Data General
No mentions found
This entity hasn't been tracked yet, or Iris is still building its knowledge base.
Related Articles from SNS
Mining Useful General Data for Low-Resource Domain Adaptation
arXiv:2511.07380v2 Announce Type: replace Abstract: Adapting large language models (LLMs) to low-resource domains remains challenging due to the scarcity of domain-specific data. While in-domain data is limited, there exists a vast amount of general-domain data that shares similar question-answer formats and reasoning patterns with domain tasks. This observation raises an important question: can useful general-domain data be mined to improve low-resource domain adaptation?
Inconsistency-Aware Minimization: Improving Generalization with Unlabeled Data
Announce Type: new Abstract: Estimating the generalization gap and developing optimization methods that improve generalization are crucial for deep learning models, for both theoretical understanding and practical applications. Leveraging unlabeled data for these purposes offers significant advantages in real-world scenarios. This paper introduces a novel generalization measure, local inconsistency, derived from an information-geometric perspective on the parameter space of neural networks.
FitED: A User-Centric, Extensible Software Environment for Robust Peak-Profile and General Functional Data Fitting
Announce Type: replace Abstract: Reliable parameter extraction from experimental data is essential for quantitative analysis across spectroscopy, diffraction, photoluminescence, chromatography, microscopy, and time-resolved measurements. However, nonlinear fitting often remains difficult to reproduce, especially when complex models, correlated parameters, uncertain derived quantities, and user-dependent fitting choices are involved. We present FitED, a Python-based desktop application for...
FitED: A User-Centric, Extensible Software Environment for Robust Peak-Profile and General Functional Data Fitting
Announce Type: replace-cross Abstract: Reliable parameter extraction from experimental data is essential for quantitative analysis across spectroscopy, diffraction, photoluminescence, chromatography, microscopy, and time-resolved measurements. However, nonlinear fitting often remains difficult to reproduce, especially when complex models, correlated parameters, uncertain derived quantities, and user-dependent fitting choices are involved. We present FitED, a Python-based desktop application...
From inverse problems to neural operators: prediction, mechanism, and generalization of data-driven models
arXiv:2606.08956v1 Announce Type: new Abstract: Scientists have historically relied on mathematical models based on differential equations to relate system inputs -- forces, fluxes, or heat sources -- to outputs, such as displacement, velocity, concentration, and temperature. These models rely on deep domain knowledge to determine the form of the governing differential equation, which is then calibrated with data by solving an inverse problem. In recent years, the field of Scientific Machine...
Generalized Forcing Method: Generation of Diverse Data for Training Linear Transport PDE Closure Models
arXiv:2606.05141v1 Announce Type: new Abstract: Data-driven closure modeling for transport partial differential equations requires training data that are accurate, affordable, diverse, and directly tailored to the target closure fields. We develop the Generalized Forcing Method (GFM), a data-generation framework for training linear transport closure models. GFM generates such data by running simulations with a zero initial condition and an extra body force that is constructed compatibly with...