Search Results for author: Nicolas Heess

Found 96 papers, 34 papers with code

CoMic: Co-Training and Mimicry for Reusable Skills

no code implementations ICML 2020 Leonard Hasenclever, Fabio Pardo, Raia Hadsell, Nicolas Heess, Josh Merel

Finally we show that it is possible to interleave the motion capture tracking with training on complementary tasks, enriching the resulting skill space, and enabling the reuse of skills not well covered by the motion capture data such as getting up from the ground or catching a ball.

Continuous Control Motion Capture

Is Curiosity All You Need? On the Utility of Emergent Behaviours from Curious Exploration

no code implementations17 Sep 2021 Oliver Groth, Markus Wulfmeier, Giulia Vezzani, Vibhavari Dasagi, Tim Hertweck, Roland Hafner, Nicolas Heess, Martin Riedmiller

Curiosity-based reward schemes can present powerful exploration mechanisms which facilitate the discovery of solutions for complex, sparse or long-horizon tasks.

Collect & Infer -- a fresh look at data-efficient Reinforcement Learning

no code implementations23 Aug 2021 Martin Riedmiller, Jost Tobias Springenberg, Roland Hafner, Nicolas Heess

This position paper proposes a fresh look at Reinforcement Learning (RL) from the perspective of data-efficiency.

From Motor Control to Team Play in Simulated Humanoid Football

no code implementations25 May 2021 SiQi Liu, Guy Lever, Zhe Wang, Josh Merel, S. M. Ali Eslami, Daniel Hennes, Wojciech M. Czarnecki, Yuval Tassa, Shayegan Omidshafiei, Abbas Abdolmaleki, Noah Y. Siegel, Leonard Hasenclever, Luke Marris, Saran Tunyasuvunakool, H. Francis Song, Markus Wulfmeier, Paul Muller, Tuomas Haarnoja, Brendan D. Tracey, Karl Tuyls, Thore Graepel, Nicolas Heess

In a sequence of stages, players first learn to control a fully articulated body to perform realistic, human-like movements such as running and turning; they then acquire mid-level football skills such as dribbling and shooting; finally, they develop awareness of others and play as a team, bridging the gap between low-level motor control at a timescale of milliseconds, and coordinated goal-directed behaviour as a team at the timescale of tens of seconds.

Decision Making Imitation Learning +2

Neural Production Systems

no code implementations2 Mar 2021 Anirudh Goyal, Aniket Didolkar, Nan Rosemary Ke, Charles Blundell, Philippe Beaudoin, Nicolas Heess, Michael Mozer, Yoshua Bengio

First, GNNs do not predispose interactions to be sparse, as relationships among independent entities are likely to be.

Explicit Pareto Front Optimization for Constrained Reinforcement Learning

no code implementations1 Jan 2021 Sandy Huang, Abbas Abdolmaleki, Philemon Brakel, Steven Bohez, Nicolas Heess, Martin Riedmiller, Raia Hadsell

We propose a framework that uses a multi-objective RL algorithm to find a Pareto front of policies that trades off between the reward and constraint(s), and simultaneously searches along this front for constraint-satisfying policies.

Continuous Control

Divide-and-Conquer Monte Carlo Tree Search

no code implementations1 Jan 2021 Giambattista Parascandolo, Lars Holger Buesing, Josh Merel, Leonard Hasenclever, John Aslanides, Jessica B Hamrick, Nicolas Heess, Alexander Neitz, Theophane Weber

are constrained by an implicit sequential planning assumption: The order in which a plan is constructed is the same in which it is executed.

Continuous Control Decision Making

RL Unplugged: A Collection of Benchmarks for Offline Reinforcement Learning

1 code implementation NeurIPS 2020 Caglar Gulcehre, Ziyu Wang, Alexander Novikov, Thomas Paine, Sergio Gómez, Konrad Zolna, Rishabh Agarwal, Josh S. Merel, Daniel J. Mankowitz, Cosmin Paduraru, Gabriel Dulac-Arnold, Jerry Li, Mohammad Norouzi, Matthew Hoffman, Nicolas Heess, Nando de Freitas

We hope that our suite of benchmarks will increase the reproducibility of experiments and make it possible to study challenging tasks with a limited computational budget, thus making RL research both more systematic and more accessible across the community.

