「Calibration」の検索結果
53 件この研究では、Zero-Touch自動化のために、時間系列モデルをトラッキングするための、機械学習ベースのアプローチを開発しました。このアプローチは、機器の唯一の出力を利用し、データを生成することができます。
予測の正確性と確信度の関係を調査。モデルの予測の正確性と確信度の関係が誤差の大きさに左右されることを示した。
Football event data constitute a rich spatiotemporal source for quantitative analysis of player actions in tea
Clinical early warning systems built on electronic health records, in which clinical observations are recorded
言語モデルの寿命リスクへの適用を実現するために、コックス比例危険モデルを使用して、新しいアプローチを提案します。
Synthetic healthcare data are widely proposed as privacy-preserving substitutes for real patient data, yet the
AI Scientist agents are often evaluated as if capability were mainly a function of model quality, prompting, o
ウェットウェストワーテルマーケティングのインシデント検出を目的としたBayesian Selective Latent Inferenceが提案される。
脳-エージェント接続での脳サイン入力を安全に実行できるシステムを提案し、脳サイン入力攻撃を検知することができる。
可変化の帯域幅を考慮した、聴覚超材料の逆設計における新しいフレームワークである Physics-Guided Sequence-Based Generative Framework for Acoustic Metama
訓練データ以外の問題解決を検討したため、新しい評価方法であるexpert-curated rubric-based evaluationを 提案。
Large language models (LLMs) are increasingly used for medical summarization, but their outputs can omit medic
Chinese discriminatory-language detection is challenging because harmful intent is often implicit and context-
Reliable robotic navigation necessitates the seamless integration of accurate global localization and dense, m
Accurate calibration of particle-based simulators is crucial for robotic earthwork simulation, but analytical
Large language models frequently fail in a characteristic way: rather than acknowledging ignorance, they produ
Unmanned aerial vehicles (UAVs) are increasingly being deployed in logistics, service robotics, and other real
Effective visuo-tactile integration is critical for robotic dexterous manipulation, especially when visual obs
Multimodal Large Language Models (MLLMs) face a significant inference bottleneck due to the quadratic computat
Externally controlled survival trials are increasingly used when concurrent randomized controls are infeasible
The main goal in regression modelling consists in approximating the conditional mean of a response given a set
Large Language Models (LLMs) have significantly propelled the advancement of edge intelligence and have been w
Reliable evaluation of large language models in surgery remains underdeveloped. Broad medical benchmarks test
Large language models (LLMs) are increasingly used in academic research workflows, but scholarly tasks require
Visual Autoregressive (VAR) models adopt a next-scale prediction paradigm, offering high-quality generation wi
Recovering the relative 6-DoF pose between two image groups underlies cross-sequence relocalization and multi-
Feed-forward 3D reconstruction models have recently shown strong generalization across diverse scenes, yet mos
In-context imitation learning (ICIL) enables robots to learn new tasks from a small number of demonstrations b
Synthetic dialogue corpora are increasingly used as proxies for target dialogue data, yet persona-grounded gen
この論文では、四足ロボットのシマイルのためのQuadVerseフレームワークを提案した。QuadVerseは、視覚的、物理的、動的なギャップを考慮したシマイルを用い、四足ロボットの実験環境とシマイルを統合した。
負のイベントのリスクを受け流すことで個人が極端な負担にならないように、各参加者に信用できる負担金の上限を提供する仕組みを作ります。
この研究では、加速度データの正確化に取り組み、加速度計の線形化を実現する ALAC を提案します。
この研究では、人間-ロボット 協力のためのDistributed Conversational Frameworkを提案します。
Multimodal Large Language Models (MLLMs) excel at 2D semantic understanding but lack intrinsic 3D awareness, r
A situated query like "where is Lin Wei?" often encodes more than its literal content: the user may also want
Large language models are increasingly evaluated by other models, raising a natural question: can a model pred
Resource-constrained pricing controllers can make fixed-price inference impossible: the controller's resource
Large Language Models remain plagued by hallucinations. Recent work has sought to tame their prevalence using
スタンダードなコンフォーマル推測 (CP) 手法では、p値を使用します。しかし、p値だけで十分ではありません。e値ベースのコンフォーマル推測手法はp値と連関があることを実証しました。e-value手法はstatistic
Concepts of calibration formalize the compatibility between probabilistic predictions and the respective outco
Urban traffic simulation is a critical tool for infrastructure planning, including the placement of electric v
Probabilistic time series forecasting has attracted increasing attention in financial applications due to the
We present a single classification pipeline that combines an Equiangular Tight Frame (ETF) preprocessing stage
Robust optimization offers a tractable approach to balance operating costs and reliability in power systems do
Due to their explicit priors and ability to model uncertainty, Bayesian methods have played a major role in dy
We present Stable-Layers, a reinforcement learning framework that eliminates the need for paired supervision b
The classic concept of "calibrated forecasts" and its more recent refinement, "calibeating," are defined with
Validators on generic Proof of Stake chains earn the same fees whether they handle attestation work correctly
We prove that no reinforcement learning policy with confidence-gated autonomy can simultaneously achieve maxim
本論文では、機械学習と物理法則を組み合わせたPhysics-Informed Machine Learning (PIML)を電力システム予測に適用した。PIMLは、機械学習と物理法則を組み合わせて、高精度で正確な予測を
One prominent method of evaluating machine learning model trustworthiness is the notion of calibration. In the
Negotiation is a central mechanism of economic exchange, shaping markets, procurement, labor agreements, and r
This paper proposes Drain-Vortex Optimization (DVO), a population-based metaheuristic for continuous optimizat