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2,609 papers
It is learned that genAI was created to be people pleasing which can override its ability to find truth, and how it supports error analysis in Large Language Models, and how the public should view these tools.
With the rapid development of the Internet of Things, computation intensive directed acyclic graph (DAG) tasks have become increasingly common in cloud-edge-end collaborative environments. However, cloud, edge, and end nodes are highly heterogeneous in computing capacity, network bandwidth, and energy consumption, whic...
This paper introduces Geometric Topology Optimization Algorithms (GTOA) – a novel approach that integrates deep learning with topology optimization to accelerate complex structural simulations and design. We propose a novel "拓扑感知" algorithm utilizing Generative Adversarial Networks (GANs) to learn the geometric str...
This paper presents a novel approach to robot motion planning based on adaptive learning. Traditional robot motion planning methods often struggle to cope with dynamic and uncertain environments. This research leverages reinforcement learning (RL) to develop a system capable of autonomously learning optimal motion stra...
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This paper explores a novel approach to reinforcement learning (RL) that integrates causal inference to enhance both the reliability and interpretability of decision-making processes. Traditional RL algorithms often operate as "black boxes," lacking transparency into the reasoning behind their actions. This research ad...
This paper introduces a Dynamic Semantic Network Generator (DSNG), a novel system designed to automatically construct and maintain semantic networks representing complex, dynamic systems. The core of the DSNG is a reinforcement learning (RL) framework that adapts the network's structure and connections in response to c...
This paper introduces Dynamic Context-Aware Neural Architecture Search (DCA-NAS), a novel approach to neural architecture search that addresses the limitations of traditional NAS methods by incorporating dynamic contextual information during the search process. The core claim of DCA-NAS is that real-time analysis of in...
This paper investigates the potential of dynamically self-organizing neural networks inspired by biological systems. Traditional static neural networks often struggle with adaptability and efficiency in complex environments. We propose a novel approach that leverages reinforcement learning to dynamically adjust connect...
This paper investigates the application of Multi-Agent Reinforcement Learning (MARL) to the complex system scheduling problem. Traditional scheduling methods often struggle to adapt to dynamic and intricate system environments, leading to suboptimal performance. This research proposes a novel framework leveraging MARL...
Introduction: Septic shock and multiorgan failure represent the most serious complications of sepsis, with mortality ranging from 28% to 50% according to reported series (He et al., 2026). Clinical decision support systems based on artificial intelligence have emerged as promising tools to optimize the management of th...
To mitigate high expert-annotation costs, domain-preference misalignment, and the inherent trade-off between sensitive-data protection and training utility in petrochemical dataset construction, an iterative framework combining human-feedback-aligned reinforcement learning (RLHF) with post hoc data de-identification is...
This paper proposes a lightweight, simplified Q-learning energy management strategy for extended-range electric vehicles (REEVs), successfully implemented on an STM32 microcontroller, fully satisfying strict on-board embedded system constraints and providing a highly feasible solution for intelligent REEV energy manage...
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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