KAIST researchers have developed a safety verification technology that uncovers roughly seven times more hidden ...
Researchers from the Max Planck Institute for Intelligent Systems (MPI-IS), the Tübingen AI Center and Ellis Institute ...
In this repository, we publish the code used to implement the Alternate Training through the Epochs (ATE) procedure for training Multi-Task Neural Networks (MTNN) presented in ATE-SG: alternate ...
Dr. James McCaffrey presents a complete end-to-end demonstration of the kernel ridge regression technique to predict a single numeric value. The demo uses stochastic gradient descent, one of two ...
Abstract: Entropy regularization is an efficient technique for encouraging exploration and preventing a premature convergence of (vanilla) policy gradient (PG) methods in reinforcement learning (RL).
Stochastic optimization problems involve making decisions in environments with uncertainty. This uncertainty can arise from various sources, such as sensor noise, system disturbances, or unpredictable ...
In talk, we will discuss a proximal gradient algorithm for feedback controls of finite-time horizon stochastic control problems. The state dynamics are continuous time nonlinear diffusions with ...
Though we’re living through a time of extraordinary innovation in GPU-accelerated machine learning, the latest research papers frequently (and prominently) feature algorithms that are decades, in ...
As artificial intelligence (AI) applications become ubiquitous in medical care, autonomous driving, robotics, and other fields, accuracy requirements and neural network complexity increase in tandem, ...
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