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29 results for “Continual Learning”

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zenodo32/100

ViLCo: VIdeo Language COntinual learning Benchmark

<p>We introduce the first <strong>VIdeo Language COntinual learning Benchmark (ViLCo-Bench). </strong>Video language continual learning involves continuously adapting to information from video and text inputs, enhancing a model&rsquo;s ability to handle new tasks while retaining prior knowledge. This field is a relatively under-explored area, and establishing appropriate datasets is crucial for facilitating communication and research in this field. In this study, we present the first dedicated benchmark, ViLCo-Bench, designed to evaluate continual learning models across a range of video-text tasks. The dataset comprises ten-minute-long videos and corresponding language queries collected from publicly available datasets.</p> <p>Additionally, we introduce a novel memory-efficient framework that incorporates self-supervised learning and mimics long-term and short-term memory effects. This framework addresses challenges including memory complexity from long video clips, natural language complexity from open queries, and text-video misalignment. We posit that ViLCo-Bench, with greater complexity compared to existing continual learning benchmarks, would serve as a critical tool for exploring the video-language domain, extending beyond conventional class-incremental tasks, and addressing complex and limited annotation issues.</p> <p>More detailed information can also be found on our url: https://github.com/cruiseresearchgroup/ViLCo</p>

opencc-by-4.0Jun 2024View details →
ClinicalTrials.gov32/100

Web-Based Intervention Designed to Educate and Improve Adherence Through Learning to Use Continuous Glucose Monitoring

ClinicalTrials.gov study NCT03367351. IPD Sharing: NO. Countries: 1. Publications: 1.

closedIPD-NOFeb 2026View details →
dryad32/100

Data from: Learning from the past to prepare for the future: felids face continued threat from declining prey richness

Open the record for dataset details and reuse information.

publicApr 2017View details →
zenodo28/100

Impact of Usability on Continuance Usage Intention in Language Learning Apps with Gamification Features

Open the record for dataset details and reuse information.

opencc-by-4.0Aug 2024View details →
ClinicalTrials.gov28/100

Does the Continuous Repetition of Motor Skills at a Consistent Speed With Music Affect Children's Decision-Making and Learning Abilities?

ClinicalTrials.gov study NCT07147686. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.

restrictedIPD-UNDECIDEDFeb 2026View details →
ClinicalTrials.gov24/100

A Study to Learn How Well a Monitoring System Called Continuous Glucose Monitoring (CGM) Which Measures Glucose on an Ongoing Basis Works and How Safe it is in Chinese Patients in Usual Practice

ClinicalTrials.gov study NCT05038761. IPD Sharing: NO. Countries: 1. Publications: 0.

closedIPD-NOFeb 2026View details →
ClinicalTrials.gov20/100

A Machine Learning Approach to Continuous Vital Sign Data Analysis

ClinicalTrials.gov study NCT01448161. IPD Sharing: Not stated. Countries: 0. Publications: 0.

restrictedIPD-UNDECIDEDFeb 2026View details →
zenodo12/100

Revisiting Machine Learning based Test Case Prioritization for Continuous Integration

<p>Aborted version</p>

restrictedAug 2022View details →
zenodo8/100

Revisiting Machine Learning based Test Case Prioritization for Continuous Integration

<p>This repository contains a replication package for a research paper submitted to the 45th International Conference on Software Engineering (https://conf.researchr.org/home/icse-2023). We provide our code, data, and result for the ease of replicating our experiments.</p> <p><strong>Code</strong></p> <ul> <li>In the&nbsp;<strong>collect_data</strong>&nbsp;subdirectory, scripts for constructing TCP datasets are provided. We do dependency analysis using Understand (https://www.scitools.com/), so please download the related tools in advance.</li> <li>In the<strong>&nbsp;rl</strong>&nbsp;subdirectory, we provide the python implementation for algorithms RL, COLEMAN, PPO2-PO, ACER-PA, PPO1-LI.</li> <li>In the&nbsp;<strong>supervised_learning</strong>&nbsp;subdirectory, we provide implementations for MART, RankNet, RankBoost, CA, L-MART, which mainly rely on Ranklib&nbsp;(https://sourceforge.net/p/lemur/wiki/RankLib/.). We also provide implementation for DeepOrder.</li> </ul> <p><strong>Data</strong></p> <ul> <li>The&nbsp;<strong>origin</strong>&nbsp;subdirectory contains the original datasets collected from github using our scripts, including 11 projects.</li> <li>The&nbsp;<strong>smote</strong>&nbsp;subdirectory contains the datasets pre-processed by SMOTE.</li> </ul> <p><strong>Result</strong></p> <ul> <li>Results for&nbsp;<strong>RQ1</strong>,&nbsp;<strong>RQ2</strong>,&nbsp;<strong>RQ3</strong>, and&nbsp;<strong>threats to validity</strong>&nbsp;are provided in the corresponding subdirectories. Scripts for plotting figures are also provided.</li> </ul> <p><strong>Reference</strong></p> <p>We adopt code from previous work</p> <p>Learning-to-Rank vs Ranking-to-Learn: Strategies for Regression Testing in Continuous Integration&nbsp;(https://dl.acm.org/doi/abs/10.1145/3377811.3380369) Github repository: https://github.com/icse20/RT-CI</p> <p>Reinforcement Learning for Test Case Prioritization&nbsp;(https://ieeexplore.ieee.org/abstract/document/9394799) Github repository: https://github.com/moji1/tp_rl</p> <p>DeepOrder: Deep Learning for Test Case Prioritization in Continuous Integration Testing&nbsp;(https://ieeexplore.ieee.org/abstract/document/9609187) Github repository: https://github.com/AizazSharif/DeepOrder-ICSME21</p> <p>A Multi-Armed Bandit Approach for Test Case Prioritization in Continuous Integration Environments&nbsp;(https://ieeexplore.ieee.org/abstract/document/9086053) Github repository: https://github.com/jacksonpradolima/coleman4hcs</p>

restrictedAug 2022View details →

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Allen Brain Atlas

Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

Annotated Behaviour and Observability Dataset (ABODe)

ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

DANDI Archive for NWB datasets

DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

International Brain Laboratory public data

The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

OpenNeuro

OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record