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.
- 2019-04arXiv 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.
- 2019-04arXiv 1904.01685Measuring Calibration in Deep Learning
Jeremy Nixon, Mike Dusenberry, Ghassen Jerfel et al. · Machine learning
Overconfidence and underconfidence in machine learning classifiers is measured by calibration: the degree to which the probabilities predicted for each class match the accuracy of the classifier on that prediction.
- 2019-03arXiv 1903.08254Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables
Kate Rakelly, Aurick Zhou, Deirdre Quillen et al. · Machine learning
Deep reinforcement learning algorithms require large amounts of experience to learn an individual task. While in principle meta-reinforcement learning (meta-RL) algorithms enable agents to learn new skills from small amounts of experience, several major challenges preclude their practicality.
Cited inStudy: Genesys program prior art - 2019-03arXiv 1903.07756Hierarchical Routing Mixture of Experts
Wenbo Zhao, Yang Gao, Shahan Ali Memon et al. · Machine learning
In regression tasks the distribution of the data is often too complex to be fitted by a single model. In contrast, partition-based models are developed where data is divided and fitted by local models.
- 2019-03arXiv 1903.04684The limits of distribution-free conditional predictive inference
Rina Foygel Barber, Emmanuel J. Candès, Aaditya Ramdas et al. · Statistics & theory
We consider the problem of distribution-free predictive inference, with the goal of producing predictive coverage guarantees that hold conditionally rather than marginally.
- 2019-03arXiv 1903.00742Autocurricula and the Emergence of Innovation from Social Interaction: A Manifesto for Multi-Agent Intelligence Research
Joel Z. Leibo, Edward Hughes, Marc Lanctot et al. · AI & agents
Evolution has produced a multi-scale mosaic of interacting adaptive units. Innovations arise when perturbations push parts of the system away from stable equilibria into new regimes where previously well-adapted solutions no longer work.
- 2019-02arXiv 1902.10197RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space
Zhiqing Sun, Zhi-Hong Deng, Jian-Yun Nie et al. · Machine learning
We study the problem of learning representations of entities and relations in knowledge graphs for predicting missing links. The success of such a task heavily relies on the ability of modeling and inferring the patterns of (or between) the relations.
- 2019-02arXiv 1902.03393Improved Knowledge Distillation via Teacher Assistant
Seyed-Iman Mirzadeh, Mehrdad Farajtabar, Ang Li et al. · Machine learning
Despite the fact that deep neural networks are powerful models and achieve appealing results on many tasks, they are too large to be deployed on edge devices like smartphones or embedded sensor nodes.
- 2019-02arXiv 1902.00751Parameter-Efficient Transfer Learning for NLP
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski et al. · Machine learning
Fine-tuning large pre-trained models is an effective transfer mechanism in NLP. However, in the presence of many downstream tasks, fine-tuning is parameter inefficient: an entire new model is required for every task.
- 2019-01arXiv 1901.11356Functional Regularisation for Continual Learning with Gaussian Processes
Michalis K. Titsias, Jonathan Schwarz, Alexander G. de G. Matthews et al. · Machine learning
We introduce a framework for Continual Learning (CL) based on Bayesian inference over the function space rather than the parameters of a deep neural network.
- 2019-01arXiv 1901.09192SelectiveNet: A Deep Neural Network with an Integrated Reject Option
Yonatan Geifman, Ran El-Yaniv · Machine learning
We consider the problem of selective prediction (also known as reject option) in deep neural networks, and introduce SelectiveNet, a deep neural architecture with an integrated reject option.
- 2019-01arXiv 1901.01753Paired Open-Ended Trailblazer (POET): Endlessly Generating Increasingly Complex and Diverse Learning Environments and Their Solutions
Rui Wang, Joel Lehman, Jeff Clune et al. · Neuroscience & neural computation
While the history of machine learning so far largely encompasses a series of problems posed by researchers and algorithms that learn their solutions, an important question is whether the problems themselves can be generated by the algorithm at the same time as they are being solved.
- 2018-12arXiv 1812.07671Deep Online Learning via Meta-Learning: Continual Adaptation for Model-Based RL
Anusha Nagabandi, Chelsea Finn, Sergey Levine · Machine learning
Humans and animals can learn complex predictive models that allow them to accurately and reliably reason about real-world phenomena, and they can adapt such models extremely quickly in the face of unexpected changes.
