b'Mario Lucic'

research

∙ 07/08/2022

Beyond Transfer Learning: Co-finetuning for Action Localisation

Transfer learning is the predominant paradigm for training deep networks...

Anurag Arnab, et al. ∙

research

∙ 06/15/2022

VCT: A Video Compression Transformer

We show how transformers can be used to vastly simplify neural video com...

Fabian Mentzer, et al. ∙

research

∙ 11/25/2021

Scene Representation Transformer: Geometry-Free Novel View Synthesis Through Set-Latent Scene Representations

A classical problem in computer vision is to infer a 3D scene representa...

Mehdi S. M. Sajjadi, et al. ∙

research

∙ 11/25/2021

PolyViT: Co-training Vision Transformers on Images, Videos and Audio

Can we train a single transformer model capable of processing multiple m...

Valerii Likhosherstov, et al. ∙

research

∙ 06/15/2021

Revisiting the Calibration of Modern Neural Networks

Accurate estimation of predictive uncertainty (model calibration) is ess...

Matthias Minderer, et al. ∙

research

∙ 05/04/2021

MLP-Mixer: An all-MLP Architecture for Vision

Convolutional Neural Networks (CNNs) are the go-to model for computer vi...

Ilya Tolstikhin, et al. ∙

research

∙ 04/09/2021

SI-Score: An image dataset for fine-grained analysis of robustness to object location, rotation and size

Before deploying machine learning models it is critical to assess their ...

Jessica Yung, et al. ∙

research

∙ 03/29/2021

ViViT: A Video Vision Transformer

We present pure-transformer based models for video classification, drawi...

Anurag Arnab, et al. ∙

research

∙ 11/06/2020

Underspecification Presents Challenges for Credibility in Modern Machine Learning

ML models often exhibit unexpectedly poor behavior when they are deploye...

Alexander D'Amour, et al. ∙

research

∙ 10/27/2020

A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation

The idea behind the unsupervised learning of disentangled representation...

Francesco Locatello, et al. ∙

research

∙ 10/13/2020

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

Transfer learning has been recently popularized as a data-efficient alte...

Cedric Renggli, et al. ∙

research

∙ 10/06/2020

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

We propose a method to learn image representations from uncurated videos...

Rob Romijnders, et al. ∙

research

∙ 07/28/2020

A Commentary on the Unsupervised Learning of Disentangled Representations

The goal of the unsupervised learning of disentangled representations is...

Francesco Locatello, et al. ∙

research

∙ 07/16/2020

On Robustness and Transferability of Convolutional Neural Networks

Modern deep convolutional networks (CNNs) are often criticized for not g...

Josip Djolonga, et al. ∙

research

∙ 12/05/2019

Self-Supervised Learning of Video-Induced Visual Invariances

We propose a general framework for self-supervised learning of transfera...

Michael Tschannen, et al. ∙

research

∙ 11/26/2019

Semantic Bottleneck Scene Generation

Coupling the high-fidelity generation capabilities of label-conditional ...

Samaneh Azadi, et al. ∙

research

∙ 07/31/2019

On Mutual Information Maximization for Representation Learning

Many recent methods for unsupervised or self-supervised representation l...

Michael Tschannen, et al. ∙

research

∙ 05/26/2019

Evaluating Generative Models Using Divergence Frontiers

Despite the tremendous progress in the estimation of generative models, ...

Josip Djolonga, et al. ∙

research

∙ 03/06/2019

High-Fidelity Image Generation With Fewer Labels

Deep generative models are becoming a cornerstone of modern machine lear...

Mario Lucic, et al. ∙

research

∙ 12/12/2018

Recent Advances in Autoencoder-Based Representation Learning

Learning useful representations with little or no supervision is a key c...

Michael Tschannen, et al. ∙

research

∙ 11/29/2018

Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations

In recent years, the interest in unsupervised learning of disentangled r...

Francesco Locatello, et al. ∙

research

∙ 11/27/2018

Self-Supervised Generative Adversarial Networks

Conditional GANs are at the forefront of natural image synthesis. The ma...

Ting Chen, et al. ∙

research

∙ 10/02/2018

On Self Modulation for Generative Adversarial Networks

Training Generative Adversarial Networks (GANs) is notoriously challengi...

Ting Chen, et al. ∙

research

∙ 07/12/2018

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

Generative Adversarial Networks (GANs) are a class of deep generative mo...

Karol Kurach, et al. ∙

research

∙ 05/31/2018

Assessing Generative Models via Precision and Recall

Recent advances in generative modeling have led to an increased interest...

Mehdi S. M. Sajjadi, et al. ∙

research

∙ 05/28/2018

Deep Generative Models for Distribution-Preserving Lossy Compression

We propose and study the problem of distribution-preserving lossy compre...

Michael Tschannen, et al. ∙

research

∙ 11/28/2017

Are GANs Created Equal? A Large-Scale Study

Generative adversarial networks (GAN) are a powerful subclass of generat...

Mario Lucic, et al. ∙

research

∙ 11/27/2017

One-Shot Coresets: The Case of k-Clustering

Scaling clustering algorithms to massive data sets is a challenging task...

Olivier Bachem, et al. ∙

research

∙ 11/05/2017

Stochastic Submodular Maximization: The Case of Coverage Functions

Stochastic optimization of continuous objectives is at the heart of mode...

Mohammad Reza Karimi, et al. ∙

research

∙ 03/23/2017

Training Mixture Models at Scale via Coresets

How can we train a statistical mixture model on a massive data set? In t...

Mario Lucic, et al. ∙

research

∙ 03/19/2017

Practical Coreset Constructions for Machine Learning

We investigate coresets - succinct, small summaries of large data sets -...

Olivier Bachem, et al. ∙

research

∙ 02/27/2017

Uniform Deviation Bounds for Unbounded Loss Functions like k-Means

Uniform deviation bounds limit the difference between a model's expected...

Olivier Bachem, et al. ∙

research

∙ 02/27/2017

Scalable and Distributed Clustering via Lightweight Coresets

Coresets are compact representations of data sets such that models train...

Olivier Bachem, et al. ∙

research

∙ 05/31/2016

Horizontally Scalable Submodular Maximization

A variety of large-scale machine learning problems can be cast as instan...

Mario Lucic, et al. ∙

research

∙ 05/02/2016

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

Faced with massive data, is it possible to trade off (statistical) risk,...

Mario Lucic, et al. ∙

research

∙ 05/02/2016

Linear-time Outlier Detection via Sensitivity

Outliers are ubiquitous in modern data sets. Distance-based techniques a...

Mario Lucic, et al. ∙

research

∙ 08/21/2015

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...

Mario Lucic, et al. ∙

Mario Lucic

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