githubGitHubあり2026-09-08
pruna — Pruna is a model optimization framework built for developers, enabling you to deliver faster, more efficient models with minimal overhead.
デベロッパー向けのモデロプティミゼーションフレームワークです。モデルの高速化と効率化を実現することができます。
深層学習Transformer分類音声
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Use Case
材料探索に使えるAI/機械学習技術
材料候補探索、分子生成、実験条件探索、ベイズ最適化に関係する技術です。
実装しやすい候補
githubGitHubあり2026-09-08
DeepSpeed — DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
分散トレーニングと推論を容易、効率的に実行するためのディープラーニング最適化ライブラリです。
深層学習軽量化・量子化
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githubGitHubあり2026-08-22
deep-learning-drizzle — Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!
Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learni
数学・理論最適化分類画像音声
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huggingfaceHugging Faceあり2026-09-02
Percolation Dynamics in Optimization : Variance Cascades and Discrete Scale Invariance
We study the dynamics of Stochastic Gradient Descent (SGD), which is known to steer deep neural networks towar
深層学習正規化・最適化手法
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huggingfaceGitHubありHugging Faceあり2026-08-31
WHALE: A Simple Recipe for Joint Harness-Weight Optimization
Agent performance depends jointly on the model parameters and the executable harness code that manages context
自然言語処理ファインチューニングQAテキスト
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huggingfaceHugging Faceあり2026-09-01
A Glance Is All You Need: Single-Pass Fine-Grained Image Captioning with SimLoss
An image may be worth a thousand words, but most captioning models describe it in only a few. Modern vision-la
MI向き品質予測/異常検知自然言語処理ファインチューニング生成画像テキスト
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関連記事
githubGitHubあり2026-09-08
pruna — Pruna is a model optimization framework built for developers, enabling you to deliver faster, more efficient models with minimal overhead.
デベロッパー向けのモデロプティミゼーションフレームワークです。モデルの高速化と効率化を実現することができます。
深層学習Transformer分類音声
- 用途
- モデロプティミゼーション
- 難易度
- Easy
- コスト
- Low
→
githubGitHubあり2026-09-08
DeepSpeed — DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
分散トレーニングと推論を容易、効率的に実行するためのディープラーニング最適化ライブラリです。
深層学習軽量化・量子化
- 用途
- ディープラーニング最適化ライブラリ
- 難易度
- Easy
- コスト
- High
→
githubGitHubあり2026-08-22
deep-learning-drizzle — Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!
Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learni
数学・理論最適化分類画像音声
- 用途
- 分類
- 難易度
- Easy
- コスト
- Low
→
huggingfaceHugging Faceあり2026-09-02
Percolation Dynamics in Optimization : Variance Cascades and Discrete Scale Invariance
We study the dynamics of Stochastic Gradient Descent (SGD), which is known to steer deep neural networks towar
深層学習正規化・最適化手法
- 用途
- 技術検証・論文読解補助
- 難易度
- Easy
- コスト
- Medium
→
huggingfaceGitHubありHugging Faceあり2026-08-31
WHALE: A Simple Recipe for Joint Harness-Weight Optimization
Agent performance depends jointly on the model parameters and the executable harness code that manages context
自然言語処理ファインチューニングQAテキスト
- 用途
- QA
- 難易度
- Easy
- コスト
- High
→
huggingfaceHugging Faceあり2026-09-01
A Glance Is All You Need: Single-Pass Fine-Grained Image Captioning with SimLoss
An image may be worth a thousand words, but most captioning models describe it in only a few. Modern vision-la
MI向き品質予測/異常検知自然言語処理ファインチューニング生成画像テキスト
- 用途
- 生成
- 難易度
- Easy
- コスト
- High
→
huggingfaceHugging Faceあり2026-08-19
Institutional Newspapers Pipeline: Deriving billions of high quality tokens from historical newspapers
Historical newspapers are an abundant record of public life, but their dense, irregular and sometimes noisy la
表形式向き説明可能MI向き品質予測/異常検知自然言語処理埋め込み・検索分類検出生成
- 用途
- 分類
- 難易度
- Easy
- コスト
- Low
→
huggingfaceHugging Faceあり2026-08-31
Group Adaptive Clipping Policy Optimization
Group relative policy optimization for reinforcement learning with verifiable rewards (RLVR) typically uses a
説明可能自然言語処理大規模言語モデル強化学習
- 用途
- 技術検証・論文読解補助
- 難易度
- Easy
- コスト
- High
→
huggingfaceGitHubありHugging Faceあり2026-08-29
Locked at the Entrance, Open Inside: Where RLVR Narrows the Solution Space
Reinforcement learning with verifiable rewards (RLVR) substantially improves single-sample accuracy (pass@1) b
深層学習Transformer検出強化学習
- 用途
- 検出
- 難易度
- Easy
- コスト
- High
→
arxivGitHubあり2026-09-04
VICAL: Vicinal Consistency Alignment for Long-Tailed Visual Recognition
Multi-expert models have become the dominant paradigm for long-tailed learning, largely attributed to their pr
深層学習軽量化・量子化分類画像テキスト
- 用途
- 分類
- 難易度
- Easy
- コスト
- Low
→
arxivGitHubあり2026-09-01
Artificial Rosetta Stone: Constrained Maximum A Posteriori (MAP) Reconstruction of Symbolic Raga Sequences via Order-k Markov Models
Reconstructing a damaged musical fragment is an inverse problem: the observed sequence contains partial inform
深層学習軽量化・量子化セグメンテーションテキスト音声
- 用途
- セグメンテーション
- 難易度
- Easy
- コスト
- Low
→
arxivGitHubあり2026-09-03
Artificial Intelligence for Energy Optimization in Data Centers
Data centers are increasingly optimized by artificial intelligence and, at the same time, increasingly loaded
コンピュータビジョンセグメンテーションテキスト
- 用途
- 技術検証・論文読解補助
- 難易度
- Easy
- コスト
- Medium
→
arxivGitHubあり2026-09-03
Efficient Constant Optimization for Symbolic Regression with GPU-Accelerated Tree-Based Genetic Programming
Constant optimization refines the numerical coefficients of candidate expressions in tree-based genetic progra
品質予測/異常検知深層学習軽量化・量子化生成回帰
- 用途
- 生成
- 難易度
- Easy
- コスト
- Medium
→
arxivGitHubあり2026-09-02
No-Regret Bayesian Optimization with Finite-Library Input-Warped Kernels
Gaussian-process Bayesian optimization (GP-BO) excels at black-box optimization of costly functions, e.g., hyp
少数データ向きCPUで試しやすい条件最適化コンピュータビジョンセグメンテーション
- 用途
- 技術検証・論文読解補助
- 難易度
- Easy
- コスト
- Medium
→
arxivGitHubあり2026-09-03
DRACO: Fine-Grained Credit Assignment with Dynamic Rubrics for Long-Horizon Agent Training
Reinforcement Learning from Verifiable Rewards works well when a task has a programmatic checker, but most lon
数学・理論最適化強化学習
- 用途
- 技術検証・論文読解補助
- 難易度
- Hard
- コスト
- High
→
arxivGitHubあり2026-08-03
An Evolutionary Algorithm Assisted by an Ensemble of Pareto-Optimal Surrogate Models
An ensemble of surrogate models helps improve the prediction quality and robustness of surrogate models, and i
説明可能品質予測/異常検知機械学習アンサンブル学習
- 用途
- 技術検証・論文読解補助
- 難易度
- Easy
- コスト
- Medium
→