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.
- 2019-06arXiv 1906.02530Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift
Yaniv Ovadia, Emily Fertig, Jie Ren et al. · Machine learning
Modern machine learning methods including deep learning have achieved great success in predictive accuracy for supervised learning tasks, but may still fall short in giving useful estimates of their predictive {\em uncertainty}.
- 2019-04arXiv 1904.03178Reducing catastrophic forgetting when evolving neural networks
Joseph Early · Neuroscience & neural computation
A key stepping stone in the development of an artificial general intelligence (a machine that can perform any task), is the production of agents that can perform multiple tasks at once instead of just one.
- 2018-05arXiv 1805.06370Progress & Compress: A scalable framework for continual learning
Jonathan Schwarz, Jelena Luketina, Wojciech M. Czarnecki et al. · Machine learning
We introduce a conceptually simple and scalable framework for continual learning domains where tasks are learned sequentially.
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: Elastic weight consolidation deep diveStudy: Fortifying knowledge in continual learningStudy: Generational competitive continual learning - 2018-05arXiv 1805.05551Knowledge Distillation in Generations: More Tolerant Teachers Educate Better Students
Chenglin Yang, Lingxi Xie, Siyuan Qiao et al. · Vision
We focus on the problem of training a deep neural network in generations. The flowchart is that, in order to optimize the target network (student), another network (teacher) with the same architecture is first trained, and used to provide part of supervision signals in the next stage.
- 2018-05arXiv 1805.04770Born Again Neural Networks
Tommaso Furlanello, Zachary C. Lipton, Michael Tschannen et al. · Machine learning
Knowledge Distillation (KD) consists of transferring âknowledgeâ from one machine learning model (the teacher) to another (the student). Commonly, the teacher is a high-capacity model with formidable performance, while the student is more compact.
- 2018-03arXiv 1803.03453The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities
Joel Lehman, Jeff Clune, Dusan Misevic et al. · Neuroscience & neural computation
Biological evolution provides a creative fount of complex and subtle adaptations, often surprising the scientists who discover them. However, because evolution is an algorithmic process that transcends the substrate in which it occurs, evolution's creativity is not limited to nature.
- 2017-11arXiv 1711.09846Population Based Training of Neural Networks
Max Jaderberg, Valentin Dalibard, Simon Osindero et al. · Machine learning
Neural networks dominate the modern machine learning landscape, but their training and success still suffer from sensitivity to empirical choices of hyperparameters such as model architecture, loss function, and optimisation algorithm.
- 2017-08arXiv 1708.01547Lifelong Learning with Dynamically Expandable Networks
Jaehong Yoon, Eunho Yang, Jeongtae Lee et al. · Machine learning
We propose a novel deep network architecture for lifelong learning which we refer to as Dynamically Expandable Network (DEN), that can dynamically decide its network capacity as it trains on a sequence of tasks, to learn a compact overlapping knowledge sharing structure among tasks.
- 2017-05arXiv 1705.10257Boltzmann Exploration Done Right
Nicolò Cesa-Bianchi, Claudio Gentile, Gábor Lugosi et al. · Machine learning
Boltzmann exploration is a classic strategy for sequential decision-making under uncertainty, and is one of the most standard tools in Reinforcement Learning (RL).
- 2017-01arXiv 1701.08734PathNet: Evolution Channels Gradient Descent in Super Neural Networks
Chrisantha Fernando, Dylan Banarse, Charles Blundell et al. · Neuroscience & neural computation
For artificial general intelligence (AGI) it would be efficient if multiple users trained the same giant neural network, permitting parameter reuse, without catastrophic forgetting.
- 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.
- 2016-09arXiv 1609.09106HyperNetworks
David Ha, Andrew Dai, Quoc V. Le · Machine learning
This work explores hypernetworks: an approach of using a one network, also known as a hypernetwork, to generate the weights for another network.
- 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.
- 2016-05arXiv 1605.03142Self-Modification of Policy and Utility Function in Rational Agents
Tom Everitt, Daniel Filan, Mayank Daswani et al. · AI & agents
Any agent that is part of the environment it interacts with and has versatile actuators (such as arms and fingers), will in principle have the ability to self-modify -- for example by changing its own source code.
- 2011-03arXiv 1103.4601Doubly Robust Policy Evaluation and Learning
Miroslav Dudik, John Langford, Lihong Li · Machine learning
We study decision making in environments where the reward is only partially observed, but can be modeled as a function of an action and an observed context. This setting, known as contextual bandits, encompasses a wide variety of applications including health-care policy and Internet advertising.
- 2010-03arXiv 1003.5956Unbiased Offline Evaluation of Contextual-bandit-based News Article Recommendation Algorithms
Lihong Li, Wei Chu, John Langford et al. · Machine learning
Contextual bandit algorithms have become popular for online recommendation systems such as Digg, Yahoo! \emph{Offline} evaluation of the effectiveness of new algorithms in these applications is critical for protecting online user experiences but very challenging due to their "partial-label" nature.
- 2010-02arXiv 1003.0146A Contextual-Bandit Approach to Personalized News Article Recommendation
Lihong Li, Wei Chu, John Langford et al. · Presented at the Nineteenth International Conference on World Wide Web (WWW 2010), Raleigh, NC, USA, 2010 · Machine learning
Personalized web services strive to adapt their services (advertisements, news articles, etc) to individual users by making use of both content and user information.
- 2008-05arXiv 0805.3415On Upper-Confidence Bound Policies for Non-Stationary Bandit Problems
Aurélien Garivier, Eric Moulines · Statistics & theory
Multi-armed bandit problems are considered as a paradigm of the trade-off between exploring the environment to find profitable actions and exploiting what is already known.
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.
