「Calibration」の検索結果
61 件We introduce GLASS, a framework for graph-level anomaly detection (GLAD) that achieves robust cross-domain tra
Evaluating the calibration of Large Language Models (LLMs) is critical for their safe deployment as zero-shot
Client heterogeneity creates both an opportunity and a risk in personalized federated graph learning. Knowledg
The Duckworth-Lewis-Stern (DLS) method has been the international standard for revising target scores in rain-
Graph coarsening reduces the large Quadratic Unconstrained Binary Optimization (QUBO) formulations arising whe
Mixture-of-Experts (MoE) architectures provide an efficient paradigm for scaling large language models (LLMs),
Multilingual large language models often struggle to reason in low- to mid-resource languages. Prior work has
Hyperspectral and multispectral image fusion (HMIF) aims to reconstruct a high-resolution hyperspectral image
Continuous sliders are useful only when coefficient changes produce predictable image changes. Yet most diffus
Background and Objective: External evaluation of medical-imaging AI is often collapsed into discrimination. We
Industrial anomaly detection must handle two distinct defect families: structural anomalies, which manifest as
A conformal certificate can be valid when an LLM answers alone and invalid when the same LLM sees peers that u
Reinforcement learning from human feedback (RLHF) has emerged as a powerful yet sample-inefficient approach fo
Agent evaluations report a tool-call rate read off the serving stack. That number can be zero while the model
Federated Learning (FL) with Differential Privacy (DP) is increasingly adopted to preserve data confidentialit
We introduce Stateless Bernoulli Watermarking (SBW), a new statistical watermark for Large Language Models tha
Knowledge graphs describe reality in crisp assertions, while the systems now consuming them, foundation models
Experimental design is commonly framed as choosing the experiment expected to provide the most information. Un
Species Distribution Modelling (SDM) is essential for understanding how environmental conditions shape biodive
Whisper is a widely used foundation model for automatic speech recognition (ASR), but its generative decoder c
A critical challenge in deploying Large Language Models (LLMs) is developing reliable mechanisms to estimate t
Intent-based networking realization starts by translating high-level intents into low-level network configurat
Information abstraction, which groups strategically similar private states into a tractable number of buckets,
Autonomous underwater robots are widely used for exploration, monitoring, and inspection, where safe navigatio
Object-centric visual representations are important for physical-world perception, but existing visual pretrai
Evaluating physical reasoning in video models is difficult because absolute motion measurements depend on fram
Mobile robotic platforms offer a flexible alternative to fixed manipulators for non-destructive evaluation (ND
Evaluating robot manipulation policies is becoming increasingly important as generalist models, particularly v
Conformal risk control is an emerging framework for the safe deployment of machine learning models with finite
Generative modeling directly on geometric manifolds can avoid errors introduced by flattening non-Euclidean da
Stochastic gradient Markov chain Monte Carlo (SGMCMC) methods enable scalable Bayesian inference, but their pe
Accurate joint encoder offsets are essential for kinematic consistency in humanoid lower limbs, yet existing c
Autonomous lunar missions require real-time per- ception under three coupled constraints: extreme low-light co
Multi-agent LLM systems commonly use an orchestrator to decompose a task for a team of workers and then improv
Post-hoc calibration corrects reported confidence, yet a multiclass calibrator can also change the associated
Model compression techniques such as pruning and quantization facilitate the efficient deployment and accelera
Many statistical models involve parameter-dependent normalizing constants that are computationally intractable
At a junction, a score field can reveal weighted tangent rays, yet these first-order quantities do not determi
Language model agents increasingly propose actions, observe external feedback, and explain their own behavior.
Early recognition of lane-change intention is essential for proactive decision-making in autonomous driving an
We develop a marginal coordinate test for regression with Euclidean predictors and a random-object response in
Machine learning systems are increasingly corrected while they run, and the decision of when to intervene is i
World models have made remarkable progress in action-conditioned future prediction for embodied agents, yet st
Robotic perception from a single viewpoint is often limited by self-occlusion and incomplete surface visibilit
Professional agent tasks often depend on conventions that are absent from public corpora, yet benchmarks rarel
Missing data, measurement error, and population heterogeneity are pervasive challenges in analyzing data arisi
Mobile AI acts as a visual oracle, empowering users to snap a picture of something and ask for information. Sn
We study risk-averse decision making, in which an agent selects actions while being uncertain about the true s
Bayesian inference in compound loss models must often be repeated across policies, market scenarios, and prior
Reliable prediction of time-varying channel state information (CSI) is essential for efficient wireless commun
Recommendation impressions are a finite resource, hence delivering a recommendation to a user who would discov
Marginalized importance weighting evaluates a target policy by reweighting offline state-action samples with i
The embedding dimension of categorical predictors is usually selected through heuristic tuning, although it di
Current LLM agent systems decide delegation before reasoning begins (a router picks a model) or after a respon
Large language models (LLMs) often produce fluent but incorrect answers with unwarranted confidence. A central
One implicit DDIM inversion step is the cheapest probe of whether a pretrained diffusion model encodes local m
A rapidly growing range of sequential data tasks, such as identifying trend reversals in financial markets, au
Calibration requires probabilistic reports to be conditionally unbiased and reliably interpretable as probabil
As an extension of existing Bayesian persuasion framework with inadequate message mechanism, we study direct r
We show with experiments and system-level simulations that it is possible to successfully mitigate the impact
Echo-state networks enable efficient temporal learning by fixing the recurrent dynamics and training only a li