Offline RL

Behavior Priors for Efficient Reinforcement Learning

no code implementations27 Oct 2020 Dhruva Tirumala, Alexandre Galashov, Hyeonwoo Noh, Leonard Hasenclever, Razvan Pascanu, Jonathan Schwarz, Guillaume Desjardins, Wojciech Marian Czarnecki, Arun Ahuja, Yee Whye Teh, Nicolas Heess

In this work we consider how information and architectural constraints can be combined with ideas from the probabilistic modeling literature to learn behavior priors that capture the common movement and interaction patterns that are shared across a set of related tasks or contexts.

Continuous Control Hierarchical Reinforcement Learning +2

Learning Dexterous Manipulation from Suboptimal Experts

no code implementations16 Oct 2020 Rae Jeong, Jost Tobias Springenberg, Jackie Kay, Daniel Zheng, Yuxiang Zhou, Alexandre Galashov, Nicolas Heess, Francesco Nori

Although in many cases the learning process could be guided by demonstrations or other suboptimal experts, current RL algorithms for continuous action spaces often fail to effectively utilize combinations of highly off-policy expert data and on-policy exploration data.

Offline RL Q-Learning

Local Search for Policy Iteration in Continuous Control

no code implementations12 Oct 2020 Jost Tobias Springenberg, Nicolas Heess, Daniel Mankowitz, Josh Merel, Arunkumar Byravan, Abbas Abdolmaleki, Jackie Kay, Jonas Degrave, Julian Schrittwieser, Yuval Tassa, Jonas Buchli, Dan Belov, Martin Riedmiller

We demonstrate that additional computation spent on model-based policy improvement during learning can improve data efficiency, and confirm that model-based policy improvement during action selection can also be beneficial.

Continuous Control

Temporal Difference Uncertainties as a Signal for Exploration

no code implementations5 Oct 2020 Sebastian Flennerhag, Jane X. Wang, Pablo Sprechmann, Francesco Visin, Alexandre Galashov, Steven Kapturowski, Diana L. Borsa, Nicolas Heess, Andre Barreto, Razvan Pascanu

Instead, we incorporate it as an intrinsic reward and treat exploration as a separate learning problem, induced by the agent's temporal difference uncertainties.

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.

Learning to swim in potential flow

1 code implementation30 Sep 2020 Yusheng Jiao, Feng Ling, Sina Heydari, Nicolas Heess, Josh Merel, Eva Kanso

To address the problem of underwater motion planning, we propose a simple model of a three-link fish swimming in a potential flow environment and we use model-free reinforcement learning for shape control.

Motion Planning

Importance Weighted Policy Learning and Adaptation

no code implementations10 Sep 2020 Alexandre Galashov, Jakub Sygnowski, Guillaume Desjardins, Jan Humplik, Leonard Hasenclever, Rae Jeong, Yee Whye Teh, Nicolas Heess

The ability to exploit prior experience to solve novel problems rapidly is a hallmark of biological learning systems and of great practical importance for artificial ones.

Meta Reinforcement Learning

Action and Perception as Divergence Minimization

1 code implementation3 Sep 2020 Danijar Hafner, Pedro A. Ortega, Jimmy Ba, Thomas Parr, Karl Friston, Nicolas Heess

We recommend deriving future agent objectives the joint divergence to facilitate comparison, to point out the agent's target distribution, and to identify the intrinsic objective terms needed to reach that distribution.

Decision Making Representation Learning

Critic Regularized Regression

no code implementations NeurIPS 2020 Ziyu Wang, Alexander Novikov, Konrad Zolna, Jost Tobias Springenberg, Scott Reed, Bobak Shahriari, Noah Siegel, Josh Merel, Caglar Gulcehre, Nicolas Heess, Nando de Freitas

Offline reinforcement learning (RL), also known as batch RL, offers the prospect of policy optimization from large pre-recorded datasets without online environment interaction.