Cited inStudy: Genesys program prior art - 2018-12arXiv 1812.05720Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problem
Matthias Hein, Maksym Andriushchenko, Julian Bitterwolf · Machine learning
Classifiers used in the wild, in particular for safety-critical systems, should not only have good generalization properties but also should know when they don't know, in particular make low confidence predictions far away from the training data.
- 2018-12arXiv 1812.04606Deep Anomaly Detection with Outlier Exposure
Dan Hendrycks, Mantas Mazeika, Thomas Dietterich · Machine learning
It is important to detect anomalous inputs when deploying machine learning systems. The use of larger and more complex inputs in deep learning magnifies the difficulty of distinguishing between anomalous and in-distribution examples.
- 2018-12arXiv 1812.00420Efficient Lifelong Learning with A-GEM
Arslan Chaudhry, Marc'Aurelio Ranzato, Marcus Rohrbach et al. · Machine learning
In lifelong learning, the learner is presented with a sequence of tasks, incrementally building a data-driven prior which may be leveraged to speed up learning of a new task.
- 2018-11arXiv 1811.02524Solving SAT and MaxSAT with a Quantum Annealer: Foundations, Encodings, and Preliminary Results
Zhengbing Bian, Fabian Chudak, William Macready et al. · Hardware, photonics & physics
Quantum annealers (QAs) are specialized quantum computers that minimize objective functions over discrete variables by physically exploiting quantum effects. Current QA platforms allow for the optimization of quadratic objectives defined over binary variables (qubits), also known as Ising problems.
- 2018-11arXiv 1811.00217META-DES.Oracle: Meta-learning and feature selection for ensemble selection
Rafael M. O Cruz, Robert Sabourin, George D. C. Cavalcanti · Volume 38, November 2017, Pages 84-103 · Machine learning
The key issue in Dynamic Ensemble Selection (DES) is defining a suitable criterion for calculating the classifiers' competence. There are several criteria available to measure the level of competence of base classifiers, such as local accuracy estimates and ranking.
- 2018-11arXiv 1811.01743On Meta-Learning for Dynamic Ensemble Selection
Rafael M. O. Cruz, Robert Sabourin, George D. C. Cavalcanti · Published on the International Conference on Pattern Recognition (ICPR), 2014, pp. 1230-1235 · Machine learning
In this paper, we propose a novel dynamic ensemble selection framework using meta-learning. The framework is divided into three steps. In the first step, the pool of classifiers is generated from the training data.
- 2018-10arXiv 1810.08240Time-uniform, nonparametric, nonasymptotic confidence sequences
Steven R. Howard, Aaditya Ramdas, Jon McAuliffe et al. · Ann. Statist. 49(2): 1055-1080 (April 2021) · Statistics & theory
A confidence sequence is a sequence of confidence intervals that is uniformly valid over an unbounded time horizon. Our work develops confidence sequences whose widths go to zero, with nonasymptotic coverage guarantees under nonparametric conditions.
- 2018-09arXiv 1810.01270META-DES: A Dynamic Ensemble Selection Framework using Meta-Learning
Rafael M. O. Cruz, Robert Sabourin, George D. C. Cavalcanti et al. · Pattern Recognition Volume 48, Issue 5, Pages 1925-1935 · Machine learning
Dynamic ensemble selection systems work by estimating the level of competence of each classifier from a pool of classifiers. Only the most competent ones are selected to classify a given test sample.
- 2018-09arXiv 1810.01256Continual Learning of Context-dependent Processing in Neural Networks
Guanxiong Zeng, Yang Chen, Bo Cui et al. · Machine learning
Deep neural networks (DNNs) are powerful tools in learning sophisticated but fixed mapping rules between inputs and outputs, thereby limiting their application in more complex and dynamic situations in which the mapping rules are not kept the same but changing according to different contexts.
- 2018-06arXiv 1806.07572Neural Tangent Kernel: Convergence and Generalization in Neural Networks
Arthur Jacot, Franck Gabriel, Clément Hongler · In Advances in neural information processing systems (pp. 8571-8580) 2018 · Machine learning
At initialization, artificial neural networks (ANNs) are equivalent to Gaussian processes in the infinite-width limit, thus connecting them to kernel methods. a new kernel: the Neural Tangent Kernel (NTK).
