Search Results for author:
Found 13 papers, 7 papers with code
Acme: A Research Framework for Distributed Reinforcement Learning
These implementations serve both as a validation of our design decisions as well as an important contribution to reproducibility in RL research.
Modular Meta-Learning with Shrinkage
Many real-world problems, including multi-speaker text-to-speech synthesis, can greatly benefit from the ability to meta-learn large models with only a few task-specific components.
Large-Scale Visual Speech Recognition
To achieve this, we constructed the largest existing visual speech recognition dataset, consisting of pairs of text and video clips of faces speaking (3, 886 hours of video).
Ranked #10 on
Lipreading
on LRS3-TED
(using extra training data)
Distributed Distributional Deterministic Policy Gradients
This work adopts the very successful distributional perspective on reinforcement learning and adapts it to the continuous control setting.
The Intentional Unintentional Agent: Learning to Solve Many Continuous Control Tasks Simultaneously
This paper introduces the Intentional Unintentional (IU) agent.
Learned Optimizers that Scale and Generalize
Two of the primary barriers to its adoption are an inability to scale to larger problems and a limited ability to generalize to new tasks.
Learning to Learn without Gradient Descent by Gradient Descent
We learn recurrent neural network optimizers trained on simple synthetic functions by gradient descent.
Learning to learn by gradient descent by gradient descent
The move from hand-designed features to learned features in machine learning has been wildly successful.
A General Framework for Constrained Bayesian Optimization using Information-based Search
Of particular interest to us is to efficiently solve problems with decoupled constraints, in which subsets of the objective and constraint functions may be evaluated independently.
Predictive Entropy Search for Bayesian Optimization with Unknown Constraints
Unknown constraints arise in many types of expensive black-box optimization problems.
An Entropy Search Portfolio for Bayesian Optimization
How- ever, the performance of a Bayesian optimization method very much depends on its exploration strategy, i. e. the choice of acquisition function, and it is not clear a priori which choice will result in superior performance.
Predictive Entropy Search for Efficient Global Optimization of Black-box Functions
We propose a novel information-theoretic approach for Bayesian optimization called Predictive Entropy Search (PES).
Exploiting correlation and budget constraints in Bayesian multi-armed bandit optimization
This problem is also known as fixed-budget best arm identification in the multi-armed bandit literature.
