Abstract Deep learning models have achieved near-perfect accuracy in plant disease classification within controlled laboratory settings; however, their deployment in real-world agricultural environments is severely hindered by the “deployment gap”—a critical vulnerability to environmental corruptions such as sensor noi...
Nima Saeedi, Sana Gholinavaz, Sina Samadi Gharehveran et al.· Scientific Reports· 0 citations
This paper investigates the application of reinforcement learning (RL) for adaptive quantum control. Traditional quantum control methods often rely on pre-designed pulse shapes optimized for specific scenarios, lacking adaptability to variations in system parameters or environmental noise. This work proposes a novel fr...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel matrix operation algorithm based on adaptive learning. Traditional matrix operations often rely on fixed parameters, limiting their efficiency when dealing with dynamic input matrices. The proposed algorithm dynamically adjusts the operation parameters based on the input matrix's character...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This document contains a comprehensive evaluation of the preprint "General Error Accumulation Relation" by Stamelos Loutsos (September 2026). The original work addresses a fundamental problem in computational neuroscience and reinforcement learning: how the microscopic Bellman/Temporal‑Difference update rules relate to...
Stamelos Loutsos· Figshare· 0 citations
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This study aims to analyze the effect of the Collaborative Constructive approach on the quality of students’ mental models in the topic of the male reproductive system. The study employed a mixed-methods approach with an embedded experimental design and a quasi-experimental pretest-posttest control group design. The re...
Anindya Utami Putri, Adi Rahmat, Yanti Hamdiyati· Journal La Edusci· 0 citations
This paper introduces the Dynamic Topology Algorithm Reinforcement Learning (DTARL), a novel reinforcement learning framework designed to optimize complex systems by dynamically adjusting topology. Traditional topology optimization methods often rely on manual parameter tuning, while DTARL leverages reinforcement learn...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Large-scale graph analysis, prevalent in domains like social network analysis, bioinformatics, and knowledge graph management, faces significant challenges due to the memory limitations of traditional graph processing techniques. This paper introduces a dynamic graph partitioning algorithm designed to mitigate these is...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces an innovative adaptive quantum algorithm designed to address the limitations of traditional optimization techniques, particularly when dealing with non-linear problems. The algorithm leverages reinforcement learning to dynamically adjust its parameters based on the input data, offering a significa...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
High-dimensional sparse data poses significant challenges for traditional tensor decomposition techniques. These methods often suffer from instability, poor generalization, and difficulty in capturing the underlying structure of the data due to the curse of dimensionality and the irregular sparsity patterns. This work...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper explores the design and implementation of an Algorithmic Self-Organizing Constraint Network (ASOCN) architecture, a novel approach to constraint management within complex systems. The core claim centers on the development of a dynamic constraint adjustment system leveraging iterative reinforcement learning t...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Large-scale graph analysis, prevalent in domains like social network analysis, bioinformatics, and knowledge graph management, faces significant challenges due to the memory limitations of traditional graph processing techniques. This paper introduces a dynamic graph partitioning algorithm designed to mitigate these is...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper explores the application of algorithmic chaos theory to generate optimal and aesthetically pleasing patterns within complex networks. Traditional approaches often struggle with creating truly novel and captivating designs, while chaos theory offers a potential pathway to unpredictable beauty. We propose a no...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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.
MIT News · Artificial Intelligence· news.mit.eduOct 27, 2026
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.
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.
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