Publications associées à Learning rate

Distributed Learning in Non-Convex Environments-Part II: Polynomial Escape From Saddle-Points

The diffusion strategy for distributed learning from streaming data employs local stochastic gradient updates along with exchange of iterates over neighborhoods. In Part I [3] of this work we established that agents cluster around a network centroid and pr ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them

Sabine Süsstrunk, Mathieu Salzmann, Tao Lin, Chen Liu

We analyze the influence of adversarial training on the loss landscape of machine learning models. To this end, we first provide analytical studies of the properties of adversarial loss functions under different adversarial budgets. We then demonstrate tha ...

2021

Visual Focus of Attention Estimation in 3D Scene with an Arbitrary Number of Targets

Jean-Marc Odobez, Rémy Alain Siegfried

Visual Focus of Attention (VFOA) estimation in conversation is challenging as it relies on difficult to estimate information (gaze) combined with scene features like target positions and other contextual information (speaking status) allowing to disambigua ...

IEEE COMPUTER SOC2021

Landscape and training regimes in deep learning

Matthieu Wyart, Mario Geiger, Leonardo Petrini

Deep learning algorithms are responsible for a technological revolution in a variety oftasks including image recognition or Go playing. Yet, why they work is not understood.Ultimately, they manage to classify data lying in high dimension – a feat generical ...

2021

Adaptive Social Learning

Ali H. Sayed, Virginia Bordignon

This work proposes a novel strategy for social learning by introducing the critical feature of adaptation. In social learning, several distributed agents update continually their belief about a phenomenon of interest through: i) direct observation of strea ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

Distributed Learning in Non-Convex Environments-Part I: Agreement at a Linear Rate

Ali H. Sayed, Stefan Vlaski

Driven by the need to solve increasingly complex optimization problems in signal processing and machine learning, there has been increasing interest in understanding the behavior of gradient-descent algorithms in non-convex environments. Most available wor ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

From Learning to Memory: A Comparison Between Verbal and Non-verbal Skills in 22q11.2 Deletion Syndrome

Maude Schneider, Karin Bortolin

Background: Previous studies on possible memory deficits in 22q11DS often focused on quantifying the information memorized, whereas learning processes have been mostly overlooked. Furthermore, methodological differences in task design have made verbal and ...

FRONTIERS MEDIA SA2021

Deep Compositional Denoising for High-quality Monte Carlo Rendering

Thijs Vogels, Marios Papas, Xianyao Zhang

We propose a deep-learning method for automatically decomposing noisy Monte Carlo renderings into components that kernelpredicting denoisers can denoise more effectively. In our model, a neural decomposition module learns to predict noisy components and co ...

WILEY2021

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Learning rate

Distributed Learning in Non-Convex Environments-Part II: Polynomial Escape From Saddle-Points

On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them

Visual Focus of Attention Estimation in 3D Scene with an Arbitrary Number of Targets

Landscape and training regimes in deep learning

Adaptive Social Learning

Ageing and reinforcement learning

Distributed Learning in Non-Convex Environments-Part I: Agreement at a Linear Rate

On the relation between landscape beauty and land cover: A case study in the U.K. at Sentinel-2 resolution with interpretable AI

From Learning to Memory: A Comparison Between Verbal and Non-verbal Skills in 22q11.2 Deletion Syndrome

Deep Compositional Denoising for High-quality Monte Carlo Rendering

Graph Chatbot

Chattez avec Graph Search

Visual Focus of Attention Estimation in 3D Scene with an Arbitrary Number of Targets

Adaptive Social Learning

Distributed Learning in Non-Convex Environments-Part II: Polynomial Escape From Saddle-Points

Landscape and training regimes in deep learning

From Learning to Memory: A Comparison Between Verbal and Non-verbal Skills in 22q11.2 Deletion Syndrome

On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them

Distributed Learning in Non-Convex Environments-Part I: Agreement at a Linear Rate

Ageing and reinforcement learning

Deep Compositional Denoising for High-quality Monte Carlo Rendering