On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

169,051 papers · 148 categories

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12 results for “cross-dataset”

An Extensive Study on Cross-Dataset Bias and Evaluation Metrics Interpretation for Machine Learning applied to Gastrointestinal Tract Abnormality Classificationcs.LG

Study improves machine learning models for GI tract disease detection using comprehensive evaluations and cross-dataset testing.

problem Incomplete or incorrect evaluation of machine learning models for GI tract diseases.

method Comprehensive evaluations of five machine learning models using Global Features and Deep Neural Networks, introducing performance hexagons and cross-dataset testing.

result Demonstrates the need for more sophisticated performance metrics and evaluation methods to build generalizable models.

machine learning GI tract diseases evaluation metrics cross-dataset testing performance hexagons

2020-05-08abs ↗pdf ↗

RECOVAR: Representation Covariances on Deep Latent Spaces for Seismic Event Detectioncs.LG

Unsupervised method detects earthquakes from raw waveforms, generalizing across datasets.

problem Lack of labeled data for earthquake detection.

method Uses deep autoencoders with cross-covariance triggering at bottleneck.

result Performance comparable to supervised methods, with strong cross-dataset generalization.

earthquake detection unsupervised learning deep autoencoders cross-covariance cross-dataset generalization

2024-07-25abs ↗pdf ↗

Statistical Embeddings for Similarity, Retrieval, and Interpretable Alignment of Numeric Tabular Datasetscs.LG

Method embeds numeric tabular datasets into a shared vector space for similarity and retrieval.

problem Lack of meaningful representation for numeric tabular datasets in large language models.

method Structured exploratory data analysis descriptors, sentence transformer embedding, CCA for cross-dataset alignment.

result Total P@1 score of 0.9 across 15 datasets, robust nearest-neighbor retrieval and cluster structure.

numeric tabular datasets embeddings cross-dataset alignment statistical descriptors sentence transformer

2026-05-28abs ↗pdf ↗

Improving Cross-Corpus Speech Emotion Recognition with Adversarial Discriminative Domain Generalization (ADDoG)cs.LG

ADDoG improves cross-dataset speech emotion recognition.

problem Cross-dataset speech emotion recognition failure.

method ADDoG uses an iterative approach to move representations closer together across datasets.

result ADDoG and MADDoG improve cross-dataset speech emotion recognition.

speech emotion recognition cross-dataset adversarial domain generalization multiclass

2019-03-28abs ↗pdf ↗

MusPy: A Toolkit for Symbolic Music Generationcs.SD

MusPy is a toolkit for symbolic music generation, providing tools for dataset management and analysis.

problem Facilitating the creation and analysis of symbolic music datasets.

method Development of an open-source Python library (MusPy) with features for dataset management, data I/O, preprocessing, and model evaluation. Demonstrated through statistical analysis and cross-dataset generalizability experiments.

result MusPy's dataset analysis reveals varying degrees of cross-genre representation across different music datasets.

symbolic music music generation dataset management cross-dataset generalizability statistical analysis

2020-08-05abs ↗pdf ↗

Demystifying Spectral Bias on Real-World Datastat.ML

Study reveals how spectral bias affects learnability on real-world data.

problem Understanding how well complex datasets can be learned using kernel methods.

method Use eigenvalues and eigenfunctions from idealized data to reveal spectral bias on real-world data.

result Bound learnability on real-world data using symmetries of realistic kernels.

spectral bias kernel methods learnability real-world data eigenvalues

2024-06-04abs ↗pdf ↗

Beyond In-Distribution Performance: A Cross-Dataset Study of Trajectory Prediction Robustnesscs.LG

Study shows different trajectory prediction models generalize better under OoD conditions.

problem Comparing trajectory prediction models' robustness across different datasets.

method Training models on Argoverse 2 and testing on Waymo Open Motion, and vice versa, with various augmentation strategies.

result Smallest model with highest inductive bias performs best in OoD generalization.

trajectory prediction generalization out-of-distribution model robustness data augmentation

2025-01-27abs ↗pdf ↗

Chameleon: Learning Model Initializations Across Tasks With Different Schemascs.LG

Chameleon learns model initializations across tasks with different schemas.

problem Meta-learning parameter initialization across tasks with varying predictor schemas.

method Chameleon aligns different predictor schemas to a common representation.

result Chameleon successfully learns parameter initializations across tasks with different schemas.

model initialization meta-learning few-shot learning schema alignment neural networks

2019-09-30abs ↗pdf ↗

A Problem-Oriented Taxonomy of Evaluation Metrics for Time Series Anomaly Detectioncs.AI

Study categorizes time series anomaly detection metrics based on evaluation challenges.

problem Challenges in evaluating time series anomaly detection due to diverse application objectives and metric assumptions.

method Problem-oriented framework categorizing metrics into six dimensions based on evaluation challenges.

result Quantifies each metric's discriminative ability and reveals limitations of widely used metrics.

time series anomaly detection evaluation metrics evaluation challenges IoT applications anomaly detection

2025-11-24abs ↗pdf ↗

Domain Adaptive Transfer Attack (DATA)-based Segmentation Networks for Building Extraction from Aerial Imagescs.CV

Paper proposes a method to improve building extraction from aerial images by adapting CNN models.

problem Limited generalization of CNN-based segmentation models for unseen images.

method Combines domain transfer and adversarial attack concepts to adapt input images to target images.

result Improves overall IoU and outperforms other methods in cross-dataset experiments.

semantic segmentation CNNs adversarial attack domain adaptation building extraction

2020-04-11abs ↗pdf ↗

Scaling Laws for Classical Machine Learning on Tabular Data: A Benchmark Studycs.LG

Students replicated classical ML learning curves on 18 datasets, finding power laws with tree ensembles dominating.

problem Scaling laws for classical machine learning on tabular data.

method Distributed replication of 11,536 training runs on 18 datasets using 6 model families, fitting power-law curves.

result Tree ensembles dominate, and power laws fit well (R^2 > 0.8 on 77.7% of cells).

machine learning tabular data learning curves scaling laws power laws

2026-07-23abs ↗pdf ↗

Donor-Aware scRNA-seq Benchmarks for IBD Classificationq-bio.QM

Donor-aware scRNA-seq benchmarks improve classification accuracy in inflammatory bowel disease.

problem Influenza disease classification from scRNA-seq data is prone to donor-level confounding.

method Developed and evaluated three feature representations across two IBD cohorts.

result Compartment-stratified CLR composition and GatedStructuralCFN embeddings outperform linear models in classification accuracy.

scRNA-seq IBD Donor-aware Cross-validation Feature embeddings

2026-05-05abs ↗pdf ↗