activity
20122022
most citedDeep Learning with Sets and Point Clouds

22 citations · 33 across the 6 of their papers we have counts for

collaborators

6 papers

cs.AI20222 cited

Utility Theory for Sequential Decision Making

Mehran Shakerinava, Siamak Ravanbakhsh

The von Neumann-Morgenstern (VNM) utility theorem shows that under certain axioms of rationality, decision-making is reduced to maximizing the expectation of some utility function.…

stat.ML20165 cited

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…

stat.ML201622 cited

Deep Learning with Sets and Point Clouds

Siamak Ravanbakhsh, Jeff Schneider, Barnabas Poczos

We introduce a simple permutation equivariant layer for deep learning with set structure.This type of layer, obtained by parameter-sharing, has a simple implementation and linear-t…

cs.AI2014

Revisiting Algebra and Complexity of Inference in Graphical Models

Siamak Ravanbakhsh, Russell Greiner

This paper studies the form and complexity of inference in graphical models using the abstraction offered by algebraic structures. In particular, we broadly formalize inference pro…

cs.NE20142 cited

Training Restricted Boltzmann Machine by Perturbation

Siamak Ravanbakhsh, Russell Greiner, Brendan Frey

A new approach to maximum likelihood learning of discrete graphical models and RBM in particular is introduced. Our method, Perturb and Descend (PD) is inspired by two ideas (I) pe…

cs.AI20122 cited

A Generalized Loop Correction Method for Approximate Inference in Graphical Models

Siamak Ravanbakhsh, Chun-Nam Yu, Russell Greiner

Belief Propagation (BP) is one of the most popular methods for inference in probabilistic graphical models. BP is guaranteed to return the correct answer for tree structures, but c…