smhx

10 ms·

61.
by smhx 10y ago
I'm enthusiastic that Intel is working on optimizing Torch's Tensor and nn libraries, and optimizing Neural Nets in general. This repo is most definitely not ready for primetime. Two comments: 1. I haven't seen an Intel team
62.
by smhx 10y ago
Link to the paper: https://arxiv.org/abs/1607.01759 Quotes from the paper: Both char-CNN and VDCNN are trained on a NVIDIA Tesla K40 GPU, while our models are trained on a CPU using 20 threads. Table2 shows that method
63.
by smhx 10y ago
and that's when you dont do the optimization.
64.
by smhx 10y ago
The only objective response I'll give to this comment is the number of citations (as pointed out by another user) https://news.ycombinator.com/item?id=11885161 . It's not perfect, but I cant give a reasonable answ
65.
by smhx 10y ago
The video is almost cringe-worthy in it's factual inaccuracy around 2012. The reigniting of deep learning around 2012 was because of Krizhevsky, Sutskever & Hinton winning the Imagenet challenge (1000 object classes) Contrary to ho
66.
by smhx 11y ago
Being in the deep learning community, the number of papers appearing has been getting out of control. Most of the papers appear on arxiv and are of low quality. This is particularly problematic right before conference deadlines. Karpathy&#x
67.
by smhx 11y ago
there's a subtle transition between "deep learning masters very specialized skills" to "let's generalize this thought to everything possible". I work in deep learning and I think the article is more fear than f
68.
by smhx 11y ago
This tracks the current state of speech recognition. https://github.com/syhw/wer_are_we
69.
by smhx 11y ago
The corresponding UnrealEngine Torch plugin is released here: https://github.com/facebook/UETorch
70.
“Not so fast, FFT”: Winograd(nervanasys.com)
18 points by smhx 11y ago|0 comments
71.
by smhx 11y ago
I didn't mean it in a condescending way. I was stating that this is not algorithmically new, and is a reproduction of the paper. From the README of the repository, it is not obvious that this is a reproduction. The information is fairl
72.
by smhx 11y ago
This is simply a transpile / reproduction of the original Torch version from Deepmind, but in TensorFlow. It doesn't really do anything new or different compared to the paper by [Mnih et. al.]( http://www.nature.com&#x2F
73.
by smhx 11y ago
For people actively working full-time in the field, some of the papers which have simple but powerful ideas, reading the paper for 2 hours (or glancing at the key diagram / formula) is enough to implement it. For example: Deep Residual
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by smhx 11y ago
yes. more like 5-shot hold-out, and the held-out set is pretty big as well, so more legit.
75.
by smhx 11y ago
NVIDIA M40s, which are the new cards, so double that estimate.
76.
by smhx 11y ago
The input to the G-network is a Vector of numbers, of size K. The output is an image. Different random vectors give different output images. We are doing vector arithmetic in this input space, and the outputs seem to be semantically changin
77.
by smhx 11y ago
Both use GANs to generate images, and that similarity is indeed there. Original GAN paper: http://papers.nips.cc/paper/5423-generative-adversarial-nets
78.
by smhx 11y ago
I'm the one who runs the benchmarks. It's sad that you put such a twist to the whole thing. I've been running convnet-benchmarks forever now, and I've been running them independently on separate personal hardware. I do t
79.
by smhx 11y ago
I've done an apples to apples, TensorFlow + CuDNN R2 vs Torch + CuDNN R2
80.
by smhx 11y ago
They didn't release the multi-machine version of TensorFlow. They said they're still working on it and will release it when it's ready.
81.
by smhx 11y ago
I'm not able to see the full title of the posts (13" MBP Retina). It is cut off half-way. As a front-page for a HN-style site, that's a serious usability bug that you want to fix. http://imgur.com/DatjKpe
82.
by smhx 11y ago
Technical write-up on how this was done: http://larseidnes.com/2015/10/13/auto-generating-clickbait-w... Source code: https://github.com/larspars/word-rnn
83.
Click-o-tron – Clickbait site using deep learning(clickotron.com)
2 points by smhx 11y ago|1 comments
84.
by smhx 11y ago
I run convnet-benchmarks, and also work at Facebook. Unlike what the public perception is, NVIDIA, Facebook and other companies collaborate very very closely. NVIDIA's latest R3 release has FFT-based convolutions inspired by Facebook&#
85.
by smhx 11y ago
OpenCL neuralnet backend for the Torch framework. Supports all kinds of stuff. https://github.com/hughperkins/clnn
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by smhx 11y ago
Quoc Le is neither an author of the word2vec paper or in the committers list of word2vec software (there's only two committers, mikolov and sutskever). Not sure where you got that inference from.
87.
by smhx 11y ago
I used Lush for my homework as part of the Machine Learning course at NYU (taught by LeCun). It was pretty mature, mostly bug-free for general use. However, at that time, I never used a lisp-y language before that, and found it quite diffic
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by smhx 11y ago
Can you name a few such outsourcing shops that seem too good to be true (and likely are).
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by smhx 12y ago
Memory Networks: http://arxiv.org/abs/1410.3916
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by smhx 12y ago
For github projects, I've been using http://gitter.im I've found the integration to be awesome and well thought-out.

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