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  • Annotated history of modern AI and deep neural networks

    For a while, DanNet enjoyed a monopoly. From 2011 to 2012 it won every contest it entered, winning four of them in a row (15 May 2011, 6 Aug 2011, 1 Mar 2012, 10 Sep 2012).[GPUCNN5] In particular, at IJCNN 2011 in Silicon Valley, DanNet blew away the competition and achieved the first superhuman visual pattern recognition[DAN1] in an international contest. DanNet was also the first deep CNN to win

      Annotated history of modern AI and deep neural networks
    • https://deeplearningtheory.com/PDLT.pdf

      The Principles of Deep Learning Theory An Effective Theory Approach to Understanding Neural Networks Daniel A. Roberts and Sho Yaida based on research in collaboration with Boris Hanin drob@mit.edu, shoyaida@fb.com ii Contents Preface vii 0 Initialization 1 0.1 An Effective Theory Approach . . . . . . . . . . . . . . . . . . . . . . . . 2 0.2 The Theoretical Minimum . . . . . . . . . . . . . . . .

      • Scientific Computing in Rust - aftix's dominion

        While getting my degree in Physics, I had to take classes in both MatLab and Python for scientific computing. I preferred python, where we used the SciPy and NumPy packages. In fact, I used those packages again (along with matplotlib) in an undergraduate research project simulating bacteria films. There's a catch: I was also pursuing a degree in Computer Science, and Python just wasn't fast enough

        • GitHub - hanjuku-kaso/awesome-offline-rl: An index of algorithms for offline reinforcement learning (offline-rl)

          Value-Aided Conditional Supervised Learning for Offline RL Jeonghye Kim, Suyoung Lee, Woojun Kim, and Youngchul Sung. arXiv, 2024. Towards an Information Theoretic Framework of Context-Based Offline Meta-Reinforcement Learning Lanqing Li, Hai Zhang, Xinyu Zhang, Shatong Zhu, Junqiao Zhao, and Pheng-Ann Heng. arXiv, 2024. DiffStitch: Boosting Offline Reinforcement Learning with Diffusion-based Traj

            GitHub - hanjuku-kaso/awesome-offline-rl: An index of algorithms for offline reinforcement learning (offline-rl)
          • Functions are Vectors

            Conceptualizing functions as infinite-dimensional vectors lets us apply the tools of linear algebra to a vast landscape of new problems, from image and geometry processing to curve fitting, light transport, and machine learning. Prerequisites: introductory linear algebra, introductory calculus, introductory differential equations. This article received an honorable mention in 3Blue1Brown’s Summer

            • Policy Roundtable: The Future of Japanese Security and Defense - Texas National Security Review

              In this roundtable, which grew out of a conference on maritime strategy in the Indo-Pacific region sponsored jointly by the United States Naval War College, the Japan Maritime Self-Defense Forces Maritime Command and Staff College, and the Sasakawa Peace Foundation, our contributors examine growing Japanese defense capabilities and aspirations. The authors examine the impact of a more robust Japan

                Policy Roundtable: The Future of Japanese Security and Defense - Texas National Security Review
              • Quantum Algorithm Zoo

                This is a comprehensive catalog of quantum algorithms. If you notice any errors or omissions, please email me at spj.jordan@gmail.com. (Alternatively, you may submit a pull request to the repository on github.) Although I cannot guarantee a prompt response, your help is appreciated and will be acknowledged. Algebraic and Number Theoretic Algorithms Algorithm: Factoring Speedup: Superpolynomial Imp

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