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Related publications (31)

Selective attention involves a feature-specific sequential release from inhibitory gating

David Pascucci, Gijs Plomp

Selective attention is a fundamental cognitive mechanism that allows our brain to preferentially process relevant sensory information, while filtering out distracting information. Attention is thought to flexibly gate the communication of irrelevant inform ...

2022

Practical Byzantine-resilient Stochastic Gradient Descent

Sébastien Louis Alexandre Rouault

Algorithms are everywhere. The recipe for the frangipane cake is an algorithm. If all the listed ingredients are available and the cook is sufficiently deft, after a finite number of small, well-defined steps a delicious dessert will exit the oven. Now, wh ...

EPFL2022

Generalization Properties of NAS under Activation and Skip Connection Search

Volkan Cevher, Grigorios Chrysos, Fanghui Liu, Zhenyu Zhu

Neural Architecture Search (NAS) has fostered the automatic discovery of stateof- the-art neural architectures. Despite the progress achieved with NAS, so far there is little attention to theoretical guarantees on NAS. In this work, we study the generaliza ...

2022

OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Association

Alexandre Massoud Alahi, Sven Kreiss, Lorenzo Bertoni

Many image-based perception tasks can be formulated as detecting, associating and tracking semantic keypoints, e.g., human body pose estimation and tracking. In this work, we present a general framework that jointly detects and forms spatio-temporal keypoi ...

IEEE Institute of Electrical and Electronics Engineers2022

Relative stability toward diffeomorphisms indicates performance in deep nets

Matthieu Wyart, Alessandro Favero, Mario Geiger, Leonardo Petrini

Understanding why deep nets can classify data in large dimensions remains a challenge. It has been proposed that they do so by becoming stable to diffeomorphisms, yet existing empirical measurements support that it is often not the case. We revisit this qu ...

2021

Counting People by Estimating People Flows

Pascal Fua, Mathieu Salzmann, Weizhe Liu

Modern methods for counting people in crowded scenes rely on deep networks to estimate people densities in individual images. As such, only very few take advantage of temporal consistency in video sequences, and those that do only impose weak smoothness co ...

2022

Medical Image Classification Using Generalized Zero Shot Learning

Seyedbehzad Bozorgtabar

In many real world medical image classification settings we do not have access to samples of all possible disease classes, while a robust system is expected to give high performance in recognizing novel test data. We propose a generalized zero shot learnin ...

IEEE COMPUTER SOC2021

Gem5-X : A Many-Core Heterogeneous Simulation Platform for Architectural Exploration and Optimization

David Atienza Alonso, Marina Zapater Sancho, William Andrew Simon, Yasir Mahmood Qureshi

The increasing adoption of smart systems in our daily life has led to the development of new applications with varying performance and energy constraints, and suitable computing architectures need to be developed for these new applications. In this paper, ...

2021

Consensus Control for Decentralized Deep Learning

Martin Jaggi, Tao Lin, Sebastian Urban Stich, Anastasiia Koloskova, Lingjing Kong

Decentralized training of deep learning models enables on-device learning over networks, as well as efficient scaling to large compute clusters. Experiments in earlier works reveal that, even in a data-center setup, decentralized training often suffers fro ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2021

Weakly Supervised Deep Learning Methods for Biomicroscopy

Adrian Shajkofci

Optical microscopy, an invaluable tool in biology and medicine to observe and quantify cellular function, organ development, or disease mechanisms, requires constant trade-offs between spatial, temporal, and spectral resolution, invasiveness, acquisition t ...

EPFL2021

Landmark Regularization: Ranking Guided Super-Net Training in Neural Architecture Search

Mathieu Salzmann, Kaicheng Yu

Weight sharing has become a de facto standard in neural architecture search because it enables the search to be done on commodity hardware. However, recent works have empirically shown a ranking disorder between the performance of stand-alone architectures ...

IEEE COMPUTER SOC2021

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