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2,609 papers
This paper presents a novel approach to computational graph optimization utilizing reinforcement learning (RL). Traditional static optimization methods often fail to adequately address the dynamic and evolving resource constraints encountered in modern computing environments. We propose a system where a reinforcement l...
The increasing complexity of software systems poses significant challenges in managing dependencies. Traditional dependency analysis tools struggle to adapt to the dynamic changes in topology, hindering compile-time optimization and runtime performance. This paper introduces the Dynamic Topology-Dependent Graph Optimiz...
This paper presents an innovative reinforcement learning algorithm designed to enhance the performance of chaotic systems through the integration of adaptive chaos algorithms. The core of this approach lies in employing reinforcement learning to train an adaptive chaos algorithm that dynamically adjusts its parameters...
This paper presents a novel dynamic adaptive optimization framework combining reinforcement learning with a dynamically adjusting optimization strategy. The core focus is on optimizing complex, non-structured problems, leveraging reinforcement learning to learn an optimized objective function and dynamically adjust the...
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Universal Constraint Induction (UCI) presents a novel framework for automatically generating and verifying constraints for complex dynamical systems, facilitating the creation of entirely new and unpredictable system behavior. This work introduces a constraint-discovery algorithm leveraging Generative Adversarial Netwo...
This dataset contains the supplementary material supporting the manuscript "Bridging formal topologies and graph-embedded deep reinforcement learning for maintenance scheduling in high-energy irradiation facilities". It includes the Master Sequence of Operations (SOP) generated by the GCN-TD3 optimizer to achieve the 7...
Reinforcement learning (RL) relies heavily on dynamic programming (DP) to find optimal policies. However, the curse of dimensionality significantly limits its applicability in environments with large or continuous state spaces. Traditional DP methods become computationally intractable, often requiring exponential time...
Distributed deep learning has emerged as a crucial paradigm for training increasingly complex models, often necessitating the partitioning of computational graphs across multiple devices. However, traditional graph partitioning techniques rely on static heuristics, failing to adapt to the dynamic and evolving workload...
Universal Constraint Induction (UCI) presents a novel framework for automatically generating and verifying constraints for complex dynamical systems, facilitating the creation of entirely new and unpredictable system behavior. This work introduces a constraint-discovery algorithm leveraging Generative Adversarial Netwo...
Reinforcement learning (RL) relies heavily on dynamic programming (DP) to find optimal policies. However, the curse of dimensionality significantly limits its applicability in environments with large or continuous state spaces. Traditional DP methods become computationally intractable, often requiring exponential time...
This paper introduces Dynamic Topology Dependency Learning (TDTL), a novel approach to neural architecture search (NAS) that leverages the inherent dependencies within network topology to achieve more efficient architecture optimization. The core idea is to train a "topology controller" using reinforcement learning (RL...
Research Manager Mike Walker reflects on a career spanning hacker culture, cybersecurity, and AI, sharing lessons about trust, teamwork, leadership, and why solving difficult problems requires curiosity, judgment, agility—and an understanding of the rules. The post Learn the rules: The hacker mindset appeared first on Microsoft Research.
Learn how Google Antigravity and our Cheminformatics Skills accelerate drug discovery workflows, search compounds, and propose novel rigidified macrocycles.
Prabha Jha used her experience with MIT Open Learning’s program for senior executives to develop AI solutions for her employer, Verizon, and to write a book.
Education Innovation
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.
A Blog post by NVIDIA on Hugging Face
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