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Transition role of entangled data in quantum machine learning
Recent progress has highlighted its positive impact on learning quantum dynamics, wherein the integration of entanglement into quantum operations or measurements of quantum machine learning (QML) models leads to substantial reductions in training data size, surpassing a specified prediction error threshold.
Power of Quantum Generative Learning
We further employ these generalization bounds to exhibit potential advantages in quantum state preparation and Hamiltonian learning.
Few-shot Backdoor Defense Using Shapley Estimation
Deep neural networks have achieved impressive performance in a variety of tasks over the last decade, such as autonomous driving, face recognition, and medical diagnosis.
Spatial-Temporal-Fusion BNN: Variational Bayesian Feature Layer
Bayesian neural networks (BNNs) have become a principal approach to alleviate overconfident predictions in deep learning, but they often suffer from scaling issues due to a large number of distribution parameters.
Fairness-aware Federated Learning
Federated Learning is a machine learning technique where a network of clients collaborates with a server to learn a centralized model while keeping data localized.
Wasserstein Distributionally Robust Optimization: A Three-Player Game Framework
Wasserstein distributionally robust optimization (DRO) has recently received significant attention in machine learning due to its connection to generalization, robustness and regularization.
Explicit Learning Topology for Differentiable Neural Architecture Search
Differentiable neural architecture search (NAS) has gained much success in discovering more flexible and diverse cell types.
Stretchable Cells Help DARTS Search Better
However, even for this consistent search, the searched cells often suffer from poor performance, especially for the supernet with fewer layers, as current DARTS methods are prone to wide and shallow cells, and this topology collapse induces sub-optimal searched cells.
Understanding Generalization in Recurrent Neural Networks
We first present a new generalization bound for recurrent neural networks based on matrix 1-norm and Fisher-Rao norm.
Theoretical Analysis of Adversarial Learning: A Minimax Approach
Here we propose a general theoretical method for analyzing the risk bound in the presence of adversaries.
