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Exploring Novel Quality Diversity Methods For Generalization in Reinforcement Learning
The Reinforcement Learning field is strong on achievements and weak on reapplication; a computer playing GO at a super-human level is still terrible at Tic-Tac-Toe.
Graph Harmony: Denoising and Nuclear-Norm Wasserstein Adaptation for Enhanced Domain Transfer in Graph-Structured Data
To navigate these obstacles, we develop the Denoising and Nuclear-Norm Wasserstein Adaptation Network (DNAN).
Conditional Seq2Seq model for the time-dependent two-level system
We apply the deep learning neural network architecture to the two-level system in quantum optics to solve the time-dependent Schrodinger equation.
3D Point Cloud Completion with Geometric-Aware Adversarial Augmentation
In contrast to the PGD-k attack, our method generates adversarial samples that keep the geometric features in clean samples and contain few outliers.
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