2 citations · 2 across the 3 of their papers we have counts for
6 papers
SentenceMIM: A Latent Variable Language Model
Micha Livne, Kevin Swersky, David J. Fleet
SentenceMIM is a probabilistic auto-encoder for language data, trained with Mutual Information Machine (MIM) learning to provide a fixed length representation of variable length la…
High Mutual Information in Representation Learning with Symmetric Variational Inference
Micha Livne, Kevin Swersky, David J. Fleet
We introduce the Mutual Information Machine (MIM), a novel formulation of representation learning, using a joint distribution over the observations and latent state in an encoder/d…
MIM: Mutual Information Machine
Micha Livne, Kevin Swersky, David J. Fleet
We introduce the Mutual Information Machine (MIM), a probabilistic auto-encoder for learning joint distributions over observations and latent variables. MIM reflects three design p…
TzK: Flow-Based Conditional Generative Model
Micha Livne, David Fleet
We formulate a new class of conditional generative models based on probability flows. Trained with maximum likelihood, it provides efficient inference and sampling from class-condi…
Walking on Thin Air: Environment-Free Physics-based Markerless Motion Capture
Micha Livne, Leonid Sigal, Marcus A. Brubaker +1
We propose a generative approach to physics-based motion capture. Unlike prior attempts to incorporate physics into tracking that assume the subject and scene geometry are calibrat…
TzK Flow - Conditional Generative Model
Micha Livne, David J. Fleet
We introduce TzK (pronounced "task"), a conditional probability flow-based model that exploits attributes (e.g., style, class membership, or other side information) in order to lea…