Offline RL

RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning

1 code implementation24 Jun 2020 Caglar Gulcehre, Ziyu Wang, Alexander Novikov, Tom Le Paine, Sergio Gomez Colmenarejo, Konrad Zolna, Rishabh Agarwal, Josh Merel, Daniel Mankowitz, Cosmin Paduraru, Gabriel Dulac-Arnold, Jerry Li, Mohammad Norouzi, Matt Hoffman, Ofir Nachum, George Tucker, Nicolas Heess, Nando de Freitas

We hope that our suite of benchmarks will increase the reproducibility of experiments and make it possible to study challenging tasks with a limited computational budget, thus making RL research both more systematic and more accessible across the community.

Atari Games DQN Replay Dataset +1

dm_control: Software and Tasks for Continuous Control

1 code implementation22 Jun 2020 Yuval Tassa, Saran Tunyasuvunakool, Alistair Muldal, Yotam Doron, Piotr Trochim, Si-Qi Liu, Steven Bohez, Josh Merel, Tom Erez, Timothy Lillicrap, Nicolas Heess

The dm_control software package is a collection of Python libraries and task suites for reinforcement learning agents in an articulated-body simulation.

Continuous Control

Simple Sensor Intentions for Exploration

no code implementations15 May 2020 Tim Hertweck, Martin Riedmiller, Michael Bloesch, Jost Tobias Springenberg, Noah Siegel, Markus Wulfmeier, Roland Hafner, Nicolas Heess

In particular, we show that a real robotic arm can learn to grasp and lift and solve a Ball-in-a-Cup task from scratch, when only raw sensor streams are used for both controller input and in the auxiliary reward definition.

Continuous-Discrete Reinforcement Learning for Hybrid Control in Robotics

no code implementations2 Jan 2020 Michael Neunert, Abbas Abdolmaleki, Markus Wulfmeier, Thomas Lampe, Jost Tobias Springenberg, Roland Hafner, Francesco Romano, Jonas Buchli, Nicolas Heess, Martin Riedmiller

In contrast, we propose to treat hybrid problems in their 'native' form by solving them with hybrid reinforcement learning, which optimizes for discrete and continuous actions simultaneously.

Catch & Carry: Reusable Neural Controllers for Vision-Guided Whole-Body Tasks

no code implementations15 Nov 2019 Josh Merel, Saran Tunyasuvunakool, Arun Ahuja, Yuval Tassa, Leonard Hasenclever, Vu Pham, Tom Erez, Greg Wayne, Nicolas Heess

We address the longstanding challenge of producing flexible, realistic humanoid character controllers that can perform diverse whole-body tasks involving object interactions.

Quinoa: a Q-function You Infer Normalized Over Actions

no code implementations5 Nov 2019 Jonas Degrave, Abbas Abdolmaleki, Jost Tobias Springenberg, Nicolas Heess, Martin Riedmiller

We present an algorithm for learning an approximate action-value soft Q-function in the relative entropy regularised reinforcement learning setting, for which an optimal improved policy can be recovered in closed form.

Normalising Flows

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

Stabilizing Transformers for Reinforcement Learning

4 code implementations ICML 2020 Emilio Parisotto, H. Francis Song, Jack W. Rae, Razvan Pascanu, Caglar Gulcehre, Siddhant M. Jayakumar, Max Jaderberg, Raphael Lopez Kaufman, Aidan Clark, Seb Noury, Matthew M. Botvinick, Nicolas Heess, Raia Hadsell

Harnessing the transformer's ability to process long time horizons of information could provide a similar performance boost in partially observable reinforcement learning (RL) domains, but the large-scale transformers used in NLP have yet to be successfully applied to the RL setting.

