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Found 9 papers, 7 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.
Learning Mesh-Based Simulation with Graph Networks
Our model can be trained to pass messages on a mesh graph and to adapt the mesh discretization during forward simulation.
Generalization of Reinforcement Learners with Working and Episodic Memory
In this paper, we aim to develop a comprehensive methodology to test different kinds of memory in an agent and assess how well the agent can apply what it learns in training to a holdout set that differs from the training set along dimensions that we suggest are relevant for evaluating memory-specific generalization.
Interval timing in deep reinforcement learning agents
The measurement of time is central to intelligent behavior.
Revisiting Bayes by Backprop
We also empirically demonstrate how Bayesian RNNs are superior to traditional RNNs on a language modelling benchmark and an image captioning task, as well as showing how each of these methods improve our model over a variety of other schemes for training them.
Noisy Networks for Exploration
We introduce NoisyNet, a deep reinforcement learning agent with parametric noise added to its weights, and show that the induced stochasticity of the agent's policy can be used to aid efficient exploration.
Ranked #1 on
Atari Games
on Atari 2600 Surround
Bayesian Recurrent Neural Networks
We also empirically demonstrate how Bayesian RNNs are superior to traditional RNNs on a language modelling benchmark and an image captioning task, as well as showing how each of these methods improve our model over a variety of other schemes for training them.
Pointer Networks
It differs from the previous attention attempts in that, instead of using attention to blend hidden units of an encoder to a context vector at each decoder step, it uses attention as a pointer to select a member of the input sequence as the output.
Ranked #9 on
Point Cloud Completion
on ShapeNet
(using extra training data)
