MLINFO COLLECTION
日次更新の新着・公開記事を新しい順に掲載しています。
This paper introduces Quaternion Approximate Networks (QUAN), a novel deep learning framework that leverages quaternion algebra for rotation equivariant image classification and object detection.…
このライブラリは、空間情報を扱うためのコンピュータビジョンライブラリです。
Awesome-Video-Diffusionは、Recent Diffusion Models for Video Generation, Editing, and Othersのリストを公開しています。
マルチモーダル理解技術のための新しいアプローチであるMIRRORを提案しました。MIRRORは、テキスト、図、テキストと図の組み合わせから等価な視点を提供することで、視覚的な推論や複雑な推論力を向上し、さまざまなモデルのバリエーションを取り入れることを可能にします。
Mathematical Foundations of Reinforcement Learningは、ディープラーニングにおける推論力学習の数学的基礎を網羅している。
Machine Learning Engineeringは、機械学習の開発と運用をサポートするためのリソースを提供する。
spaCyはPythonで動くIndustrial-strength Natural Language Processing(Language理解の高度なライブラリです。文脈理解、構文解析、名詞の抽出など、複雑なNLPタスクに適しています。
Ludwigは、LLM (Large Language Model) のカスタム化と構築のための低コストフレームワークです。このフレームワークは、ユーザーがカスタム LLM を構築し、トレーニングするのを容易にします。
Feature extraction from unstructured data typically relies on opaque embeddings unsuited to interpretable decision-making. Interpretable features, on the other hand, typically require costly, and…
We study experimental results of best response dynamics (BRD) in randomly generated weighted action networked multiagent optimization problems where agent utility is the agent's marginal contribution…
Recent advances in monocular depth estimation have demonstrated highly accurate 3D reconstructions within in-distribution terrestrial domains. This study investigates the efficacy of several…
LLM-as-a-Judge has become a standard for evaluating large language models, yet most approaches implicitly assume that a single judge prompt functions as a stable evaluation instrument. Prior work has…
Sparse autoencoders (SAEs) can predict internal features associated with model errors, but association alone does not establish causal influence. We study this distinction in multiple-choice question…
Airborne magnetic anomaly navigation provides a passive, unjammable alternative to Global Navigation Satellite Systems positioning by matching magnetometer measurements against a lithospheric…
Ship-to-home fulfillment requires a retailer to select a fulfillment location and carrier before the delivery time is known. These decisions often rely on static transit-time estimates, which cannot…
Systematic reviews synthesize biomedical evidence to inform clinical and public-health decision-making, but require domain experts to manually screen thousands of studies against detailed eligibility…
We introduce EARS-Info, an information-aware extension of Event-Driven Ant Reallocation Search for multi-robot exploration on OpenStreetMap-derived grids. It combines marginal sensing gain, feasible…
Deep Gaussian processes (DGPs) are flexible surrogate models for computer experiments with nonstationary response surfaces, but inference over their latent representations remains computationally…
Bounds based on Integral Probability Metrics (IPMs) are ubiquitous in machine learning and statistics, appearing, e.g., in generalization, alignment, fairness, and privacy. IPMs reduce the…
Reinforcement learning holds the promise of robots that learn manipulation skills from interaction alone, without pretrained priors or demonstration data. In practice, on-policy methods such as PPO…
Humanoid controllers often require task-specific training despite sharing the need for balance and whole-body coordination. We present Metatron, a promptable whole-body execution layer combining a…
Automatic speech recognition (ASR) increasingly supplies transcripts for educational NLP, but aggregate word error rate (WER) can conceal errors concentrated in learner speech, multilingual turns,…
Numbers computed from trained models, such as an example's difficulty or a model's benchmark lead, change with seed and prompt. We ask when such measurements can be corrected or compared.
Medical chart abstraction transforms unstructured clinical narratives into structured variables for research and quality monitoring. We evaluated a prompting-based large language model (LLM)…