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Found 14 papers, 7 papers with code
Lie Point Symmetry Data Augmentation for Neural PDE Solvers
In this paper, we present a method, which can partially alleviate this problem, by improving neural PDE solver sample complexity -- Lie point symmetry data augmentation (LPSDA).
Geometric and Physical Quantities Improve E(3) Equivariant Message Passing
Including covariant information, such as position, force, velocity or spin is important in many tasks in computational physics and chemistry.
Boundary Graph Neural Networks for 3D Simulations
The abundance of data has given machine learning considerable momentum in natural sciences and engineering.
Learning 3D Granular Flow Simulations
Recently, the application of machine learning models has gained momentum in natural sciences and engineering, which is a natural fit due to the abundance of data in these fields.
Convergence Proof for Actor-Critic Methods Applied to PPO and RUDDER
We prove under commonly used assumptions the convergence of actor-critic reinforcement learning algorithms, which simultaneously learn a policy function, the actor, and a value function, the critic.
Cross-Domain Few-Shot Learning by Representation Fusion
On the few-shot datasets miniImagenet and tieredImagenet with small domain shifts, CHEF is competitive with state-of-the-art methods.
Align-RUDDER: Learning From Few Demonstrations by Reward Redistribution
Align-RUDDER outperforms competitors on complex artificial tasks with delayed reward and few demonstrations.
General Reinforcement Learning
Multiple Sequence Alignment
+1
Hopfield Networks is All You Need
The new update rule is equivalent to the attention mechanism used in transformers.
Modern Hopfield Networks and Attention for Immune Repertoire Classification
We show that the attention mechanism of transformer architectures is actually the update rule of modern Hopfield networks that can store exponentially many patterns.
Using LSTMs for climate change assessment studies on droughts and floods
Climate change affects occurrences of floods and droughts worldwide.
Quantum Optical Experiments Modeled by Long Short-Term Memory
In this work, we show that machine learning models can provide significant improvement over random search.
A GAN based solver of black-box inverse problems
We propose a GAN based approach to solve inverse problems which have non-differential or non-continuous forward relations.
RUDDER: Return Decomposition for Delayed Rewards
In MDPs the Q-values are equal to the expected immediate reward plus the expected future rewards.
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