Ruslan Salakhutdinov

TL;DR: This article used the continuity of text from books to train an encoder-decoder model that tries to reconstruct the surrounding sentences of an encoded passage, which can produce highly generic sentence representations that are robust and perform well in practice.

TL;DR: A Deep Boltzmann Machine is proposed for learning a generative model of multimodal data and it is shown that the model can be used to create fused representations by combining features across modalities, which are useful for classification and information retrieval.

TL;DR: This work introduces the structure-content neural language model that disentangles the structure of a sentence to its content, conditioned on representations produced by the encoder, and shows that with linear encoders, the learned embedding space captures multimodal regularities in terms of vector space arithmetic.

TL;DR: This paper presents a fully Bayesian treatment of the Probabilistic Matrix Factorization (PMF) model in which model capacity is controlled automatically by integrating over all model parameters and hyperparameters and shows that Bayesian PMF models can be efficiently trained using Markov chain Monte Carlo methods by applying them to the Netflix dataset.

TL;DR: The main theorem characterizes the permutation invariant objective functions and provides a family of functions to which any permutation covariant objective function must belong, which enables the design of a deep network architecture that can operate on sets and which can be deployed on a variety of scenarios including both unsupervised and supervised learning tasks.