NeuroGenesysThe research we build on.
1,514 papers by other researchers that our work cites, from continual learning and verification to language models, agents and hardware. Each entry links to the paper on arXiv and to where we use it. 301 are cited on published pages of this site; the rest in the research notes behind our studies. Our own papers are in the archive.
- 2022-11arXiv 2211.02633A Theoretical Study on Solving Continual Learning
Gyuhak Kim, Changnan Xiao, Tatsuya Konishi et al. · Machine learning
Continual learning (CL) learns a sequence of tasks incrementally. There are two popular CL settings, class incremental learning (CIL) and task incremental learning (TIL).
Cited inThe Capacity AuditInvestors are paying for agents that learn on the job. The hard part is knowing what they forgot.Penalising wrong answers only works if someone else is gradingStudy: Forgetting as selection at scaleStudy: Genesys program prior artInternal notesStudy: Orchestrated specialists and bridge synthesisStudy: Stopping confident wrong expert votes - 2020-10arXiv 2010.03759Energy-based Out-of-distribution Detection
Weitang Liu, Xiaoyun Wang, John D. Owens et al. · Machine learning
Determining whether inputs are out-of-distribution (OOD) is an essential building block for safely deploying machine learning models in the open world. However, previous methods relying on the softmax confidence score suffer from overconfident posterior distributions for OOD data.
- 2020-04arXiv 2004.07211Dark Experience for General Continual Learning: a Strong, Simple Baseline
Pietro Buzzega, Matteo Boschini, Angelo Porrello et al. · Machine learning
Continual Learning has inspired a plethora of approaches and evaluation settings; however, the majority of them overlooks the properties of a practical scenario, where the data stream cannot be shaped as a sequence of tasks and offline training is not viable.
- 2019-05arXiv 1905.13260Large Scale Incremental Learning
Yue Wu, Yinpeng Chen, Lijuan Wang et al. · Vision
Modern machine learning suffers from catastrophic forgetting when learning new classes incrementally. The performance dramatically degrades due to the missing data of old classes.
- 2016-11arXiv 1611.06194Expert Gate: Lifelong Learning with a Network of Experts
Rahaf Aljundi, Punarjay Chakravarty, Tinne Tuytelaars · Vision
In this paper we introduce a model of lifelong learning, based on a Network of Experts. New tasks / experts are learned and added to the model sequentially, building on what was learned before.
Built from every arXiv paper cited in our papers, posts, topic hubs, reports and research notes, with titles, authors, dates and abstracts from the arXiv API (updated 2026-11-12). Summaries are the opening sentences of each paper's own abstract, not our assessment; a citation means we used or checked the work, not that we endorse it. Fields follow each paper's primary arXiv category. Papers cited only outside arXiv (journals, proceedings) are not listed yet. Entries are generated from our citations, so a mistyped ID in a note would list the wrong paper; we remove those as we find them. Report a wrong entry.
