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#Hype #Mines
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Introduced the idea of using pairs of word-like units extracted in an unsupervised way to provide a noisy top-down signal for representation learning from raw (untranscribed) speech. The learned representations capture phonetic distinctions better than standard (un-learned) features or those learned purely bottom-up. Others later applied this idea cross-lingually (Yuan et al., Interspeech 2016) and used it as a baseline for other approaches (He, Wang, and Livescu, ICLR 2017). This paper focussed on engineering applications, but led to later funding from NSF and ESRC to explore the idea introduced here as a model of perceptual learning in infants.Introduced the idea of using pairs of word-like units extracted in an unsupervised way to provide a noisy top-down signal for representation learning from raw (untranscribed) speech. The learned representations capture phonetic distinctions better than standard (un-learned) features or those learned purely bottom-up. Others later applied this idea cross-lingually (Yuan et al., Interspeech 2016) and used it as a baseline for other approaches (He, Wang, and Livescu, ICLR 2017). This paper focussed on engineering applications, but led to later funding from NSF and ESRC to explore the idea introduced here as a model of perceptual learning in infants.Introduced the idea of using pairs of word-like units extracted in an unsupervised way to provide a noisy top-down signal for representation learning from raw (untranscribed) speech. The learned representations capture phonetic distinctions better than standard (un-learned) features or those learned purely bottom-up. Others later applied this idea cross-lingually (Yuan et al., Interspeech 2016) and used it as a baseline for other approaches (He, Wang, and Livescu, ICLR 2017). This paper focussed on engineering applications, but led to later funding from NSF and ESRC to explore the idea introduced here as a model of perceptual learning in infants.Introduced the idea of using pairs of word-like units extracted in an unsupervised way to provide a noisy top-down signal for representation learning from raw (untranscribed) speech. The learned representations capture phonetic distinctions better than standard (un-learned) features or those learned purely bottom-up. Others later applied this idea cross-lingually (Yuan et al., Interspeech 2016) and used it as a baseline for other approaches (He, Wang, and Livescu, ICLR 2017). This paper focussed on engineering applications, but led to later funding from NSF and ESRC to explore the idea introduced here as a model of perceptual learning in infants.Introduced the idea of using pairs of word-like units extracted in an unsupervised way to provide a noisy top-down signal for representation learning from raw (untranscribed) speech. The learned representations capture phonetic distinctions better than standard (un-learned) features or those learned purely bottom-up. Others later applied this idea cross-lingually (Yuan et al., Interspeech 2016) and used it as a baseline for other approaches (He, Wang, and Livescu, ICLR 2017). This paper focussed on engineering applications, but led to later funding from NSF and ESRC to explore the idea introduced here as a model of perceptual learning in infants.Introduced the idea of using pairs of word-like units extracted in an unsupervised way to provide a noisy top-down signal for representation learning from raw (untranscribed) speech. The learned representations capture phonetic distinctions better than standard (un-learned) features or those learned purely bottom-up. Others later applied this idea cross-lingually (Yuan et al., Interspeech 2016) and used it as a baseline for other approaches (He, Wang, and Livescu, ICLR 2017). This paper focussed on engineering applications, but led to later funding from NSF and ESRC to explore the idea introduced here as a model of perceptual learning in infants.Introduced the idea of using pairs of word-like units extracted in an unsupervised way to provide a noisy top-down signal for representation learning from raw (untranscribed) speech. The learned representations capture phonetic distinctions better than standard (un-learned) features or those learned purely bottom-up. Others later applied this idea cross-lingually (Yuan et al., Interspeech 2016) and used it as a baseline for other approaches (He, Wang, and Livescu, ICLR 2017). This paper focussed on engineering applications, but led to later funding from NSF and ESRC to explore the idea introduced here as a model of perceptual learning in infants.Introduced the idea of using pairs of word-like units extracted in an unsupervised way to provide a noisy top-down signal for representation learning from raw (untranscribed) speech. The learned representations capture phonetic distinctions better than standard (un-learned) features or those learned purely bottom-up. Others later applied this idea cross-lingually (Yuan et al., Interspeech 2016) and used it as a baseline for other approaches (He, Wang, and Livescu, ICLR 2017). This paper focussed on engineering applications, but led to later funding from NSF and ESRC to explore the idea introduced here as a model of perceptual learning in infants.Introduced the idea of using pairs of word-like units extracted in an unsupervised way to provide a noisy top-down signal for representation learning from raw (untranscribed) speech. The learned representations capture phonetic distinctions better than standard (un-learned) features or those learned purely bottom-up. Others later applied this idea cross-lingually (Yuan et al., Interspeech 2016) and used it as a baseline for other approaches (He, Wang, and Livescu, ICLR 2017). This paper focussed on engineering applications, but led to later funding from NSF and ESRC to explore the idea introduced here as a model of perceptual learning in infants.
Hype Mines Minecraft Server IP, Version and Information
Server IP: Hype1mines.minehut.gg
Max Players: 0
Online Players: 0
Server Version: Velocity 1.7.2-1.20.4
Message of The Day: Server is offline.
Start via the lobby with /join
Where is Hype Mines Minecraft Server Located?
Country: Canada
City: Toronto
Minecraft Server Hosting Provided by Cloudflare, Inc.
How do I play Hype Mines?
- Launch your Minecraft game.
- Choose “Multiplayer” from the main menu.
- Click “Add Server” and enter the server details.
- Server Address: Hype1mines.minehut.gg
- Click “Done” and then “Join Server” to play!