Search Results for author:
Found 12 papers, 7 papers with code
Learning Dexterous In-Hand Manipulation
We use reinforcement learning (RL) to learn dexterous in-hand manipulation policies which can perform vision-based object reorientation on a physical Shadow Dexterous Hand.
Learning to Generate Reviews and Discovering Sentiment
We explore the properties of byte-level recurrent language models.
Ranked #9 on
Subjectivity Analysis
on SUBJ
Revisiting Distributed Synchronous SGD
Distributed training of deep learning models on large-scale training data is typically conducted with asynchronous stochastic optimization to maximize the rate of updates, at the cost of additional noise introduced from asynchrony.
Improved Variational Inference with Inverse Autoregressive Flow
The framework of normalizing flows provides a general strategy for flexible variational inference of posteriors over latent variables.
Ranked #44 on
Image Generation
on CIFAR-10
(bits/dimension metric)
LFADS - Latent Factor Analysis via Dynamical Systems
Neuroscience is experiencing a data revolution in which many hundreds or thousands of neurons are recorded simultaneously.
Improving Variational Inference with Inverse Autoregressive Flow
The framework of normalizing flows provides a general strategy for flexible variational inference of posteriors over latent variables.
Revisiting Distributed Synchronous SGD
Distributed training of deep learning models on large-scale training data is typically conducted with asynchronous stochastic optimization to maximize the rate of updates, at the cost of additional noise introduced from asynchrony.
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
TensorFlow is an interface for expressing machine learning algorithms, and an implementation for executing such algorithms.
Exploring the Limits of Language Modeling
In this work we explore recent advances in Recurrent Neural Networks for large scale Language Modeling, a task central to language understanding.
Ranked #10 on
Language Modelling
on One Billion Word
Generating Sentences from a Continuous Space
The standard recurrent neural network language model (RNNLM) generates sentences one word at a time and does not work from an explicit global sentence representation.
Towards Principled Unsupervised Learning
Supervised learning is successful because it can be solved by the minimization of the training error cost function.
Fast optimization of Multithreshold Entropy Linear Classifier
Multithreshold Entropy Linear Classifier (MELC) is a density based model which searches for a linear projection maximizing the Cauchy-Schwarz Divergence of dataset kernel density estimation.
