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Found 17 papers, 12 papers with code
GraphCast: Learning skillful medium-range global weather forecasting
Global medium-range weather forecasting is critical to decision-making across many social and economic domains.
MultiScale MeshGraphNets
In recent years, there has been a growing interest in using machine learning to overcome the high cost of numerical simulation, with some learned models achieving impressive speed-ups over classical solvers whilst maintaining accuracy.
Normalizing flows for atomic solids
We present a machine-learning approach, based on normalizing flows, for modelling atomic solids.
Highly accurate protein structure prediction with AlphaFold
Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics.
Never Give Up: Learning Directed Exploration Strategies
Our method doubles the performance of the base agent in all hard exploration in the Atari-57 suite while maintaining a very high score across the remaining games, obtaining a median human normalised score of 1344. 0%.
Ranked #7 on
Atari Games
on atari game
Targeted free energy estimation via learned mappings
Here, we cast Targeted FEP as a machine learning problem in which the mapping is parameterized as a neural network that is optimized so as to increase overlap.
Fast deep reinforcement learning using online adjustments from the past
We propose Ephemeral Value Adjusments (EVA): a means of allowing deep reinforcement learning agents to rapidly adapt to experience in their replay buffer.
Meta-Learning by the Baldwin Effect
The scope of the Baldwin effect was recently called into question by two papers that closely examined the seminal work of Hinton and Nowlan.
Generative Temporal Models with Spatial Memory for Partially Observed Environments
In model-based reinforcement learning, generative and temporal models of environments can be leveraged to boost agent performance, either by tuning the agent's representations during training or via use as part of an explicit planning mechanism.
Memory-based Parameter Adaptation
Deep neural networks have excelled on a wide range of problems, from vision to language and game playing.
DARLA: Improving Zero-Shot Transfer in Reinforcement Learning
Domain adaptation is an important open problem in deep reinforcement learning (RL).
Neural Episodic Control
Deep reinforcement learning methods attain super-human performance in a wide range of environments.
PathNet: Evolution Channels Gradient Descent in Super Neural Networks
It is a neural network algorithm that uses agents embedded in the neural network whose task is to discover which parts of the network to re-use for new tasks.
Ranked #5 on
Continual Learning
on F-CelebA (10 tasks)
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Deep neural networks (NNs) are powerful black box predictors that have recently achieved impressive performance on a wide spectrum of tasks.
Model-Free Episodic Control
State of the art deep reinforcement learning algorithms take many millions of interactions to attain human-level performance.
Deep Exploration via Bootstrapped DQN
Efficient exploration in complex environments remains a major challenge for reinforcement learning.
Ranked #6 on
Atari Games
on Atari 2600 Breakout
Continuous control with deep reinforcement learning
We adapt the ideas underlying the success of Deep Q-Learning to the continuous action domain.
Ranked #3 on
Continuous Control
on Lunar Lander (OpenAI Gym)
