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.
- 2018-01arXiv 1801.01423Overcoming catastrophic forgetting with hard attention to the task
Joan Serrà, Dídac Surís, Marius Miron et al. · Machine learning
Catastrophic forgetting occurs when a neural network loses the information learned in a previous task after training on subsequent tasks. This problem remains a hurdle for artificial intelligence systems with sequential learning capabilities.
- 2017-12arXiv 1712.03847On Quadratic Penalties in Elastic Weight Consolidation
Ferenc Huszár · Machine learning
Elastic weight consolidation (EWC, Kirkpatrick et al, 2017) is a novel algorithm designed to safeguard against catastrophic forgetting in neural networks.
- 2017-11arXiv 1711.09601Memory Aware Synapses: Learning what (not) to forget
Rahaf Aljundi, Francesca Babiloni, Mohamed Elhoseiny et al. · Vision
Humans can learn in a continuous manner. Old rarely utilized knowledge can be overwritten by new incoming information while important, frequently used knowledge is prevented from being erased.
- 2017-11arXiv 1711.05769PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning
Arun Mallya, Svetlana Lazebnik · Vision
This paper presents a method for adding multiple tasks to a single deep neural network while avoiding catastrophic forgetting. Inspired by network pruning techniques, we exploit redundancies in large deep networks to free up parameters that can then be employed to learn new tasks.
- 2017-06arXiv 1706.08840Gradient Episodic Memory for Continual Learning
David Lopez-Paz, Marc'Aurelio Ranzato · Machine learning
One major obstacle towards AI is the poor ability of models to solve new problems quicker, and without forgetting previously acquired knowledge.
- 2017-03arXiv 1703.04200Continual Learning Through Synaptic Intelligence
Friedemann Zenke, Ben Poole, Surya Ganguli · Machine learning
While deep learning has led to remarkable advances across diverse applications, it struggles in domains where the data distribution changes over the course of learning.
- 2016-12arXiv 1612.04844The More You Know: Using Knowledge Graphs for Image Classification
Kenneth Marino, Ruslan Salakhutdinov, Abhinav Gupta · Vision
One characteristic that sets humans apart from modern learning-based computer vision algorithms is the ability to acquire knowledge about the world and use that knowledge to reason about the visual world.
- 2016-12arXiv 1612.00796Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz et al. · Machine learning
The ability to learn tasks in a sequential fashion is crucial to the development of artificial intelligence. Neural networks are not, in general, capable of this and it has been widely thought that catastrophic forgetting is an inevitable feature of connectionist models.
Cited inEWC protects what its Fisher sees: a literature synthesis on elastic weight consolidation, scale and coherenceEWC stiffness freezes stale facts, and no per-fact Fisher ledger can release itStudy: Decision signatures for conflict detectionStudy: Elastic weight consolidation deep diveStudy: Fortifying knowledge in continual learning - 2016-06arXiv 1606.09282Learning without Forgetting
Zhizhong Li, Derek Hoiem · Vision
When building a unified vision system or gradually adding new capabilities to a system, the usual assumption is that training data for all tasks is always available.
- 2016-06arXiv 1606.04671Progressive Neural Networks
Andrei A. Rusu, Neil C. Rabinowitz, Guillaume Desjardins et al. · Machine learning
Learning to solve complex sequences of tasks--while both leveraging transfer and avoiding catastrophic forgetting--remains a key obstacle to achieving human-level intelligence.
- 2015-07arXiv 1507.07580Computational principles of biological memory
Marcus K. Benna, Stefano Fusi · Neuroscience & neural computation
Memories are stored, retained, and recollected through complex, coupled processes operating on multiple timescales.
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.
