Online gas classification at the edge
Field applications in chemical sensing, from portable safety monitors to mobile robots, require models that are lightweight, fast to respond, and can be recalibrated from limited labelled data.…
deep-learning
MLINFO COLLECTION
製造業の異常検知
センサ時系列、画像検査、予測保全、異常スコアリングに使えるAI技術を整理します。
記事一覧
9 / 37RIDAR: Safeguarding Research Integrity in Autonomous Research Agents via Automated Detection and Runtime Intervention
Autonomous research agents are increasingly used to automate large portions of scientific research, yet they are prone to research integrity violations (RIVs): behaviors that compromise the validity,…
machine-learning
Program-Verified Self-Evolution for Vision-Language Models
Self-evolving vision-language models train on questions they generate from unlabeled images. Since these questions have no gold answers, prior methods label them by majority vote over sampled answers…
computer-vision
Seeing Through Distractions: Context-Stable Attribution via the Least Core
Shapley-value-based explanations are widely used for feature attribution in machine learning. However, in computer vision, a feature may reduce the target-class score when added to the complete…
nlp
Recertifying decisions via e-closure: uniform improvements of conformal risk control and randomized e-BH
Statistical procedures often control expected loss at a prescribed level while leaving part of their risk budget unused. Building on the e-value representation of risk-controlled decisions and the…
computer-vision
Conformal Prediction with Stable Measurement Mechanisms
In applications such as air-quality monitoring, inexpensive sensor readings can help predict pollutant concentrations measured by a separate reference instrument. We study how to adapt conformal…
nlp
Adaptive Scenario Generation for KL-DRO Around a Learned Density Model
Distributionally robust optimization (DRO) hedges data-driven two-stage stochastic programs against error in the distribution estimated from a finite record, and standard ambiguity sets differ in how…
computer-vision
From extreme weather to agricultural consequences with agentic AI
Extreme weather is forecast days ahead, yet its consequences for agriculture remain hard to predict: a forecast says where a storm will strike, not which crops will suffer or how much is at risk, and…
computer-vision
Testimony from Timelines: Evidence-Grounded Significance Testing of Temporal Clinical Hypotheses
Ask a language model whether sales tend to dip after a product recall, or whether one side effect tends to follow another after a vaccine, and it answers in a fluent sentence that reads like a…
computer-vision
The Precision Wall: An Optimizer-Dependent Boundary for Optimization under Power-Law Measurement Spectra
In low-rank matrix sensing of a rank-$D$ factor $U^\ast \in \mathbb{R}^{V \times D}$, replacing isotropic Gaussian designs with power-law measurement covariance $Σ= \mathrm{diag}(j^{-s})_{j=1}^V$…
deep-learning
Rural Readiness: Domain Generalization with Adversarially Chosen Domains
Predictive models trained at well-resourced data-rich institutions often fail when deployed in new populations. Motivated by the goal of building clinical diagnosis and decision-making algorithms for…
computer-vision
Prefix Executability: Evaluating Tool-Using Agents Beyond Final Success
Tool-use benchmarks often score agents by final task success, but terminal outcomes can obscure whether the intermediate tool-call trajectory was executable. We propose prefix executability as a…
computer-vision
Adaptive search with path-constrained mobile sensors using multi-arm bandits
We study a constrained sequential decision problem for detecting regions of interest from noisy online measurements collected by a team of energy-constrained mobile sensors. The sensor motion is…
math-theory
Optimal Path Planning with Localization constraints
This paper studies UAV path planning in areas where GPS signals are not present. Because these drones can easily get lost when they don't have GPS, they must rely on dead reckoning and occasionally…
reinforcement-learning
Finite-Sample LTI System Identification from a Misspecified Finite Model Set
We consider the problem of finite-sample identification of an unknown LTI system from a finite hypothesis class. In contrast to existing results that assume the hypothesis class contains the true…
computer-vision
Hammering with Vampire in Lean
We present Vampire-Hammer, a hammer for the Lean interactive theorem prover that discharges goals using the Vampire automated theorem prover and reconstructs proofs checked by the Lean kernel.…
computer-vision
Beyond CCT: Multi-channel SPD recovery for per-shot camera color correction
Modern cameras correct color via two or three pre-calibrated 3x3 matrices interpolated by Correlated Color Temperature (CCT). This collapses the illuminant SPD to a scalar.
computer-vision
Learning a Motion Manifold to Constrain Ambiguity in Unsupervised 4D Medical Image Interpolation
Unsupervised 4D medical image interpolation reconstructs intermediate anatomical states from temporally sparse observations without intermediate supervision. Distinct motion fields may attain…
computer-vision
Hybrid AI Patterns: Thirteen Ways to Couple Language Models with Rules, and How to Choose Among Them
Language models handle text well, and rule engines give conclusions with proofs. Systems that combine the two are usually described by example, and the same coupling turns up under different names in…
computer-vision
Human--AI Interactions as First-Class Data: A Formal Interaction Model and Runtime for Explainable AI Collaboration
AI assistants are increasingly embedded in computational notebooks, scientific workflows, software engineering tools, and learning systems, yet human-AI interactions are often treated as transient…
reinforcement-learning
BiHashFormer: Hash-Driven Dual-Branch Transformer for Efficient Object Detection in HRW Shots
Recent advances in gigapixel-level imaging have brought High-Resolution Wide shots to the forefront of research. However, these images present significant challenges: extreme sparsity of foreground,…
deep-learning
Exploring the Potential of Symbolic Regression in Precision Livestock Farming
The ability to predict farrowing time in pigs is crucial for optimal farm management and ensuring piglet survival. This study investigates the potential of explainable artificial intelligence for…
deep-learning
Optimal Contextual Pricing in Non-Stationary Environments
We study contextual dynamic pricing in a non-stationary environment. In each round, a seller observes a buyer's context, posts a price, and receives only binary purchase feedback.
nlp
Multi-output LSTM soft sensors for industrial anaerobic co-digestion using LSTM-beta-VAE generative data augmentation
Anaerobic digestion (AD) requires close monitoring of key process variables, including pH, the ratio of volatile organic acids to alkaline buffer capacity and biogas production, to maintain…
deep-learning