Current models on the JSP do not focus on generalization, although, as we show in this work, this is key to learning better heuristics on the problem.
To the best of our knowledge, our study establishes the first model-based online algorithm with regret guarantees under LTV dynamical systems.
For a given stable recurrent neural network (RNN) that is trained to perform a classification task using sequential inputs, we quantify explicit robustness bounds as a function of trainable weight matrices.
A very large number of communications are typically required to solve distributed learning tasks, and this critically limits scalability and convergence speed in wireless communications applications.
To address this issue, we propose a contrastive learning approach to improve the quality and enhance the semantic consistency of synthetic images.
Ranked #5 on Text-to-Image Generation on CUB
This paper proposes a framework of L-BFGS based on the (approximate) second-order information with stochastic batches, as a novel approach to the finite-sum minimization problems.
We propose a projected semi-stochastic gradient descent method with mini-batch for improving both the theoretical complexity and practical performance of the general stochastic gradient descent method (SGD).
The scale of modern datasets necessitates the development of efficient distributed optimization methods for machine learning.