General Reinforcement Learning Language Modelling +1

Direct Policy Gradients: Direct Optimization of Policies in Discrete Action Spaces

no code implementations NeurIPS 2020 Guy Lorberbom, Chris J. Maddison, Nicolas Heess, Tamir Hazan, Daniel Tarlow

A main benefit of DirPG algorithms is that they allow the insertion of domain knowledge in the form of upper bounds on return-to-go at training time, like is used in heuristic search, while still directly computing a policy gradient.

Exploiting Hierarchy for Learning and Transfer in KL-regularized RL

no code implementations18 Mar 2019 Dhruva Tirumala, Hyeonwoo Noh, Alexandre Galashov, Leonard Hasenclever, Arun Ahuja, Greg Wayne, Razvan Pascanu, Yee Whye Teh, Nicolas Heess

As reinforcement learning agents are tasked with solving more challenging and diverse tasks, the ability to incorporate prior knowledge into the learning system and to exploit reusable structure in solution space is likely to become increasingly important.

Continuous Control

The Termination Critic

no code implementations26 Feb 2019 Anna Harutyunyan, Will Dabney, Diana Borsa, Nicolas Heess, Remi Munos, Doina Precup

In this work, we consider the problem of autonomously discovering behavioral abstractions, or options, for reinforcement learning agents.

Emergent Coordination Through Competition

no code implementations ICLR 2019 Si-Qi Liu, Guy Lever, Josh Merel, Saran Tunyasuvunakool, Nicolas Heess, Thore Graepel

We study the emergence of cooperative behaviors in reinforcement learning agents by introducing a challenging competitive multi-agent soccer environment with continuous simulated physics.

Continuous Control

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.

Composing Entropic Policies using Divergence Correction

no code implementations5 Dec 2018 Jonathan J. Hunt, Andre Barreto, Timothy P. Lillicrap, Nicolas Heess

Composing previously mastered skills to solve novel tasks promises dramatic improvements in the data efficiency of reinforcement learning.

Continuous Control

Relative Entropy Regularized Policy Iteration

1 code implementation5 Dec 2018 Abbas Abdolmaleki, Jost Tobias Springenberg, Jonas Degrave, Steven Bohez, Yuval Tassa, Dan Belov, Nicolas Heess, Martin Riedmiller

Our algorithm draws on connections to existing literature on black-box optimization and 'RL as an inference' and it can be seen either as an extension of the Maximum a Posteriori Policy Optimisation algorithm (MPO) [Abdolmaleki et al., 2018a], or as an extension of Trust Region Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES) [Abdolmaleki et al., 2017b; Hansen et al., 1997] to a policy iteration scheme.

Continuous Control OpenAI Gym

Neural probabilistic motor primitives for humanoid control

no code implementations ICLR 2019 Josh Merel, Leonard Hasenclever, Alexandre Galashov, Arun Ahuja, Vu Pham, Greg Wayne, Yee Whye Teh, Nicolas Heess

We focus on the problem of learning a single motor module that can flexibly express a range of behaviors for the control of high-dimensional physically simulated humanoids.

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.

Mix & Match - Agent Curricula for Reinforcement Learning

no code implementations ICML 2018 Wojciech Czarnecki, Siddhant Jayakumar, Max Jaderberg, Leonard Hasenclever, Yee Whye Teh, Nicolas Heess, Simon Osindero, Razvan Pascanu

We introduce Mix and match (M&M) – a training framework designed to facilitate rapid and effective learning in RL agents that would be too slow or too challenging to train otherwise. The key innovation is a procedure that allows us to automatically form a curriculum over agents.

Curriculum Learning

Maximum a Posteriori Policy Optimisation

1 code implementation ICLR 2018 Abbas Abdolmaleki, Jost Tobias Springenberg, Yuval Tassa, Remi Munos, Nicolas Heess, Martin Riedmiller

We introduce a new algorithm for reinforcement learning called Maximum aposteriori Policy Optimisation (MPO) based on coordinate ascent on a relative entropy objective.

