Publications associées à Hinge loss

On the Generalization of Stochastic Gradient Descent with Momentum

While momentum-based accelerated variants of stochastic gradient descent (SGD) are widely used when training machine learning models, there is little theoretical understanding on the generalization error of such methods. In this work, we first show that th ...

Brookline2024

Dynamic Modeling of an Origami Prismatic Joint

Jamie Paik, Mustafa Mete, Jian-Lin Huang

Dynamic modeling of folding joints is critical for predicting dynamic behavior, optimizing design parameters, and developing control strategies for origami robots and machines. Although kinematics of the folded joints exists, little research describes thei ...

IEEE2023

Parametrized classifiers for optimal EFT sensitivity

Andrea Wulzer, Alfredo Glioti, Siyu Chen

We study unbinned multivariate analysis techniques, based on Statistical Learning, for indirect new physics searches at the LHC in the Effective Field Theory framework. We focus in particular on high-energy ZW production with fully leptonic decays, modeled ...

SPRINGER2021

Deep visual Re-identification with confidence

Alexandre Massoud Alahi, Sven Kreiss, George Adaimi

Transportation systems often rely on understanding the flow of vehicles or pedestrian. From traffic monitoring at the city scale, to commuters in train terminals, recent progress in sensing technology make it possible to use cameras to better understand th ...

2021

Generalization error in high-dimensional perceptrons: Approaching Bayes error with convex optimization

Florent Gérard Krzakala, Lenka Zdeborová

We consider a commonly studied supervised classification of a synthetic dataset whose labels are generated by feeding a one-layer non-linear neural network with random iid inputs. We study the generalization performances of standard classifiers in the high ...

Curran Associates, Inc.2020

Second-Order Guarantees In Federated Learning

Ali H. Sayed, Stefan Vlaski, Elsa Rizk

Federated learning is a useful framework for centralized learning from distributed data under practical considerations of heterogeneity, asynchrony, and privacy. Federated architectures are frequently deployed in deep learning settings, which generally giv ...

IEEE2020

Chemical machine learning with kernels: The impact of loss functions

Volkan Cevher, Quoc Tran Dinh, Sandip De, Junhong Lin

Machine learning promises to accelerate materials discovery by allowing computational efficient property predictions from a small number of reference calculations. As a result, the literature has spent a considerable effort in designing representations tha ...

2019

Deep Visual Re-Identification with Confidence

Alexandre Massoud Alahi, Sven Kreiss, George Adaimi

2019

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Hinge loss

On the Generalization of Stochastic Gradient Descent with Momentum

Dynamic Modeling of an Origami Prismatic Joint

Gradient boosting with extreme-value theory for wildfire prediction

STORM+: Fully Adaptive SGD with Momentum for Nonconvex Optimization

Parametrized classifiers for optimal EFT sensitivity

Deep visual Re-identification with confidence

Generalization error in high-dimensional perceptrons: Approaching Bayes error with convex optimization

Second-Order Guarantees In Federated Learning

Chemical machine learning with kernels: The impact of loss functions

Deep Visual Re-Identification with Confidence

Graph Chatbot

Chattez avec Graph Search

Gradient boosting with extreme-value theory for wildfire prediction

Dynamic Modeling of an Origami Prismatic Joint

On the Generalization of Stochastic Gradient Descent with Momentum

STORM+: Fully Adaptive SGD with Momentum for Nonconvex Optimization

Generalization error in high-dimensional perceptrons: Approaching Bayes error with convex optimization

Deep visual Re-identification with confidence

Second-Order Guarantees In Federated Learning

Parametrized classifiers for optimal EFT sensitivity

Chemical machine learning with kernels: The impact of loss functions

Deep Visual Re-Identification with Confidence