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In neural architecture search (NAS), the space of neural network architectures is automatically explored to maximize predictive accuracy for a given task. Jonathan graduated from Cambridge University with a MPhil in Machine Learning, Speech and Language Technology in 2017. TaskNorm: rethinking batch normalization for meta-learning. 1 larsmaaloee/auxiliary-deep-generative-models. We propose a link between permutation equivariance and compositional generalization, and provide equivariant language models Jonathan Gordon, David Lopez-Paz, Marco Baroni, Diane Bouchecourt. Contact GitHub support about this user’s behavior. Reasoning about parameters is made challenging by the high-dimensionality and over-parameterization of the space. Code to reproduce experiments in "Meta-learning probabilistic inference for prediction", Python

… You can always update your selection by clicking Cookie Preferences at the bottom of the page. Since disease progression is not linear, and the accuracy measurement cannot tell large from small prediction errors, we dispense with the linearity assumption and apply ordinal classification t... Leveraging the wealth of unlabeled data produced in recent years provides great potential for improving supervised models. Learn more about reporting abuse. I am a Ph.D. candidate with the Computational and Biological Learning group at the University of Cambridge, supervised by Dr José Miguel Hernández-Lobato and advised by Dr Richard Turner. Working with Diane Bouchacourt and David Lopez-Paz on modelling symmetries in language tasks. Cambridge CB2 1PZ

This paper develops a general framework for data efficient and versatile deep learning. All rights reserved. A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Email: jg801@eng.cam.ac.uk. 1, Forked from larsmaaloee/auxiliary-deep-generative-models. University students and faculty, institute members, and independent researchers, Technology or product developers, R&D specialists, and government or NGO employees in scientific roles, Health care professionals, including clinical researchers, Journalists, citizen scientists, or anyone interested in reading and discovering research. United Kingdom (map) Take a look at the Seeing something unexpected? PhD in Machine Learning @CambridgeUniversity. Deep generative models for semi-supervised learning.

Jekyll-Mono is a simple and elegant GitHub Profile cum Blog theme based on Barry Clark's Jekyll-Now, Code for BatchNorm for Bayesian Neural Networks.

John Bronskill, Jonathan Gordon, James Requeima, Sebastian Nowozin, and Richard E. Turner. Prevent this user from interacting with your repositories and sending you notifications. Jonathan Gordon Machine Learning PhD Student University of Cambridge. They enable the statistical symmetry of underlying physical phenomena to be leveraged, thereby aiding generalization. Stationary stochastic processes (SPs) are a key component of many probabilistic models, such as those for off-the-grid spatio-temporal data. Profiles; jg801. Created 2 Gordonjo has no activity Jonathan Gordon PHD Student in Machine Learning at University of Cambridge Cherry Hinton, Cambridgeshire, United Kingdom 500+ connections

Learn more. PDF Cite Code Permutation Equivariant Models for Compositional Generalization in Language. We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. Prediction in such models can be viewed as a translation equivariant map from observed data... Specifying a Bayesian prior is notoriously difficult for complex models such as neural networks. yet for this period. Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes, Combining deep generative and discriminative models for Bayesian semi-supervised learning, TaskNorm: Rethinking Batch Normalization for Meta-Learning, Convolutional Conditional Neural Processes, Insights into Amyotrophic Lateral Sclerosis from a Machine Learning Perspective, META-LEARNING PROBABILISTIC INFERENCE FOR PREDICTION, Bayesian Batch Active Learning as Sparse Subset Approximation, Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive Processes, Decision-Theoretic Meta-Learning: Versatile and Efficient Amortization of Few-Shot Learning, Bayesian Semisupervised Learning with Deep Generative Models, Exposing and modeling underlying mechanisms in ALS with machine learning, Machine Learning and Perception Research Group.

The goal of this paper is to design image classification systems that, after an initial multi-task training phase, can automatically adapt to new tasks encountered at test time. Due to our privacy policy, only current members can send messages to people on ResearchGate. Only verified researchers can join ResearchGate and send messages to other members. repository. © 2008-2020 ResearchGate GmbH. He is funded by a Samsung doctoral studies grant and supervised by Dr. José Miguel Hernández-Lobato. Learn more, We use analytics cookies to understand how you use our websites so we can make them better, e.g.

Translation equivariance is an important inductive bias for many learning problems including time series modelling, spatial data, and images. Priors that seem benign and uninformative can have unintuitive and detrimental effects on a model's predictions.
When the cost of acquiring labels is high, probabilistic active learning methods can be used to greedily select the most informative data points to be labeled. Fax: +44 1223 332662, Information provided by: web-editor@eng.cam.ac.uk, Undergraduate teachingGraduate studyResearchCollaborationGiving to the DepartmentEvents and outreachPeopleAlumni RelationsEngineering DiversityIntranet, Undergraduates Overview & COVID Information, Energy, Fluid Mechanics and Turbomachinery, Energy, Transport and Urban Infrastructure, Complex, Resilient and Intelligent Systems, Work Experience at the Department of Engineering, Centre for Languages and Inter-Communication. PhD in Machine Learning at the University of Cambridge - Gordonjo. commits in Actin comets rocket around in cells with genetic defect found in Lowe syndrome [Jonathan Gadsby, Gallop lab] Discussing neural stem cells with the public at the Cambridge Science Festival Probing the role of double-stranded RNA uptake. Department role and responsibilities Raven login; Intranet; Contact. Trumpington Street To avo... Join ResearchGate to find the people and research you need to help your work. Objective: Amyotrophic lateral sclerosis (ALS) disease state prediction usually assumes linear progression and uses a classifier evaluated by its accuracy.

8, Python they're used to gather information about the pages you visit and how many clicks you need to accomplish a task. Jonathan GORDON of University of Cambridge, Cambridge (Cam) | Read 13 publications | Contact Jonathan GORDON In contrast, discriminative models cannot learn from unlabelled data, but tend to outperform their generative counterparts in supervised tasks.
Despite the success of recent approaches, most existing methods cannot be directly applied to large scale problems because of their prohibitive computational complexity or high memory usage.

52 We develop a framework to jointly train deep generative and discrimin... Modern meta-learning approaches for image classification rely on increasingly deep networks to achieve state-of-the-art performance, making batch normalization an essential component of meta-learning pipelines. You signed in with another tab or window. We use essential cookies to perform essential website functions, e.g. In 37th International Conference on Machine Learning.

Telephone: +44 1223 7 48511. Tel: +44 1223 332600 Academic Division: Information Engineering, Research group: Computational and Biological Learning, Engineering Department We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. Dismiss Create your own GitHub profile. Learn more, PhD in Machine Learning at the University of Cambridge. For more information, see our Privacy Statement.

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