Continuous Control

Mix&Match - Agent Curricula for Reinforcement Learning

no code implementations5 Jun 2018 Wojciech Marian Czarnecki, Siddhant M. Jayakumar, Max Jaderberg, Leonard Hasenclever, Yee Whye Teh, Simon Osindero, Nicolas Heess, Razvan Pascanu

(2) We further show that M&M can be used successfully to progress through a curriculum of architectural variants defining an agents internal state.

Curriculum Learning

Graph networks as learnable physics engines for inference and control

1 code implementation ICML 2018 Alvaro Sanchez-Gonzalez, Nicolas Heess, Jost Tobias Springenberg, Josh Merel, Martin Riedmiller, Raia Hadsell, Peter Battaglia

Understanding and interacting with everyday physical scenes requires rich knowledge about the structure of the world, represented either implicitly in a value or policy function, or explicitly in a transition model.

Reinforcement and Imitation Learning for Diverse Visuomotor Skills

1 code implementation ICLR 2018 Yuke Zhu, Ziyu Wang, Josh Merel, Andrei Rusu, Tom Erez, Serkan Cabi, Saran Tunyasuvunakool, János Kramár, Raia Hadsell, Nando de Freitas, Nicolas Heess

We propose a model-free deep reinforcement learning method that leverages a small amount of demonstration data to assist a reinforcement learning agent.

Imitation Learning

Learning an Embedding Space for Transferable Robot Skills

no code implementations ICLR 2018 Karol Hausman, Jost Tobias Springenberg, Ziyu Wang, Nicolas Heess, Martin Riedmiller

We present a method for reinforcement learning of closely related skills that are parameterized via a skill embedding space.

Variational Inference

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

Distral: Robust Multitask Reinforcement Learning

no code implementations NeurIPS 2017 Yee Whye Teh, Victor Bapst, Wojciech Marian Czarnecki, John Quan, James Kirkpatrick, Raia Hadsell, Nicolas Heess, Razvan Pascanu

Moreover, the proposed learning process is more robust and more stable---attributes that are critical in deep reinforcement learning.

Transfer Learning

Robust Imitation of Diverse Behaviors

no code implementations NeurIPS 2017 Ziyu Wang, Josh Merel, Scott Reed, Greg Wayne, Nando de Freitas, Nicolas Heess

Compared to purely supervised methods, Generative Adversarial Imitation Learning (GAIL) can learn more robust controllers from fewer demonstrations, but is inherently mode-seeking and more difficult to train.

Imitation Learning

Learning human behaviors from motion capture by adversarial imitation

1 code implementation7 Jul 2017 Josh Merel, Yuval Tassa, Dhruva TB, Sriram Srinivasan, Jay Lemmon, Ziyu Wang, Greg Wayne, Nicolas Heess

Rapid progress in deep reinforcement learning has made it increasingly feasible to train controllers for high-dimensional humanoid bodies.

Imitation Learning Motion Capture

Emergence of Locomotion Behaviours in Rich Environments

5 code implementations7 Jul 2017 Nicolas Heess, Dhruva TB, Srinivasan Sriram, Jay Lemmon, Josh Merel, Greg Wayne, Yuval Tassa, Tom Erez, Ziyu Wang, S. M. Ali Eslami, Martin Riedmiller, David Silver

The reinforcement learning paradigm allows, in principle, for complex behaviours to be learned directly from simple reward signals.

Filtering Variational Objectives

3 code implementations NeurIPS 2017 Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Mohammad Norouzi, andriy mnih, Arnaud Doucet, Yee Whye Teh

When used as a surrogate objective for maximum likelihood estimation in latent variable models, the evidence lower bound (ELBO) produces state-of-the-art results.

Latent Variable Models

Metacontrol for Adaptive Imagination-Based Optimization

1 code implementation7 May 2017 Jessica B. Hamrick, Andrew J. Ballard, Razvan Pascanu, Oriol Vinyals, Nicolas Heess, Peter W. Battaglia

The metacontroller component is a model-free reinforcement learning agent, which decides both how many iterations of the optimization procedure to run, as well as which model to consult on each iteration.

