Mario Lucic

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Google ∙ ETH Zurich

Research Scientist in the Google Brain team

Which Model to Transfer? Finding the Needle in the Growing Haystack

Transfer learning has been recently popularized as a data-efficient alte...
10/13/2020 ∙ by Cèdric Renggli, et al. ∙ 19 ∙ share

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Representation learning from videos in-the-wild: An object-centric approach

We propose a method to learn image representations from uncurated videos...
10/06/2020 ∙ by Rob Romijnders, et al. ∙ 6 ∙ share

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A Commentary on the Unsupervised Learning of Disentangled Representations

The goal of the unsupervised learning of disentangled representations is...
07/28/2020 ∙ by Francesco Locatello, et al. ∙ 15 ∙ share

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On Robustness and Transferability of Convolutional Neural Networks

Modern deep convolutional networks (CNNs) are often criticized for not g...
07/16/2020 ∙ by Josip Djolonga, et al. ∙ 15 ∙ share

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Self-Supervised Learning of Video-Induced Visual Invariances

We propose a general framework for self-supervised learning of transfera...
12/05/2019 ∙ by Michael Tschannen, et al. ∙ 14 ∙ share

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Semantic Bottleneck Scene Generation

Coupling the high-fidelity generation capabilities of label-conditional ...
11/26/2019 ∙ by Samaneh Azadi, et al. ∙ 33 ∙ share

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On Mutual Information Maximization for Representation Learning

Many recent methods for unsupervised or self-supervised representation l...
07/31/2019 ∙ by Michael Tschannen, et al. ∙ 0 ∙ share

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Evaluating Generative Models Using Divergence Frontiers

Despite the tremendous progress in the estimation of generative models, ...
05/26/2019 ∙ by Josip Djolonga, et al. ∙ 0 ∙ share

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High-Fidelity Image Generation With Fewer Labels

Deep generative models are becoming a cornerstone of modern machine lear...
03/06/2019 ∙ by Mario Lucic, et al. ∙ 14 ∙ share

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Recent Advances in Autoencoder-Based Representation Learning

Learning useful representations with little or no supervision is a key c...
12/12/2018 ∙ by Michael Tschannen, et al. ∙ 0 ∙ share

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Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations

In recent years, the interest in unsupervised learning of disentangled r...
11/29/2018 ∙ by Francesco Locatello, et al. ∙ 4 ∙ share

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Self-Supervised Generative Adversarial Networks

Conditional GANs are at the forefront of natural image synthesis. The ma...
11/27/2018 ∙ by Ting Chen, et al. ∙ 0 ∙ share

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On Self Modulation for Generative Adversarial Networks

Training Generative Adversarial Networks (GANs) is notoriously challengi...
10/02/2018 ∙ by Ting Chen, et al. ∙ 0 ∙ share

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The GAN Landscape: Losses, Architectures, Regularization, and Normalization

Generative Adversarial Networks (GANs) are a class of deep generative mo...
07/12/2018 ∙ by Karol Kurach, et al. ∙ 6 ∙ share

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Assessing Generative Models via Precision and Recall

Recent advances in generative modeling have led to an increased interest...
05/31/2018 ∙ by Mehdi S. M. Sajjadi, et al. ∙ 0 ∙ share

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Deep Generative Models for Distribution-Preserving Lossy Compression

We propose and study the problem of distribution-preserving lossy compre...
05/28/2018 ∙ by Michael Tschannen, et al. ∙ 2 ∙ share

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Are GANs Created Equal? A Large-Scale Study

Generative adversarial networks (GAN) are a powerful subclass of generat...
11/28/2017 ∙ by Mario Lucic, et al. ∙ 0 ∙ share

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One-Shot Coresets: The Case of k-Clustering

Scaling clustering algorithms to massive data sets is a challenging task...
11/27/2017 ∙ by Olivier Bachem, et al. ∙ 0 ∙ share

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Stochastic Submodular Maximization: The Case of Coverage Functions

Stochastic optimization of continuous objectives is at the heart of mode...
11/05/2017 ∙ by Mohammad Reza Karimi, et al. ∙ 0 ∙ share

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Training Mixture Models at Scale via Coresets

How can we train a statistical mixture model on a massive data set? In t...
03/23/2017 ∙ by Mario Lucic, et al. ∙ 0 ∙ share

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Practical Coreset Constructions for Machine Learning

We investigate coresets - succinct, small summaries of large data sets -...
03/19/2017 ∙ by Olivier Bachem, et al. ∙ 0 ∙ share

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Uniform Deviation Bounds for Unbounded Loss Functions like k-Means

Uniform deviation bounds limit the difference between a model's expected...
02/27/2017 ∙ by Olivier Bachem, et al. ∙ 0 ∙ share

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Scalable and Distributed Clustering via Lightweight Coresets

Coresets are compact representations of data sets such that models train...
02/27/2017 ∙ by Olivier Bachem, et al. ∙ 0 ∙ share

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Horizontally Scalable Submodular Maximization

A variety of large-scale machine learning problems can be cast as instan...
05/31/2016 ∙ by Mario Lucic, et al. ∙ 0 ∙ share

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Tradeoffs for Space, Time, Data and Risk in Unsupervised Learning

Faced with massive data, is it possible to trade off (statistical) risk,...
05/02/2016 ∙ by Mario Lucic, et al. ∙ 0 ∙ share

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Linear-time Outlier Detection via Sensitivity

Outliers are ubiquitous in modern data sets. Distance-based techniques a...
05/02/2016 ∙ by Mario Lucic, et al. ∙ 0 ∙ share

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Strong Coresets for Hard and Soft Bregman Clustering with Applications to Exponential Family Mixtures

Coresets are efficient representations of data sets such that models tra...
08/21/2015 ∙ by Mario Lucic, et al. ∙ 0 ∙ share

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Mario Lucic

Featured Co-authors

Bernhard Schölkopf

Trevor Darrell

Andreas Krause

Ce Zhang

Marco Cuturi

Amin Karbasi

Sylvain Gelly

Stefan Bauer

Michael Tschannen

Hamed Hassani

Ting Chen

Which Model to Transfer? Finding the Needle in the Growing Haystack

Representation learning from videos in-the-wild: An object-centric approach

A Commentary on the Unsupervised Learning of Disentangled Representations

On Robustness and Transferability of Convolutional Neural Networks

Self-Supervised Learning of Video-Induced Visual Invariances

Semantic Bottleneck Scene Generation

On Mutual Information Maximization for Representation Learning

Evaluating Generative Models Using Divergence Frontiers

High-Fidelity Image Generation With Fewer Labels

Recent Advances in Autoencoder-Based Representation Learning

Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations

Self-Supervised Generative Adversarial Networks

On Self Modulation for Generative Adversarial Networks

The GAN Landscape: Losses, Architectures, Regularization, and Normalization

Assessing Generative Models via Precision and Recall

Deep Generative Models for Distribution-Preserving Lossy Compression

Are GANs Created Equal? A Large-Scale Study

One-Shot Coresets: The Case of k-Clustering

Stochastic Submodular Maximization: The Case of Coverage Functions

Training Mixture Models at Scale via Coresets

Practical Coreset Constructions for Machine Learning

Uniform Deviation Bounds for Unbounded Loss Functions like k-Means

Scalable and Distributed Clustering via Lightweight Coresets

Horizontally Scalable Submodular Maximization

Tradeoffs for Space, Time, Data and Risk in Unsupervised Learning

Linear-time Outlier Detection via Sensitivity

Strong Coresets for Hard and Soft Bregman Clustering with Applications to Exponential Family Mixtures