Paper2Code: Automating Code Generation from Scientific Papers in Machine Learning
Despite the rapid growth of machine learning research, corresponding code implementations are often unavailable, making it slow and labor-intensive for researchers to reproduce results and build upon prior work. In the meantime, recent Large Language Models (LLMs) excel at understanding scientific documents and generating high-quality code. Inspired by this, we introduce PaperCoder, a multi-agent
Darwin Godel Machine: Open-Ended Evolution of Self-Improving Agents
Today's AI systems have human-designed, fixed architectures and cannot autonomously and continuously improve themselves. The advance of AI could itself be automated. If done safely, that would accelerate AI development and allow us to reap its benefits much sooner. Meta-learning can automate the discovery of novel algorithms, but is limited by first-order improvements and the human design of a sui
Google DeepMind、AIが自らアルゴリズムを発見・進化させる「AlphaEvolve」発表
米Google傘下のGoogle DeepMindは5月14日（現地時間）、AIが自らコンピュータの計算手順やアルゴリズムを発見し、さらに進化させる新しいAIエージェント「AlphaEvolve」を発表した。 AlphaEvolveの目的は、コンピュータサイエンスの基礎となるアルゴリズムや複雑な数学的課題に対する解法を、人間がゼロから開発するのではなく、AI自身が見つけ出し、最適化することにあるという。 このシステムの大まかな流れは、高速な「Gemini Flash」でアイデア候補を生成し、それを「Gemini Pro」が深く洞察して提案することで、アルゴリズムを実装するプログラムを生み出す。このプログラムを、AlphaEvolveに組み込まれた自動評価ツールが検証、実行、スコアリングすることで、アイデアを「進化」させていく。 AlphaEvolveは、既にGoogle内部で多岐にわたる成
Model evaluation, model selection, and algorithm selection in machine learning
A single-PDF version of Model Evaluation parts 1-4 is available on arXiv: https://arxiv.org/abs/1811.12808 Introduction Machine learning has become a central part of our life – as consumers, customers, and hopefully as researchers and practitioners! Whether we are applying predictive modeling techniques to our research or business problems, I believe we have one thing in common: We want to make “g
Back-to-Basics Weekend Reading - Survey of Local Algorithms
Back-to-Basics Weekend Reading - Survey of Local AlgorithmsMay 29, 2015 • 132 words As we know the run time of most algorithms increases when the input set increases in size. There is one noticeable exception: there is a class of distributed algorithms, dubbed local algorithms, that run in constant time, independently of the size of the network. Being highly scalable and fault tolerant, such algor
Design patterns for efficient graph algorithms in MapReduce
Design Patterns for Efficient Graph Algorithms in MapReduce Jimmy Lin and Michael Schatz University of Maryland, College Park {jimmylin,mschatz}@umd.edu ABSTRACT Graphs are analyzed in many important contexts, includ- ing ranking search results based on the hyperlink struc- ture of the world wide web, module detection of protein- protein interaction networks, and privacy analysis of social network
HyperLogLog++: Google’s Take On Engineering HLL –
Matt Abrams recently pointed me to Google’s excellent paper “HyperLogLog in Practice: Algorithmic Engineering of a State of The Art Cardinality Estimation Algorithm” [UPDATE: changed the link to the paper version without typos] and I thought I’d share my take on it and explain a few points that I had trouble getting through the first time. The paper offers a few interesting improvements that are w
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Balanced Partitioning and Hierarchical Clustering at Scale
http://www.vldb.org/pvldb/vol11/p2102-faloutsos.pdf
Bandit Problems
Understanding Dropout
Unsupervised Computer Vision: The State of the Art | Stitch Fix Technology – Multithreaded
http://data-people.cs.illinois.edu/courses/cs598/papers/turkit.pdf
A taxonomy of sequential pattern mining algorithms
https://dl.acm.org/citation.cfm?doid=2452376.2452456
http://outlier-analytics.org/odd13kdd/papers/guzman,poblete.pdf
Dimension Independent Matrix Square using MapReduce
https://tech.nextroll.com/media/hllminhash.pdf
http://research.google.com/pubs/archive/40671.pdf
https://algo.inria.fr/flajolet/Publications/DuFl03.pdf