57 citations · 405 across the 34 of their papers we have counts for
25 papers · 1 filter
PathGuide: Dynamic Classifier-Free Guidance via On-Policy Transport Alignment
Avishag Nevo, Tamir Hazan
While modern generative models excel at modeling complex data, precise inference-time control in conditional generation remains a critical challenge. Classifier-free guidance (CFG)…
Layer Collaboration in the Forward-Forward Algorithm
Guy Lorberbom, Itai Gat, Yossi Adi +2
Backpropagation, which uses the chain rule, is the de-facto standard algorithm for optimizing neural networks nowadays. Recently, Hinton (2022) proposed the forward-forward algorit…
On the Importance of Gradient Norm in PAC-Bayesian Bounds
Itai Gat, Yossi Adi, Alexander Schwing +1
Generalization bounds which assess the difference between the true risk and the empirical risk, have been studied extensively. However, to obtain bounds, current techniques use str…
A Functional Information Perspective on Model Interpretation
Itai Gat, Nitay Calderon, Roi Reichart +1
Contemporary predictive models are hard to interpret as their deep nets exploit numerous complex relations between input elements. This work suggests a theoretical framework for mo…
Learning Discrete Structured Variational Auto-Encoder using Natural Evolution Strategies
Alon Berliner, Guy Rotman, Yossi Adi +2
Discrete variational auto-encoders (VAEs) are able to represent semantic latent spaces in generative learning. In many real-life settings, the discrete latent space consists of hig…
Latent Space Explanation by Intervention
Itai Gat, Guy Lorberbom, Idan Schwartz +1
The success of deep neural nets heavily relies on their ability to encode complex relations between their input and their output. While this property serves to fit the training dat…