Search Results for author: Lars Buesing

Found 27 papers, 5 papers with code

Pushing the limits of self-supervised ResNets: Can we outperform supervised learning without labels on ImageNet?

no code implementations13 Jan 2022 Nenad Tomasev, Ioana Bica, Brian McWilliams, Lars Buesing, Razvan Pascanu, Charles Blundell, Jovana Mitrovic

Despite recent progress made by self-supervised methods in representation learning with residual networks, they still underperform supervised learning on the ImageNet classification benchmark, limiting their applicability in performance-critical settings.

Representation Learning Self-Supervised Image Classification +2

Representation Learning via Invariant Causal Mechanisms

no code implementations15 Oct 2020 Jovana Mitrovic, Brian McWilliams, Jacob Walker, Lars Buesing, Charles Blundell

Self-supervised learning has emerged as a strategy to reduce the reliance on costly supervised signal by pretraining representations only using unlabeled data.

Contrastive Learning Out-of-Distribution Generalization +3

Beyond Tabula-Rasa: a Modular Reinforcement Learning Approach for Physically Embedded 3D Sokoban

no code implementations3 Oct 2020 Peter Karkus, Mehdi Mirza, Arthur Guez, Andrew Jaegle, Timothy Lillicrap, Lars Buesing, Nicolas Heess, Theophane Weber

We explore whether integrated tasks like Mujoban can be solved by composing RL modules together in a sense-plan-act hierarchy, where modules have well-defined roles similarly to classic robot architectures.

reinforcement-learning

Pointer Graph Networks

no code implementations NeurIPS 2020 Petar Veličković, Lars Buesing, Matthew C. Overlan, Razvan Pascanu, Oriol Vinyals, Charles Blundell

This static input structure is often informed purely by insight of the machine learning practitioner, and might not be optimal for the actual task the GNN is solving.

Approximate Inference in Discrete Distributions with Monte Carlo Tree Search and Value Functions

no code implementations15 Oct 2019 Lars Buesing, Nicolas Heess, Theophane Weber

A plethora of problems in AI, engineering and the sciences are naturally formalized as inference in discrete probabilistic models.

Decision Making Decision Making Under Uncertainty

Credit Assignment Techniques in Stochastic Computation Graphs

no code implementations7 Jan 2019 Théophane Weber, Nicolas Heess, Lars Buesing, David Silver

Stochastic computation graphs (SCGs) provide a formalism to represent structured optimization problems arising in artificial intelligence, including supervised, unsupervised, and reinforcement learning.

reinforcement-learning

Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search

no code implementations ICLR 2019 Lars Buesing, Theophane Weber, Yori Zwols, Sebastien Racaniere, Arthur Guez, Jean-Baptiste Lespiau, Nicolas Heess

In contrast to off-policy algorithms based on Importance Sampling which re-weight data, CF-GPS leverages a model to explicitly consider alternative outcomes, allowing the algorithm to make better use of experience data.

Temporal Difference Variational Auto-Encoder

1 code implementation ICLR 2019 Karol Gregor, George Papamakarios, Frederic Besse, Lars Buesing, Theophane Weber

To act and plan in complex environments, we posit that agents should have a mental simulator of the world with three characteristics: (a) it should build an abstract state representing the condition of the world; (b) it should form a belief which represents uncertainty on the world; (c) it should go beyond simple step-by-step simulation, and exhibit temporal abstraction.

reinforcement-learning

Learning model-based planning from scratch

1 code implementation19 Jul 2017 Razvan Pascanu, Yujia Li, Oriol Vinyals, Nicolas Heess, Lars Buesing, Sebastien Racanière, David Reichert, Théophane Weber, Daan Wierstra, Peter Battaglia

Here we introduce the "Imagination-based Planner", the first model-based, sequential decision-making agent that can learn to construct, evaluate, and execute plans.

Continuous Control Decision Making

Black box variational inference for state space models

1 code implementation23 Nov 2015 Evan Archer, Il Memming Park, Lars Buesing, John Cunningham, Liam Paninski

These models have the advantage of learning latent structure both from noisy observations and from the temporal ordering in the data, where it is assumed that meaningful correlation structure exists across time.

Time Series Variational Inference

Clustered factor analysis of multineuronal spike data

no code implementations NeurIPS 2014 Lars Buesing, Timothy A. Machado, John P. Cunningham, Liam Paninski

High-dimensional, simultaneous recordings of neural spiking activity are often explored, analyzed and visualized with the help of latent variable or factor models.

Variational Inference

Bayesian Manifold Learning: The Locally Linear Latent Variable Model (LL-LVM)

no code implementations NeurIPS 2015 Mijung Park, Wittawat Jitkrittum, Ahmad Qamar, Zoltan Szabo, Lars Buesing, Maneesh Sahani

We introduce the Locally Linear Latent Variable Model (LL-LVM), a probabilistic model for non-linear manifold discovery that describes a joint distribution over observations, their manifold coordinates and locally linear maps conditioned on a set of neighbourhood relationships.

Inferring neural population dynamics from multiple partial recordings of the same neural circuit

no code implementations NeurIPS 2013 Srini Turaga, Lars Buesing, Adam M. Packer, Henry Dalgleish, Noah Pettit, Michael Hausser, Jakob H. Macke

Simultaneous recordings of the activity of large neural populations are extremely valuable as they can be used to infer the dynamics and interactions of neurons in a local circuit, shedding light on the computations performed.

Spectral learning of linear dynamics from generalised-linear observations with application to neural population data

no code implementations NeurIPS 2012 Lars Buesing, Jakob H. Macke, Maneesh Sahani

Here, we show how spectral learning methods for linear systems with Gaussian observations (usually called subspace identification in this context) can be extended to estimate the parameters of dynamical system models observed through non-Gaussian noise models.

Simplified Rules and Theoretical Analysis for Information Bottleneck Optimization and PCA with Spiking Neurons

no code implementations NeurIPS 2007 Lars Buesing, Wolfgang Maass

We show that under suitable assumptions (primarily linearization) a simple and perspicuous online learning rule for Information Bottleneck optimization with spiking neurons can be derived.

online learning

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