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Found 8 papers, 5 papers with code
Automating Rigid Origami Design
Rigid origami has shown potential in large diversity of practical applications.
Normalized Attention Without Probability Cage
Attention architectures are widely used; they recently gained renewed popularity with Transformers yielding a streak of state of the art results.
The Frechet Distance of training and test distribution predicts the generalization gap
Empirically across several data domains, we substantiate this viewpoint by showing that test performance correlates strongly with the distance in data distributions between training and test set.
On Identifiability in Transformers
We show that, for sequences longer than the attention head dimension, attention weights are not identifiable.
Attentive Multi-Task Deep Reinforcement Learning
Sharing knowledge between tasks is vital for efficient learning in a multi-task setting.
Learning Policies through Quantile Regression
Policy gradient based reinforcement learning algorithms coupled with neural networks have shown success in learning complex policies in the model free continuous action space control setting.
Using State Predictions for Value Regularization in Curiosity Driven Deep Reinforcement Learning
Learning in sparse reward settings remains a challenge in Reinforcement Learning, which is often addressed by using intrinsic rewards.
Teaching a Machine to Read Maps with Deep Reinforcement Learning
Localization and navigation is also an important problem in domains such as robotics, and has recently become a focus of the deep reinforcement learning community.
