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2,609 papers
This paper presents a novel self-adaptive neural network architecture leveraging machine learning to dynamically adjust network structure and parameters. Traditional neural network design relies heavily on human expertise, limiting the network's adaptability. This work introduces a framework that utilizes machine learn...
This paper presents Adaptive Network Routing Based on Reinforcement Learning (ANRRL), a novel approach to network routing optimization utilizing reinforcement learning (RL). Traditional network routing protocols often rely on static configurations or shortest-path algorithms, which may not adapt effectively to dynamic...
This paper explores the application of Deep Reinforcement Learning (DRL) for optimizing quantum resource allocation in quantum computing. Traditional quantum resource allocation often relies on manual design and expert knowledge, which can be computationally expensive and limit scalability. We propose a novel framework...
This paper investigates the application of Deep Reinforcement Learning (DRL) for dynamic hardware resource allocation in complex systems. Traditional resource allocation methods often rely on static rules, which struggle to adapt to fluctuating workloads and system states. This research proposes a DRL-based approach wh...
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This paper introduces an adaptive constraint-based fractal generation system designed to produce visually compelling and optimized fractal images. The core innovation lies in a reinforcement learning algorithm that dynamically adjusts fractal parameters based on feedback from the input data, allowing for continuous opt...
Quantum circuit compilation is a crucial step in realizing quantum algorithms on real quantum hardware. Traditionally, compilation methods are largely static, relying on pre-defined mappings and optimizations that fail to account for the inherent noise and variability present in quantum devices. This paper proposes a n...
This paper presents a novel framework for intelligent learning and decision-making in complex environments by fusing probabilistic graphical model learning with reinforcement learning. The core idea is to construct a dynamic system capable of learning both the structure and parameters of probabilistic graphical models,...
The design of neural networks for deployment on specialized hardware is a significant bottleneck in the widespread adoption of deep learning. Traditional methods often rely on manual tuning or computationally expensive search algorithms, failing to effectively account for the unique constraints and opportunities presen...
This paper proposes a novel approach to online anomaly detection utilizing adaptive Bayesian Networks (ABNs). Traditional Bayesian Networks (BNs) often fail to effectively handle the dynamic nature of real-world data streams, leading to inaccurate anomaly detection. Our approach overcomes this limitation by dynamically...
This research proposes a novel approach to neural network design centered around dynamic topology adaptation. The core concept leverages the principles of biological neural networks, specifically incorporating dynamic synaptic plasticity and connection modulation, to create networks capable of real-time adaptation to c...
Dynamical Topology Optimization (DTO) provides a framework for optimizing complex network topologies by iteratively refining connections based on emergent properties. This paper introduces a novel algorithm, termed 'Adaptive Topology Evolution', which integrates reinforcement learning and graph theory to achieve automa...
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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