- 2018-05arXiv 1805.08206Bias-Reduced Uncertainty Estimation for Deep Neural Classifiers
Yonatan Geifman, Guy Uziel, Ran El-Yaniv · Machine learning
We consider the problem of uncertainty estimation in the context of (non-Bayesian) deep neural classification. In this context, all known methods are based on extracting uncertainty signals from a trained network optimized to solve the classification problem at hand.
- 2018-05arXiv 1805.07810Online Structured Laplace Approximations For Overcoming Catastrophic Forgetting
Hippolyt Ritter, Aleksandar Botev, David Barber · Machine learning
We introduce the Kronecker factored online Laplace approximation for overcoming catastrophic forgetting in neural networks.
- 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.
- 2018-05arXiv 1805.03643Learning to Teach
Yang Fan, Fei Tian, Tao Qin et al. · Machine learning
Teaching plays a very important role in our society, by spreading human knowledge and educating our next generations. A good teacher will select appropriate teaching materials, impact suitable methodologies, and set up targeted examinations, according to the learning behaviors of the students.
- 2018-03arXiv 1803.10122World Models
David Ha, Jürgen Schmidhuber · Machine learning
We explore building generative neural network models of popular reinforcement learning environments. Our world model can be trained quickly in an unsupervised manner to learn a compressed spatial and temporal representation of the environment.
- 2018-03arXiv 1803.04585Categorizing Variants of Goodhart's Law
David Manheim, Scott Garrabrant · AI & agents
There are several distinct failure modes for overoptimization of systems on the basis of metrics. This occurs when a metric which can be used to improve a system is used to an extent that further optimization is ineffective or harmful, and is sometimes termed Goodhart's Law.
- 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.
- 2018-02arXiv 1802.07239Continual Reinforcement Learning with Complex Synapses
Christos Kaplanis, Murray Shanahan, Claudia Clopath · AI & agents
Unlike humans, who are capable of continual learning over their lifetimes, artificial neural networks have long been known to suffer from a phenomenon known as catastrophic forgetting, whereby new learning can lead to abrupt erasure of previously acquired knowledge.
- 2018-02arXiv 1802.07329Bayesian Incremental Learning for Deep Neural Networks
Max Kochurov, Timur Garipov, Dmitry Podoprikhin et al. · Machine learning
In industrial machine learning pipelines, data often arrive in parts. Particularly in the case of deep neural networks, it may be too expensive to train the model from scratch each time, so one would rather use a previously learned model and the new data to improve performance.
- 2018-02arXiv 1802.02950Rotate your Networks: Better Weight Consolidation and Less Catastrophic Forgetting
Xialei Liu, Marc Masana, Luis Herranz et al. · Vision
In this paper we propose an approach to avoiding catastrophic forgetting in sequential task learning scenarios. Our technique is based on a network reparameterization that approximately diagonalizes the Fisher Information Matrix of the network parameters.
- 2018-02arXiv 1802.01569Alleviating catastrophic forgetting using context-dependent gating and synaptic stabilization
Nicolas Y. Masse, Gregory D. Grant, David J. Freedman · Proceedings of the National Academy of Sciences, 115(44), E10467-E10475 · Machine learning
Humans and most animals can learn new tasks without forgetting old ones. However, training artificial neural networks (ANNs) on new tasks typically cause it to forget previously learned tasks.
- 2018-01arXiv 1801.10112Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence
Arslan Chaudhry, Puneet K. Dokania, Thalaiyasingam Ajanthan et al. · Vision
Incremental learning (IL) has received a lot of attention recently, however, the literature lacks a precise problem definition, proper evaluation settings, and metrics tailored specifically for the IL problem.
- 2018-01arXiv 1801.06519Piggyback: Adapting a Single Network to Multiple Tasks by Learning to Mask Weights
Arun Mallya, Dillon Davis, Svetlana Lazebnik · Vision
This work presents a method for adapting a single, fixed deep neural network to multiple tasks without affecting performance on already learned tasks.
- 2018-01arXiv 1801.05927An Overview of Machine Teaching
Xiaojin Zhu, Adish Singla, Sandra Zilles et al. · Machine learning
In this paper we try to organize machine teaching as a coherent set of ideas. Each idea is presented as varying along a dimension. The collection of dimensions then form the problem space of machine teaching, such that existing teaching problems can be characterized in this space.
- 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.
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.
