Publications associées à Meta-learning (computer science)

Predicting Superagers by Machine Learning Classification Based on the Functional Brain Connectome Using Resting-State Functional Magnetic Resonance Imaging

Chang-Hyun Park

Superagers are defined as older adults who have youthful memory performance comparable to that of middle-aged adults. Classifying superagers based on the brain connectome using machine learning modeling can provide important insights on the physiology unde ...

OXFORD UNIV PRESS INC2021

System and method for privacy-preserving distributed training of machine learning models on distributed datasets

Jean-Pierre Hubaux, Juan Ramón Troncoso-Pastoriza, Jean-Philippe Léonard Bossuat, Apostolos Pyrgelis, David Jules Froelicher, Joao André Gomes de Sá e Sousa, Sinem Sav

A computer-implemented method and a distributed computer system (100) for privacy- preserving distributed training of a global model on distributed datasets (DS1 to DSn). The system has a plurality of data providers (DP1 to DPn) being communicatively coupl ...

2021

Self-supervised Pre-training Enhances Change Detection in Sentinel-2 Imagery

Devis Tuia, Diego Marcos Gonzalez

While annotated images for change detection using satellite imagery are scarce and costly to obtain, there is a wealth of unlabeled images being generated every day. In order to leverage these data to learn an image representation more adequate for change ...

Springer, Cham2021

Robust Reinforcement Learning via Adversarial training with Langevin Dynamics

Volkan Cevher, Paul Thierry Yves Rolland, Ya-Ping Hsieh, Yu-Ting Huang, Kamalaruban Parameswaran, Cheng Shi

We introduce a sampling perspective to tackle the challenging task of training robust Reinforcement Learning (RL) agents. Leveraging the powerful Stochastic Gradient Langevin Dynamics, we present a novel, scalable two-player RL algorithm, which is a sampli ...

2020

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

Practical Low-Rank Communication Compression in Decentralized Deep Learning

Martin Jaggi, Sai Praneeth Reddy Karimireddy, Thijs Vogels

Lossy gradient compression has become a practical tool to overcome the communication bottleneck in centrally coordinated distributed training of machine learning models. However, algorithms for decentralized training with compressed communication over arbi ...

2020

Multitask Learning Over Graphs: An Approach for Distributed, Streaming Machine Learning

Ali H. Sayed, Jie Chen, Stefan Vlaski, Roula Nassif

The problem of simultaneously learning several related tasks has received considerable attention in several domains, especially in machine learning, with the so-called multitask learning (MTL) problem, or learning to learn problem [1], [2]. MTL is an appro ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Generative adversarial training of product of policies for robust and adaptive movement primitives

Sylvain Calinon, Emmanuel Pignat, Hakan Girgin

In learning from demonstrations, many generative models of trajectories make simplifying assumptions of independence. Correctness is sacrificed in the name of tractability and speed of the learning phase. The ignored dependencies, which are often the kinem ...

2020

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Meta-learning (computer science)

Learning Saliency From Single Noisy Labelling: A Robust Model Fitting Perspective

Predicting Superagers by Machine Learning Classification Based on the Functional Brain Connectome Using Resting-State Functional Magnetic Resonance Imaging

System and method for privacy-preserving distributed training of machine learning models on distributed datasets

Self-supervised Pre-training Enhances Change Detection in Sentinel-2 Imagery

Robust Reinforcement Learning via Adversarial training with Langevin Dynamics

A meta-learning approach for genomic survival analysis

Self-Supervised Prototypical Transfer Learning for Few-Shot Classification

Practical Low-Rank Communication Compression in Decentralized Deep Learning

Multitask Learning Over Graphs: An Approach for Distributed, Streaming Machine Learning

Generative adversarial training of product of policies for robust and adaptive movement primitives

Graph Chatbot

Chattez avec Graph Search

Self-Supervised Prototypical Transfer Learning for Few-Shot Classification

Learning Saliency From Single Noisy Labelling: A Robust Model Fitting Perspective

A meta-learning approach for genomic survival analysis

Self-supervised Pre-training Enhances Change Detection in Sentinel-2 Imagery

Multitask Learning Over Graphs: An Approach for Distributed, Streaming Machine Learning

Robust Reinforcement Learning via Adversarial training with Langevin Dynamics

Practical Low-Rank Communication Compression in Decentralized Deep Learning

Generative adversarial training of product of policies for robust and adaptive movement primitives

Predicting Superagers by Machine Learning Classification Based on the Functional Brain Connectome Using Resting-State Functional Magnetic Resonance Imaging

System and method for privacy-preserving distributed training of machine learning models on distributed datasets