Search Results for author: Ulrich Paquet

Found 19 papers, 7 papers with code

Bridging the Human-AI Knowledge Gap: Concept Discovery and Transfer in AlphaZero

no code implementations25 Oct 2023 Lisa Schut, Nenad Tomasev, Tom McGrath, Demis Hassabis, Ulrich Paquet, Been Kim

Artificial Intelligence (AI) systems have made remarkable progress, attaining super-human performance across various domains.

Game of Chess

Role of Human-AI Interaction in Selective Prediction

1 code implementation13 Dec 2021 Elizabeth Bondi, Raphael Koster, Hannah Sheahan, Martin Chadwick, Yoram Bachrach, Taylan Cemgil, Ulrich Paquet, Krishnamurthy Dvijotham

Using real-world conservation data and a selective prediction system that improves expected accuracy over that of the human or AI system working individually, we show that this messaging has a significant impact on the accuracy of human judgements.

Acquisition of Chess Knowledge in AlphaZero

no code implementations17 Nov 2021 Thomas McGrath, Andrei Kapishnikov, Nenad Tomašev, Adam Pearce, Demis Hassabis, Been Kim, Ulrich Paquet, Vladimir Kramnik

In this work we provide evidence that human knowledge is acquired by the AlphaZero neural network as it trains on the game of chess.

Game of Chess

Unsupervised Separation of Dynamics from Pixels

no code implementations20 Jul 2019 Silvia Chiappa, Ulrich Paquet

We present an approach to learn the dynamics of multiple objects from image sequences in an unsupervised way.

A Factorial Mixture Prior for Compositional Deep Generative Models

no code implementations18 Dec 2018 Ulrich Paquet, Sumedh K. Ghaisas, Olivier Tieleman

We assume that a high-dimensional datum, like an image, is a compositional expression of a set of properties, with a complicated non-linear relationship between the datum and its properties.

Variational Inference

An Efficient Implementation of Riemannian Manifold Hamiltonian Monte Carlo for Gaussian Process Models

no code implementations28 Oct 2018 Ulrich Paquet, Marco Fraccaro

This technical report presents pseudo-code for a Riemannian manifold Hamiltonian Monte Carlo (RMHMC) method to efficiently simulate samples from $N$-dimensional posterior distributions $p(x|y)$, where $x \in R^N$ is drawn from a Gaussian Process (GP) prior, and observations $y_n$ are independent given $x_n$.

Recurrent Relational Networks for complex relational reasoning

1 code implementation ICLR 2018 Rasmus Berg Palm, Ulrich Paquet, Ole Winther

Humans possess an ability to abstractly reason about objects and their interactions, an ability not shared with state-of-the-art deep learning models.

Relational Reasoning

Recurrent Relational Networks

6 code implementations NeurIPS 2018 Rasmus Berg Palm, Ulrich Paquet, Ole Winther

We achieve state of the art results on the bAbI textual question-answering dataset with the recurrent relational network, consistently solving 20/20 tasks.

Ranked #3 on Question Answering on bAbi (Mean Error Rate metric)

Question Answering Relational Reasoning

A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning

1 code implementation NeurIPS 2017 Marco Fraccaro, Simon Kamronn, Ulrich Paquet, Ole Winther

This paper takes a step towards temporal reasoning in a dynamically changing video, not in the pixel space that constitutes its frames, but in a latent space that describes the non-linear dynamics of the objects in its world.


The Bayesian Low-Rank Determinantal Point Process Mixture Model

no code implementations15 Aug 2016 Mike Gartrell, Ulrich Paquet, Noam Koenigstein

Determinantal point processes (DPPs) are an elegant model for encoding probabilities over subsets, such as shopping baskets, of a ground set, such as an item catalog.

Point Processes Product Recommendation

An Adaptive Resample-Move Algorithm for Estimating Normalizing Constants

no code implementations7 Apr 2016 Marco Fraccaro, Ulrich Paquet, Ole Winther

The estimation of normalizing constants is a fundamental step in probabilistic model comparison.

Low-Rank Factorization of Determinantal Point Processes for Recommendation

1 code implementation17 Feb 2016 Mike Gartrell, Ulrich Paquet, Noam Koenigstein

In this work we present a new method for learning the DPP kernel from observed data using a low-rank factorization of this kernel.

Point Processes Product Recommendation

On the Convergence of Stochastic Variational Inference in Bayesian Networks

no code implementations16 Jul 2015 Ulrich Paquet

We highlight a pitfall when applying stochastic variational inference to general Bayesian networks.

Variational Inference

Scalable Bayesian Modelling of Paired Symbols

no code implementations9 Sep 2014 Ulrich Paquet, Noam Koenigstein, Ole Winther

We present a novel, scalable and Bayesian approach to modelling the occurrence of pairs of symbols (i, j) drawn from a large vocabulary.

One-class Collaborative Filtering with Random Graphs: Annotated Version

no code implementations26 Sep 2013 Ulrich Paquet, Noam Koenigstein

The bane of one-class collaborative filtering is interpreting and modelling the latent signal from the missing class.

Collaborative Filtering Variational Inference

Perturbative Corrections for Approximate Inference in Gaussian Latent Variable Models

no code implementations12 Jan 2013 Manfred Opper, Ulrich Paquet, Ole Winther

A perturbative expansion is made of the exact but intractable correction, and can be applied to the model's partition function and other moments of interest.

Improving on Expectation Propagation

no code implementations NeurIPS 2008 Manfred Opper, Ulrich Paquet, Ole Winther

We develop as series of corrections to Expectation Propagation (EP), which is one of the most popular methods for approximate probabilistic inference.

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