pytorch — Tensors and Dynamic neural networks in Python with strong GPU acceleration
pytorchはPythonでTensorや動的ニューラルネットワークを実装でき、強力なGPUアクセラレーションをサポートしている。
CUDAアクセラレーション付きのAI
MLINFO COLLECTION
機械学習
記事一覧
1 / 5tensorflow — An Open Source Machine Learning Framework for Everyone
TensorFlowはオープンソースの機械学習フレームワークで、誰でも使用できるように設計されている。
人間に学習フレームワーク
streamlit — Streamlit — A faster way to build and share data apps.
streamlitはStreamlitライブラリを使って、データアプリを作成・共有することができる。
データアプリ作成
julia — The Julia Programming Language
juliaはJuliaプログラミング言語で、高速演算とパッケージ管理が可能である。
Juliaプログラミング
netdata — The fastest path to AI-powered full stack observability, even for lean teams.
netdataは、チームに関係なくAIパワーで全システム観察できる最速のパスを提供している。
全システム観察
scikit-learn — scikit-learn: machine learning in Python
scikit-learnはPythonで機械学習を行うことができるライブラリで、データの分類・可視化・特徴量選択などが可能である。
マシンラーニング
shap — A game theoretic approach to explain the output of any machine learning model.
機械学習モデルの出力を説明するゲーム理論的手法を取ったライブラリ。
機械学習モデルの解釈
keras — Deep Learning for humans
kerasは、人類が開発できる深層学習フレームワークで、モデル定義からトレーニングまでを行うことが可能。
人間による深層学習
fiftyone — Refine high-quality datasets and visual AI models
FiftyOneは、データセットの精査とAIモデル可視化を支援するライブラリです。このライブラリは、データセットの品質を高め、AIモデルを可視化するのを支援するために使用できます。
データセットの精査とAIモデル可視化
google-research — Google Research
google-researchはGoogle Researchで、研究者がアクセスすることができる機械学習リソースの一つである。
Google研究
photoprism — AI-Powered Photos App 🌈💎✨
photoprismはAIパワーで管理される写真管理アプリケーションで、写真の特徴や情報を自動的に検出することができる。
写真管理
Self-regularization and structural adaptation of NPMLE for Gaussian mixture models in all dimensions
We establish structural properties of the nonparametric maximum likelihood estimator (NPMLE) for $d$-dimensional Gaussian mixtures whose means lie in a hypercube. We show that, with high probability,…
machine-learning
Fast and accurate neural triage decoding of ultra-high-rate quantum LDPC codes
Quantum error correction is essential for fault-tolerant quantum computing. High-rate quantum low-density parity-check (qLDPC) codes have emerged as a promising approach to reducing the substantial…
machine-learning
Causal-prior-constrained deep learning for rapid-intensification warning of western North Pacific tropical cyclones
Rapid intensification (RI) is one of the most dangerous and unpredictable phases of tropical cyclones, and its environmental and structural drivers are difficult to identify and separate in…
machine-learning
Decision-Level Error Attribution and Depth-Aware Safety Evaluation of Monocular RGB Human Tracking for Autonomous Robot Navigation
Monocular RGB tracking is useful for robots operating near people, but perception errors affect binary stop/proceed decisions in ways that geometric accuracy metrics do not capture. We examine how…
machine-learning
Humanoid Dodgeball: Learning Fast Dynamic Obstacle Avoidance Motions for Humanoids
Humanoids have become increasingly agile and capable in real-world environments. To operate safely in dynamic scenarios, they must rapidly evade fast-moving obstacles while maintaining stability and…
machine-learning
A physics-informed uncertainty-aware surrogate for $p$-$n$ junction diodes
We develop an uncertainty-aware physics-informed surrogate for a one-dimensional $p$-$n$ junction diode, conditioned on device length, doping and bias. Using the internal state of the device as…
machine-learning
Open-set multi-view seahorse re-identification from underwater photograph sequences
Automated animal re-identification from photograph sequences can facilitate minimally invasive Capture-Mark-Recapture studies at scale. Yet, photographs taken during an observation session can vary…
machine-learning
Possibility-based Inference: A Deterministic Framework for Robust and Reliable Uncertainty Quantification in Decision-Making
Decision-making under uncertainty remains a key issue in Machine Learning (ML), particularly in the deployment of safety-critical systems, where reliable quantification of system behavior is…
machine-learning
Long-Term Agent Memory through Complementary Curation and Consolidation
Long-horizon dialogue agents require effective memory systems to recall facts, events, and relationships from interaction histories. In this work, we propose a hybrid-memory architecture comprising…
machine-learning
Diagnosing Optimizer-Landscape Interaction via Representation in Subset Selection Multitasking: Evidence from Piano Fingering
Continuous relaxations are commonly used as an encoding scheme for evolutionary multitasking optimization (EMTO), allowing heterogeneous tasks to be optimized within a unified search space. However,…
machine-learning
Sandman: Building and Operating Sandboxes for Agentic Reinforcement Learning on Shared Kubernetes
Agentic reinforcement learning (RL) often requires repeated tool calls in task-specific environments. A training job may create thousands of these environments in bursts while sharing a cluster with…
machine-learning
A New AFIB Detection Method with Deep Neural Networks
Early detection of atrial fibrillation (AFIB), characterized by irregular heart rhythms, can potentially save thousands of lives annually [20]. Numerous studies have attempted to address this…
machine-learning
Rebuilding a Field's Open-Problem Catalog from the Literature: A Two-Layer Knowledge Graph, and Why Status Must Stay Proposed
A research field records its open problems in surveys, keynotes and community reports. Those are prose, written every few years, and nobody can ask them who works on a problem now or whether it was…
machine-learning