Abstract: Dynamic Programming suffers from the curse of dimensionality due to large state and action spaces, a challenge further compounded by uncertainties in the environment. To mitigate these issue ...

Machine learning requires humans to manually label features while deep learning automatically learns features directly from raw data. ML uses traditional algorithms like decision tress, SVM, etc., ...

AI is the broad goal of creating intelligent systems, no matter what technique is used. In comparison, Machine Learning is a specific technique to train intelligent systems by teaching models to learn ...

Federated graph learning advances the field of federated learning by enabling privacy-preserving collaborative training on distributed graph data. Conventional federated graph learning methods excel ...

Abstract: Temporal difference (TD) learning is a fundamental technique in reinforcement learning that updates value function estimates for states or state-action pairs using a TD target. This target ...

Understanding real-world videos with complex semantics and long temporal dependencies remains a fundamental challenge in computer vision. Recent progress in multimodal large language models (MLLMs) ...

The examples are nothing if not relatable: preparing breakfast, or playing a game of chess or tic-tac-toe. Yet the idea of learning from the environment and taking steps that progress toward a goal ...

In the 1980s, Andrew Barto and Rich Sutton were considered eccentric devotees to an elegant but ultimately doomed idea—having machines learn, as humans and animals do, from experience. Decades on, ...

In today’s rapidly evolving business environment, talent and learning development (L&D) leaders are tasked with ensuring their programs drive measurable impact. But navigating the learning technology ...