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28 papers · 1 filter
Diversity Transfer Network for Few-Shot Learning
Mengting Chen, Yuxin Fang, Xinggang Wang +6
Few-shot learning is a challenging task that aims at training a classifier for unseen classes with only a few training examples. The main difficulty of few-shot learning lies in th…
Probabilistic Simulation of Quantum Circuits with the Transformer
Juan Carrasquilla, Di Luo, Felipe Pérez +4
The fundamental question of how to best simulate quantum systems using conventional computational resources lies at the forefront of condensed matter and quantum computation. It im…
Learning to grow: control of material self-assembly using evolutionary reinforcement learning
Stephen Whitelam, Isaac Tamblyn
We show that neural networks trained by evolutionary reinforcement learning can enact efficient molecular self-assembly protocols. Presented with molecular simulation trajectories,…
Angular Visual Hardness
Beidi Chen, Weiyang Liu, Zhiding Yu +4
Recent convolutional neural networks (CNNs) have led to impressive performance but often suffer from poor calibration. They tend to be overconfident, with the model confidence not…
Advances in High Dimensional Quantum Entanglement
Manuel Erhard, Mario Krenn, Anton Zeilinger
Since its discovery in the last century, quantum entanglement has challenged some of our most cherished classical views, such as locality and reality. Today, the second quantum rev…
MANGA: Method Agnostic Neural-policy Generalization and Adaptation
Homanga Bharadhwaj, Shoichiro Yamaguchi, Shin-ichi Maeda
In this paper we target the problem of transferring policies across multiple environments with different dynamics parameters and motor noise variations, by introducing a framework…