Benchmarking Learning Efficiency in Deep Reservoir Computing

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F22%3A00364196" target="_blank" >RIV/68407700:21730/22:00364196 - isvavai.cz</a>

Result on the web

<a href="https://proceedings.mlr.press/v199/cisneros22a/cisneros22a.pdf" target="_blank" >https://proceedings.mlr.press/v199/cisneros22a/cisneros22a.pdf</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Benchmarking Learning Efficiency in Deep Reservoir Computing

Original language description

It is common to evaluate the performance of a machine learning model by measuring its predictive power on a test dataset. This approach favors complicated models that can smoothly fit complex functions and generalize well from training data points. Although essential components of intelli gence, speed and data efficiency of this learning process are rarely reported or compared between different candidate models. In this paper, we introduce a benchmark of increasingly difficult tasks together with a data efficiency metric to measure how quickly machine learning models learn from training data. We compare the learning speed of some established sequential supervised models, such as RNNs, LSTMs, or Transformers, with relatively less known alternative models based on reservoir computing. The proposed tasks require a wide range of computational primitives, such as memory or the ability to compute Boolean functions, to be effectively solved. Surprisingly, we observe that reservoir computing systems that rely on dynamically evolving feature maps learn faster than fully supervised methods trained with stochastic gradient optimization while achieving comparable ac curacy scores. The code, benchmark, trained models, and results to reproduce our experiments are available at https://github.com/hugcis/benchmark_learning_efficiency/.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/EF15_003%2F0000468" target="_blank" >EF15_003/0000468: Intelligent Machine Perception</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings of The 1st Conference on Lifelong Learning Agents

ISBN

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ISSN

2640-3498

e-ISSN

2640-3498

Number of pages

16

Pages from-to

532-547

Publisher name

Proceedings of Machine Learning Research

Place of publication

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Event location

Montréal

Event date

Jul 29, 2022

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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