Concept

Weak supervision

Publications associées (145)

TTT++: When Does Self-Supervised Test-Time Training Fail or Thrive?

Alexandre Massoud Alahi, Yuejiang Liu, Bastien Germain C. Van Delft, Taylor Ferdinand Mordan, Parth Ashit Kothari

Test-time training (TTT) through self-supervised learning (SSL) is an emerging paradigm to tackle distributional shifts. Despite encouraging results, it remains unclear when this approach thrives or fails. In this work, we first provide an in-depth look at ...

2021

Comparing CTC and LFMMI for out-of-domain adaptation of wav2vec 2.0 acoustic model

Hervé Bourlard, Apoorv Vyas

In this work, we investigate if the wav2vec 2.0 self-supervised pretraining helps mitigate the overfitting issues with connectionist temporal classification (CTC) training to reduce its performance gap with flat-start lattice-free MMI (E2E-LFMMI) for autom ...

ISCA-INT SPEECH COMMUNICATION ASSOC2021

Classifying high-dimensional Gaussian mixtures: Where kernel methods fail and neural networks succeed

Florent Gérard Krzakala, Lenka Zdeborová, Maria Refinetti

A recent series of theoretical works showed that the dynamics of neural networks with a certain initialisation are well-captured by kernel methods. Concurrent empirical work demonstrated that kernel methods can come close to the performance of neural netwo ...

PMLR2021

Context-Aware Learning for Generative Models

José del Rocio Millán Ruiz, Ricardo Andres Chavarriaga Lozano, Robert Leeb, Serafeim Perdikis

This work studies the class of algorithms for learning with side-information that emerges by extending generative models with embedded context-related variables. Using finite mixture models (FMMs) as the prototypical Bayesian network, we show that maximum- ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

Fast screening of covariates in population models empowered by machine learning

Jan Sickmann Hesthaven, Nadia Terranova

One of the objectives of Pharmacometry (PMX) population modeling is the identification of significant and clinically relevant relationships between parameters and covariates. Here, we demonstrate how this complex selection task could benefit from supervise ...

2021

Efficient Configuration Exploration in Inverse Dynamics Acquisition of Robotic Manipulators

Aude Billard, Sthithpragya Gupta, Walid Amanhoud, Farshad Khadivar

The inverse dynamics of a robotic manipulator is instrumental in precise robot control and manipulation. However, acquiring such a model is challenging, not only due to unmodelled non-linearities such as joint friction, but also from a machine learning per ...

IEEE2021

Self-Supervised Prototypical Transfer Learning for Few-Shot Classification

Matthias Grossglauser, Arnout Jan J Devos, Carlos Roberto Medina Temme

Recent advances in transfer learning and few-shot learning largely rely on annotated data related to the goal task during (pre-)training. However, collecting sufficiently similar and annotated data is often infeasible. Building on advances in self-supervis ...

2020

Unsupervised classification of snowflake images using a generative adversarial network and K-medoids classification

Alexis Berne, Jussi Sakari Leinonen

The increasing availability of sensors imaging cloud and precipitation particles, like the Multi-Angle Snowflake Camera (MASC), has resulted in datasets comprising millions of images of falling snowflakes. Automated classification is required for effective ...

2020

Unsupervised Representation Learning for Gaze Estimation

Jean-Marc Odobez, Yu Yu

Although automatic gaze estimation is very important to a large variety of application areas, it is difficult to train accurate and robust gaze models, in great part due to the difficulty in collecting large and diverse data (annotating 3D gaze is expensiv ...

IEEE2020

The Devil is in the Detail: A Framework for Macroscopic Prediction via Microscopic Models

Negar Kiyavash

Macroscopic data aggregated from microscopic events are pervasive in machine learning, such as country-level COVID-19 infection statistics based on city-level data. Yet, many existing approaches for predicting macroscopic behavior only use aggregated data, ...

2020

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