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no code implementations • NeurIPS 2020 • Zhenwen Dai, Praveen Chandar, Ghazal Fazelnia, Ben Carterette, Mounia Lalmas-Roelleke

A challenge that machine learning practitioners in the industry face is the task of selecting the best model to deploy in production.

no code implementations • 21 Apr 2021 • Erik Bodin, Federico Tomasi, Zhenwen Dai

Neural architecture search (NAS) is a recent methodology for automating the design of neural network architectures.

no code implementations • 28 Oct 2020 • Jörg Lücke, Dennis Forster, Zhenwen Dai

Our derived analytical results are exact and apply for small as well as complex neural networks for decoder and encoder.

1 code implementation • 26 Oct 2020 • Erik Bodin, Zhenwen Dai, Neill D. F. Campbell, Carl Henrik Ek

We present a novel approach to Bayesian inference and general Bayesian computation that is defined through a sequential decision loop.

no code implementations • 1 Aug 2019 • Georgios Exarchakis, Jörg Bornschein, Abdul-Saboor Sheikh, Zhenwen Dai, Marc Henniges, Jakob Drefs, Jörg Lücke

The library widens the scope of dictionary learning approaches beyond implementations of standard approaches such as ICA, NMF or standard L1 sparse coding.

no code implementations • ICML 2020 • Erik Bodin, Markus Kaiser, Ieva Kazlauskaite, Zhenwen Dai, Neill D. F. Campbell, Carl Henrik Ek

Bayesian optimization (BO) methods often rely on the assumption that the objective function is well-behaved, but in practice, this is seldom true for real-world objectives even if noise-free observations can be collected.

1 code implementation • NeurIPS 2019 • Aaron Klein, Zhenwen Dai, Frank Hutter, Neil Lawrence, Javier Gonzalez

Despite the recent progress in hyperparameter optimization (HPO), available benchmarks that resemble real-world scenarios consist of a few and very large problem instances that are expensive to solve.

1 code implementation • ICLR 2019 • Zhenwen Dai, Eric Meissner, Neil D. Lawrence

A probabilistic module consists of a set of random variables with associated probabilistic distributions and dedicated inference methods.

2 code implementations • CVPR 2019 • Sungsoo Ahn, Shell Xu Hu, Andreas Damianou, Neil D. Lawrence, Zhenwen Dai

We further demonstrate the strength of our method on knowledge transfer across heterogeneous network architectures by transferring knowledge from a convolutional neural network (CNN) to a multi-layer perceptron (MLP) on CIFAR-10.

no code implementations • ICML 2018 • Xiaoyu Lu, Javier Gonzalez, Zhenwen Dai, Neil Lawrence

We tackle the problem of optimizing a black-box objective function defined over a highly-structured input space.

no code implementations • 3 Jan 2018 • Mu Niu, Pokman Cheung, Lizhen Lin, Zhenwen Dai, Neil Lawrence, David Dunson

in-GPs respect the potentially complex boundary or interior conditions as well as the intrinsic geometry of the spaces.

no code implementations • 21 Dec 2017 • Jörg Lücke, Zhenwen Dai, Georgios Exarchakis

We investigate the optimization of two probabilistic generative models with binary latent variables using a novel variational EM approach.

no code implementations • 24 Oct 2017 • Matthias Seeger, Asmus Hetzel, Zhenwen Dai, Eric Meissner, Neil D. Lawrence

Development systems for deep learning (DL), such as Theano, Torch, TensorFlow, or MXNet, are easy-to-use tools for creating complex neural network models.

no code implementations • ICML 2017 • Javier González, Zhenwen Dai, Andreas Damianou, Neil D. Lawrence

We present a new framework for this scenario that we call Preferential Bayesian Optimization (PBO) and that allows to find the optimum of a latent function that can only be queried through pairwise comparisons, so-called duels.

no code implementations • NeurIPS 2017 • Zhenwen Dai, Mauricio A. Álvarez, Neil D. Lawrence

Often in machine learning, data are collected as a combination of multiple conditions, e. g., the voice recordings of multiple persons, each labeled with an ID.

no code implementations • 12 Apr 2017 • Javier Gonzalez, Zhenwen Dai, Andreas Damianou, Neil D. Lawrence

Bayesian optimization (BO) has emerged during the last few years as an effective approach to optimizing black-box functions where direct queries of the objective are expensive.

no code implementations • 17 Oct 2016 • Mu Niu, Zhenwen Dai, Neil Lawrence, Kolja Becker

The spatio-temporal field of protein concentration and mRNA expression are reconstructed without explicitly solving the partial differential equation.

no code implementations • 30 Jun 2016 • Fariba Yousefi, Zhenwen Dai, Carl Henrik Ek, Neil Lawrence

Unsupervised learning on imbalanced data is challenging because, when given imbalanced data, current model is often dominated by the major category and ignores the categories with small amount of data.

1 code implementation • 20 Nov 2015 • César Lincoln C. Mattos, Zhenwen Dai, Andreas Damianou, Jeremy Forth, Guilherme A. Barreto, Neil D. Lawrence

We define Recurrent Gaussian Processes (RGP) models, a general family of Bayesian nonparametric models with recurrent GP priors which are able to learn dynamical patterns from sequential data.

no code implementations • 19 Nov 2015 • Zhenwen Dai, Andreas Damianou, Javier González, Neil Lawrence

We develop a scalable deep non-parametric generative model by augmenting deep Gaussian processes with a recognition model.

1 code implementation • 29 May 2015 • Javier González, Zhenwen Dai, Philipp Hennig, Neil D. Lawrence

The approach assumes that the function of interest, $f$, is a Lipschitz continuous function.

no code implementations • 10 May 2015 • Zhenwen Dai, James Hensman, Neil Lawrence

The Gaussian process latent variable model (GP-LVM) is a popular approach to non-linear probabilistic dimensionality reduction.

no code implementations • 10 Dec 2014 • Jacquelyn A. Shelton, Jan Gasthaus, Zhenwen Dai, Joerg Luecke, Arthur Gretton

We propose a nonparametric procedure to achieve fast inference in generative graphical models when the number of latent states is very large.

no code implementations • 18 Oct 2014 • Zhenwen Dai, Andreas Damianou, James Hensman, Neil Lawrence

In this work, we present an extension of Gaussian process (GP) models with sophisticated parallelization and GPU acceleration.

no code implementations • NeurIPS 2013 • Zhenwen Dai, Georgios Exarchakis, Jörg Lücke

By far most approaches to unsupervised learning learning of visual features, such as sparse coding or ICA, account for translations by representing the same features at different positions.

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