Decision Making

Particle Value Functions

no code implementations16 Mar 2017 Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Arnaud Doucet, andriy mnih, Yee Whye Teh

The policy gradients of the expected return objective can react slowly to rare rewards.

Sample Efficient Actor-Critic with Experience Replay

9 code implementations3 Nov 2016 Ziyu Wang, Victor Bapst, Nicolas Heess, Volodymyr Mnih, Remi Munos, Koray Kavukcuoglu, Nando de Freitas

This paper presents an actor-critic deep reinforcement learning agent with experience replay that is stable, sample efficient, and performs remarkably well on challenging environments, including the discrete 57-game Atari domain and several continuous control problems.

Continuous Control

Sim-to-Real Robot Learning from Pixels with Progressive Nets

no code implementations13 Oct 2016 Andrei A. Rusu, Mel Vecerik, Thomas Rothörl, Nicolas Heess, Razvan Pascanu, Raia Hadsell

The progressive net approach is a general framework that enables reuse of everything from low-level visual features to high-level policies for transfer to new tasks, enabling a compositional, yet simple, approach to building complex skills.

Memory-based control with recurrent neural networks

2 code implementations14 Dec 2015 Nicolas Heess, Jonathan J. Hunt, Timothy P. Lillicrap, David Silver

Partially observed control problems are a challenging aspect of reinforcement learning.

Continuous Control

Learning Continuous Control Policies by Stochastic Value Gradients

1 code implementation NeurIPS 2015 Nicolas Heess, Greg Wayne, David Silver, Timothy Lillicrap, Yuval Tassa, Tom Erez

One of these variants, SVG(1), shows the effectiveness of learning models, value functions, and policies simultaneously in continuous domains.

Continuous Control

Gradient Estimation Using Stochastic Computation Graphs

no code implementations NeurIPS 2015 John Schulman, Nicolas Heess, Theophane Weber, Pieter Abbeel

In a variety of problems originating in supervised, unsupervised, and reinforcement learning, the loss function is defined by an expectation over a collection of random variables, which might be part of a probabilistic model or the external world.

Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages

1 code implementation9 Mar 2015 Wittawat Jitkrittum, Arthur Gretton, Nicolas Heess, S. M. Ali Eslami, Balaji Lakshminarayanan, Dino Sejdinovic, Zoltán Szabó

We propose an efficient nonparametric strategy for learning a message operator in expectation propagation (EP), which takes as input the set of incoming messages to a factor node, and produces an outgoing message as output.

Passing Expectation Propagation Messages with Kernel Methods

no code implementations2 Jan 2015 Wittawat Jitkrittum, Arthur Gretton, Nicolas Heess

We propose to learn a kernel-based message operator which takes as input all expectation propagation (EP) incoming messages to a factor node and produces an outgoing message.

Bayes-Adaptive Simulation-based Search with Value Function Approximation

no code implementations NeurIPS 2014 Arthur Guez, Nicolas Heess, David Silver, Peter Dayan

Bayes-adaptive planning offers a principled solution to the exploration-exploitation trade-off under model uncertainty.

Recurrent Models of Visual Attention

13 code implementations NeurIPS 2014 Volodymyr Mnih, Nicolas Heess, Alex Graves, Koray Kavukcuoglu

Applying convolutional neural networks to large images is computationally expensive because the amount of computation scales linearly with the number of image pixels.

Image Classification

Learning to Pass Expectation Propagation Messages

no code implementations NeurIPS 2013 Nicolas Heess, Daniel Tarlow, John Winn

Expectation Propagation (EP) is a popular approximate posterior inference algorithm that often provides a fast and accurate alternative to sampling-based methods.

Searching for objects driven by context

no code implementations NeurIPS 2012 Bogdan Alexe, Nicolas Heess, Yee W. Teh, Vittorio Ferrari

The dominant visual search paradigm for object class detection is sliding windows.

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