Thanks. We will look into what you have suggested.
We are working on Deep Learning as a Service (DLaaS) targeting light
users, and we will let you know once it is ready for testing purposes so
that you can provide us feedback as a user.
regards
beng chin
On 2016-01-17 23:29, Mohamed Yousef wrote:
CTC along with RNNs (LSTM/GRU) is currently state of the art in ASR
enough to say it is the technique used currently by Google
[http://googleresearch.blogspot.com.eg/2015/09/google-voice-search-faster-and-more.html]
and Baidu [http://arxiv.org/abs/1512.02595v1]
The technique also won competitions in handwriting recognition and are
very successful in OCR
I am currently only a user : D and didn't delve enough in the theory
but there are a number of Cpp implementations that should greatly
simplify the job:
1) wrap-ctc released as a fast Cpu/Gpu CTC library from Baidu 4 days
ago [https://github.com/baidu-research/warp-ctc]
2) RNNLIB, the reference CTC implementation by Alex Graves
[http://sourceforge.net/p/rnnl/wiki/Home/]
3) CLSTM, an OCR library based on LSTM+CTC by Thomas Breuel
[https://github.com/tmbdev/clstm]
________________________________________
From: [email protected] <[email protected]> on behalf of Wang
Wei <[email protected]>
Sent: Wednesday, January 13, 2016 12:41 PM
To: [email protected]
Cc: 钟鸣
Subject: Re: New features to be included in v0.3
Hi Mohamed
Yes. Only GRU has been added in v0.2. I think Zhongming (cc'ed) is
implementing LSTM.
It would be interesting to provide an example for acoustic modeling
using
CTC.
If you have experience of using CTC for acoustic modelling , it would
be
appreciated if you can help to add it in v0.3.
regards,
Wei
On Wed, Jan 13, 2016 at 5:41 PM, Mohamed Yousef
<[email protected]
wrote:
I would suggest implementing an LSTM layer and a CTC layer (LSTM was
planned for v0.2, but only GRU was added)
a CTC layer would greatly simplify acoustic modeling and many other
applications
________________________________________
From: [email protected] <[email protected]> on behalf of Wang
Wei <
[email protected]>
Sent: Wednesday, January 13, 2016 8:51 AM
To: [email protected]
Subject: New features to be included in v0.3
Hi all,
I have proposed some features for v0.3, which is scheduled to be
released
in March 2016.
The major goals include usability and performance improvements.
I have assigned some of the tasks to some developers/committers.
If you are interested in any of them, please join the development by
replying to that ticket.
I will add new tickets continuously.
You are also welcomed to create new tickets for other features about
usability and performance improvements.
Let's get started!
Thanks!
regards,
Wei
SINGA-136 <https://issues.apache.org/jira/browse/SINGA-136>
Support cuDNN v4 <https://issues.apache.org/jira/browse/SINGA-136>
Haibo Chen
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SINGA-135 <https://issues.apache.org/jira/browse/SINGA-135>
Improve hybrid partitioning and benchmark performance
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Sheng Wang
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SINGA-134 <https://issues.apache.org/jira/browse/SINGA-134>
Extend SINGA to run over a GPU cluster
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SINGA-133 <https://issues.apache.org/jira/browse/SINGA-133>
Implement message passing using MPI
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SINGA-132 <https://issues.apache.org/jira/browse/SINGA-132>
Optimize training on a single node with GPUs
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SINGA-131 <https://issues.apache.org/jira/browse/SINGA-131>
Implement and optimize hybrid training using both CPU and GPU
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SINGA-130 <https://issues.apache.org/jira/browse/SINGA-130>
Implement a layer subclass for data prefetching
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SINGA-129 <https://issues.apache.org/jira/browse/SINGA-129>
Add new SGD updaters <https://issues.apache.org/jira/browse/SINGA-129>
Jinyang Gao
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SINGA-128 <https://issues.apache.org/jira/browse/SINGA-128>
Update compilation options and scripts for installing SINGA on MacOS
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SINGA-127 <https://issues.apache.org/jira/browse/SINGA-127>
Clear optimization level of compilation for debugging
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Improve Python Binding for interactive training
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Improve Python Helper - additional features
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