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2,609 papers
A comprehensive and technically grounded review of learning-based world models for Physical AI, with particular emphasis on closed-loop decision-making, and outlines principled directions for building reliable and scalable Physical AI systems.
This paper presents a novel approach to swarm control that leverages collective intelligence to achieve adaptive behavior. Traditional swarm control methods often rely on pre-defined rules and lack the flexibility to respond to dynamic environmental changes or unexpected events. This work addresses this limitation by c...
An observer‐actor‐critic architecture is introduced, where observers are used to estimate unmeasurable states, improving the reliability and accuracy of the system and proving the Lyapunov stability theory to be rigorously proven.
Deep learning models often exhibit sensitivity to training data, leading to suboptimal performance. Traditional regularization techniques, while effective, can be inflexible and require extensive hyperparameter tuning. This paper introduces an adaptive topology for deep learning model regularization that dynamically ad...
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This paper introduces a novel self-adaptive reinforcement learning algorithm based on topological networks. Traditional reinforcement learning approaches often rely on predefined reward functions and static policies, limiting their ability to adapt to dynamic environments. Our algorithm leverages the inherent topologic...
This paper presents a novel approach to neural network optimization that dynamically adapts the network topology based on learned dependencies. The core idea is to leverage reinforcement learning, where two agents collaborate: one adjusts connection weights and the other modifies the network's structure (adding, removi...
Purpose Edge-AI intrusion detection in cyber-physical-social systems (CPSS) must operate under temporal drift while respecting strict low-false-positive-rate (low-FPR) budgets. This study aims to evaluate whether a deep reinforcement learning intrusion detector, RO-DDDQN, provides a reliable low-FPR operating point und...
This paper proposes a novel cognitive architecture for embodied artificial intelligence (AI) systems, designed to address the limitations of current approaches. The architecture leverages principles of cognitive science, specifically Bayesian inference, hierarchical control, and reinforcement learning, to facilitate ro...
This paper explores the application of adaptive chaos theory, leveraging reinforcement learning to dynamically adjust parameters of chaotic systems. Traditional approaches often rely on fixed, static parameter sets, limiting the system's flexibility and potential for robust behavior. This research introduces a novel sy...
This paper presents a novel approach to digital circuit synthesis leveraging the power of Evolutionary Reinforcement Learning (ERL). Traditional circuit synthesis methods often rely on deterministic algorithms that struggle to effectively manage the complexity and dynamism inherent in modern design requirements. This r...
Neuro-hacking, understood here as the deliberate application of neuroscience-informed techniques to regulate attention, memory formation and cognitive load, has begun to migrate from clinical and self-improvement contexts into mainstream higher-education pedagogy. This review synthesises evidence from five primary stud...
This research investigates the application of reinforcement learning to automatically generate and optimize geometric patterns, focusing on identifying novel patterns exhibiting complex, self-organizing behavior. Traditional geometric pattern generation often relies on predefined rules, limiting the potential for truly...
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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