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Multi-Task Learning (MTL) networks have emerged as a promising method for transferring learned knowledge across different tasks. …
The COVID-19 pandemic has spread rapidly worldwide, overwhelming manual contact tracing in many countries and resulting in widespread …
Recent work has explored the possibility of pruning neural networks at initialization. We assess proposals for doing so: SNIP (Lee et …
Action and observation delays commonly occur in many Reinforcement Learning applications, such as remote control scenarios. We study …
The dominant term in PAC-Bayes bounds is often the Kullback–Leibler divergence between the posterior and prior. For so-called …
We propose a stochastic variant of the classical Polyak step-size (Polyak, 1987) commonly used in the subgradient method. Although …
Combinatorial optimization has found applications in numerous fields, from aerospace to transportation planning and economics. The goal …
Coronavirus Disease 2019 (COVID-19) has spread aggressively across the world causing an existential health crisis. Thus, having a …
Data augmentation is a key practice in machine learning for improving generalization performance. However, finding the best data …
Super-resolution (SR) has achieved great success due to the development of deep convolutional neural networks (CNNs). However, as the …