eng.au.dk 的前十大競爭對手

kaggle.com

Discover what actually works in AI. Join millions of builders, researchers, and labs evaluating agents, models, and frontier technology through crowdsourced benchmarks, competitions, and hackathons.

arxiv.org

A system of non-tradable credits that flow between individuals like karma, hence proposed under that name, is a mechanism for repeated resource allocation that comes with attractive efficiency and fairness properties, in theory. In this study, we test karma in an online experiment in which human subjects repeatedly compete for a resource with time-varying and stochastic individual preferences or urgency to acquire the resource. We confirm that karma has significant and sustained welfare benefits even in a population with no prior training. We identify mechanism usage in contexts with sporadic high urgency, more so than with frequent moderate urgency, and implemented as a simple (binary) karma bidding scheme as particularly effective for welfare improvements: relatively larger aggregate efficiency gains are realized that are (almost) Pareto superior. These findings provide guidance for further testing and for future implementation plans of such mechanisms in the real world.

huggingface.co

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

forums.fast.ai

[ Edição do 24/03/2018 : thread “Paperspace setup help” a ler ] [ Edição do 26/03/2018 : há 2 possibilidades para criar uma máquina no PaperSpace usando os jupyter notebooks do Fast.ai mas o passo-a-passo do @lucasosouza usando o Ubuntu 16.04 with Fast.ai Script é mais rápido porque não precisa pedir a “permissão” a PaperSpace ] Hoje de manhã em nosso primeiro encontro, usamos o Crestle que foi bem útil. Não criamos uma conta no PaperSpace conforme explicado nesta parte do video da lição 1 p...

catalyzex.com

CCA-MDD: A Coupled Cross-Attention based Framework for Streaming Mispronunciation detection and diagnosis: Paper and Code. End-to-end models are becoming popular approaches for mispronunciation detection and diagnosis (MDD). A streaming MDD framework which is demanded by many practical applications still remains a challenge. This paper proposes a streaming end-to-end MDD framework called CCA-MDD. CCA-MDD supports online processing and is able to run strictly in real-time. The encoder of CCA-MDD consists of a conv-Transformer network based streaming acoustic encoder and an improved cross-attention named coupled cross-attention (CCA). The coupled cross-attention integrates encoded acoustic features with pre-encoded linguistic features. An ensemble of decoders trained from multi-task learning is applied for final MDD decision. Experiments on publicly available corpora demonstrate that CCA-MDD achieves comparable performance to published offline end-to-end MDD models.

researchgate.net

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academia.edu

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scholar.google.com

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towardsdatascience.com

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github.com

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