Publications associées à Meta-learning (computer science)

A fast machine learning model for large-scale estimation of annual solar irradiation on rooftops

Roberto Castello, Jean-Louis Scartezzini, Nahid Mohajeri Pour Rayeni, Alina Ursula Rosa Walch

Rooftop-mounted solar photovoltaics have shown to be a promising technology to provide clean electricity in urban areas. Several large-scale studies have thus been conducted in different countries and cities worldwide to estimate their PV potential for the ...

International Solar Energy Society ISES2020

Accelerating Gradient Boosting Machines

Sai Praneeth Reddy Karimireddy

Gradient Boosting Machine (GBM) introduced by Friedman (2001) is a widely popular ensembling technique and is routinely used in competitions such as Kaggle and the KDD-Cup (Chen and Guestrin, 2016). In this work, we propose an Accelerated Gradient Boosting ...

ADDISON-WESLEY PUBL CO2020

Supplementary Material - AL2: Progressive Activation Loss for Learning General Representations in Classification Neural Networks

Sabine Süsstrunk, Majed El Helou, Frederike Dümbgen

In this supplementary material, we present the details of the neural network architecture and training settings used in all our experiments. This holds for all experiments presented in the main paper as well as in this supplementary material. We also show ...

2020

Optimization for Reinforcement Learning: From a single agent to cooperative agents

Volkan Cevher

Fueled by recent advances in deep neural networks, reinforcement learning (RL) has been in the limelight because of many recent breakthroughs in artificial intelligence, including defeating humans in games (e.g., chess, Go, StarCraft), self-driving cars, s ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Scaling description of generalization with number of parameters in deep learning

Matthieu Wyart, Clément Hongler, Mario Geiger, Stefano Spigler, Levent Dogus Sagun, Franck Raymond Gabriel, Arthur Jacot

Supervised deep learning involves the training of neural networks with a large number N of parameters. For large enough N, in the so-called over-parametrized regime, one can essentially fit the training data points. Sparsitybased arguments would suggest th ...

IOP PUBLISHING LTD2020

Deep learning assisted image transmission in multimode fibers

Demetri Psaltis, Christophe Moser, Damien Claude-Marie Loterie, Babak Rahmani, Georgia Konstantinou

We propose a data -driven approach for light transmission control inside multimode fibers (MMFs). Specifically, we show that a convolutional neural network is able to reconstruct amplitude/phase modulated images from scrambled amplitude -only images obtain ...

SPIE-INT SOC OPTICAL ENGINEERING2019

One-shot learning and behavioral eligibility traces in sequential decision making

Michael Herzog, Wulfram Gerstner, Kerstin Preuschoff, Marco Philipp Lehmann, He Xu, Vasiliki Liakoni

In many daily tasks, we make multiple decisions before reaching a goal. In order to learn such sequences of decisions, a mechanism to link earlier actions to later reward is necessary. Reinforcement learning (RL) theory suggests two classes of algorithms s ...

2019

Fast and Accurate Uncertainty Estimation in Chemical Machine Learning

Michele Ceriotti, Félix Benedito Clément Musil, Michael John Willatt

We present a scheme to obtain an inexpensive and reliable estimate of the uncertainty associated with the predictions of a machine-learning model of atomic and molecular properties. The scheme is based on resampling, with multiple models being generated ba ...

2019

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Meta-learning (computer science)

A fast machine learning model for large-scale estimation of annual solar irradiation on rooftops

Accelerating Gradient Boosting Machines

Supplementary Material - AL2: Progressive Activation Loss for Learning General Representations in Classification Neural Networks

Optimization for Reinforcement Learning: From a single agent to cooperative agents

Scaling description of generalization with number of parameters in deep learning

Incremental Transfer Learning in Two-pass Information Bottleneck Based Speaker Diarization System for Meetings

Deep learning assisted image transmission in multimode fibers

One-shot learning and behavioral eligibility traces in sequential decision making

Fast and Accurate Uncertainty Estimation in Chemical Machine Learning

A Rapid Form of Offline Consolidation in Skill Learning

Graph Chatbot

Chattez avec Graph Search

Incremental Transfer Learning in Two-pass Information Bottleneck Based Speaker Diarization System for Meetings

Supplementary Material - AL2: Progressive Activation Loss for Learning General Representations in Classification Neural Networks

Accelerating Gradient Boosting Machines

Fast and Accurate Uncertainty Estimation in Chemical Machine Learning

A Rapid Form of Offline Consolidation in Skill Learning

Optimization for Reinforcement Learning: From a single agent to cooperative agents

A fast machine learning model for large-scale estimation of annual solar irradiation on rooftops

Scaling description of generalization with number of parameters in deep learning

Deep learning assisted image transmission in multimode fibers

One-shot learning and behavioral eligibility traces in sequential decision making