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2,609 papers
Reinforcement learning (RL) relies heavily on reward signals to guide policy learning. However, conventional metrics like cumulative reward and average return frequently fail to capture the nuances of policy performance, particularly in complex environments. This work proposes a novel approach to validating RL policies...
Multi-objective reinforcement learning (MORL) presents significant challenges due to the difficulty in balancing multiple, often conflicting, objectives. Traditional exploration methods often fail to adequately explore the complex multi-objective action space, leading to suboptimal solutions. This paper proposes a nove...
This paper proposes a novel framework for dynamically mapping neuron topologies and generating adaptive neuron connections within neural networks. The core idea is to integrate topological network modeling with reinforcement learning to create a system capable of learning and adapting to complex neural network topologi...
This paper introduces a novel self-adaptive GAN (GAN) training strategy designed to enhance model generation quality and stability. Traditional GAN training often relies on manual parameter adjustment, which can be time-consuming and suboptimal. Our approach leverages a dynamic adjustment mechanism to automatically tun...
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This paper presents a novel approach to massive data stream processing based on adaptive control. The core idea is to dynamically adjust processing strategies based on the inherent characteristics of the data stream, thereby optimizing efficiency and resource utilization. We leverage reinforcement learning (RL) to lear...
Quantum error correction (QEC) is a cornerstone of quantum information processing, enabling the reliable transmission of quantum information over noisy channels. However, the performance of QEC protocols is heavily reliant on the efficient allocation of quantum channels, a process that is often static and suboptimal. T...
This paper introduces an Adaptive Graph Neural Network (AGNN) designed to enhance Bayesian inference, particularly within complex, high-dimensional datasets. Traditional graph neural networks struggle with data complexities, often requiring extensive hyperparameter optimization. We propose an innovative AGNN architectu...
This dataset accompanies the manuscript "Planning for Leakage-Aware Collaborative Carbon Mitigation: Optimizer-Informed Multi-Agent Reinforcement Learning in Shandong's Dual Metropolitan Areas" submitted to the Journal of Urban Planning and Development (ASCE). It contains the full numerical inputs, calibrated artefacts...
Quantum state tomography (QST) is a fundamental technique for characterizing the state of a quantum system. However, traditional QST protocols often require a large number of measurements, leading to significant experimental overhead and uncertainties in the reconstructed state. This paper proposes a novel approach to...
This paper develops WRRA as a minimal-computation artificial intelligence architecture. WRRA transforms SOURCE through RELATION into STATE, converts that state into observable behavior through a RENDERER, and limits every conclusion by explicit OBSERVABLE and BOUNDARY conditions. Its objective is not maximum performanc...
This Supplemental Materials file accompanies the manuscript submitted to the Journal of Urban Planning and Development (ASCE). It contains eleven supplementary figures (Figs. S1–S11) and three supplementary tables (Tables S1–S3), whose numbering matches the in-text citations and supports every robustness, calibration,...
MATLAB source code supporting the research article “QMDP-PER-DDQN Trajectory Tracking for Mecanum Robots under Partial Observability.” This repository provides the MATLAB implementation of a belief-aware trajectory-tracking framework for a four-Mecanum-wheel mobile robot operating under noisy and partially observable s...
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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