312 citations · 1.2k across the 54 of their papers we have counts for
5 papers · 1 filter
On the Identifiability of Causal Abstractions
Xiusi Li, Sékou-Oumar Kaba, Siamak Ravanbakhsh
Causal representation learning (CRL) enhances machine learning models' robustness and generalizability by learning structural causal models associated with data-generating processe…
Deep Models of Interactions Across Sets
Jason Hartford, Devon R Graham, Kevin Leyton-Brown +1
We use deep learning to model interactions across two or more sets of objects, such as user-movie ratings, protein-drug bindings, or ternary user-item-tag interactions. The canonic…
Equivariance Through Parameter-Sharing
Siamak Ravanbakhsh, Jeff Schneider, Barnabas Poczos
We propose to study equivariance in deep neural networks through parameter symmetries. In particular, given a group that acts discretely on the input and output of a…
Annealing Gaussian into ReLU: a New Sampling Strategy for Leaky-ReLU RBM
Chun-Liang Li, Siamak Ravanbakhsh, Barnabas Poczos
Restricted Boltzmann Machine (RBM) is a bipartite graphical model that is used as the building block in energy-based deep generative models. Due to numerical stability and quantifi…
Stochastic Neural Networks with Monotonic Activation Functions
Siamak Ravanbakhsh, Barnabas Poczos, Jeff Schneider +2
We propose a Laplace approximation that creates a stochastic unit from any smooth monotonic activation function, using only Gaussian noise. This paper investigates the application…