that can improve the sample quality of pre-trained likelihood based generative models. Our method constructs an energy function on the la…
A Method to Model Conditional Distributions with Normalizing Flows
Zhisheng Xiao, Qing Yan, Yali Amit
In this work, we investigate the use of normalizing flows to model conditional distributions. In particular, we use our proposed method to analyze inverse problems with invertible…
Generative Latent Flow
Zhisheng Xiao, Qing Yan, Yali Amit
In this work, we propose the Generative Latent Flow (GLF), an algorithm for generative modeling of the data distribution. GLF uses an Auto-encoder (AE) to learn latent representati…
Deep learning with asymmetric connections and Hebbian updates
Yali Amit
We show that deep networks can be trained using Hebbian updates yielding similar performance to ordinary back-propagation on challenging image datasets. To overcome the unrealistic…
Deformable Classifiers
Jiajun Shen, Yali Amit
Geometric variations of objects, which do not modify the object class, pose a major challenge for object recognition. These variations could be rigid as well as non-rigid transform…
Dynamic Partition Models
Marc Goessling, Yali Amit
We present a new approach for learning compact and intuitive distributed representations with binary encoding. Rather than summing up expert votes as in products of experts, we emp…