GPU-Trained System Understands Movies

The questions range from simpler ‘Who’ did ‘What’ to ‘Whom’ that can be solved by computer vision alone, to ‘Why’ and ‘How’ something happened in the movie, questions that can only be solved by exploiting both the visual information and dialogs.

https://news.developer.nvidia.com/gpu-trained-system-understands-movies/

Another paper about awesome application of Deep Learning. Now it is able to identify tumors.

The morphology of glands has been used routinely by pathologists to assess the malignancy degree of adenocarcinomas. Accurate segmentation of glands from histology images is a crucial step to obtain reliable morphological statistics for quantitative diagnosis. In this paper, we proposed an effective deep contour-aware network (DCAN) to solve this challenging problem under a unified multi-task learning framework. In the proposed network, multi-level contextual features from the hierarchical architecture are explored with auxiliary supervision for accurate gland segmentation. When incorporated with multi-task regularization during the training, the discriminative capability of intermediate features can be further improved. Moreover, our network can not only output accurate probability maps of glands, but also depict clear contours simultaneously for separating cluttered objects, which further boosts the gland segmentation performance. This unified framework can be efficient when applied to large-scale histopathological data without resorting to additional post-separating steps based on low-level cues. Our method (CUMedVision Team) won the 2015 MICCAI Gland Segmentation Challenge out of 13 competitive teams (photo of top teams), surpassing all the other methods by a significant margin.

http://appsrv.cse.cuhk.edu.hk/~hchen/research/2015miccai_gland.html

https://github.com/baidu-research/warp-ctc

Baidu-Research (Andrew Ng) ctc implementation

If you have any news to suggest, you can write @malev

There was a questions about nice data science MOOCs.

So there are a few links to get an idea.

First of all, post at medium:

https://medium.com/@moocaholic/my-60-moocs-the-program-555c2031e4e8#.glsu2s9mw

Second, there is a curated list of awesome data science links, including MOOCs:

https://github.com/okulbilisim/awesome-datascience

Third:

Basic, state-of-the-art and best MOOCs are Andrew Ngs Machine Learning and Hinton's Neural Networks.

Kinda AMA with Yoshua Bengio on Quora:
https://www.quora.com/profile/Yoshua-Bengio/session/37/

This is your chance to ask something you don't know about and always been dreeming to ask.

Now phones can record sound with gyroscope. Be careful.

https://crypto.stanford.edu/gyrophone/

Nice infographic on apple app charts

https://medium.com/i-data/apple-s-app-charts-2015-data-and-trends-abb95300df57

<3

New startup by David Yan implements natural language processing for search.

Much like Facebook search interface, available in US English language.

https://findo.io/

Zukerberg sits 20 feet from people who are working on Facebook AI
https://www.facebook.com/zuck/posts/10102619979696481

http://www.nature.com/nature/journal/v529/n7587/full/nature16961.html

Go is no more a puzzle.

There is a Luka — asisstent to pick places nearby. Works only in SF, but it looks promicing: http://luka.ai

Paper about recent AlphaGo solution.

https://storage.googleapis.com/deepmind-data/assets/papers/deepmind-mastering-go.pdf

A very important picture from a recent LeCun CVPR. Important for those, who want to study QA / Dialogue systems.

Deep residual networks from Microsoft Research Asia (MSRA).

https://github.com/KaimingHe/deep-residual-networks

Deep learning methods are able to automatically discover better representations of the data to improve the performance of the classifiers. However, in computer vision tasks, such as the gender recognition problem, sometimes it is difficult to directly learn from the entire image. In this work we propose a new model called Local Deep Neural Network (Local-DNN), which is based on two key concepts: local features and deep architectures. The model learns from small overlapping regions in the visual field using discriminative feed-forward networks with several layers. We evaluate our approach on two well-known gender benchmarks, showing that our Local-DNN outperforms other deep learning methods also evaluated and obtains state-of-the-art results in both benchmarks.

http://www.sciencedirect.com/science/article/pii/S0167865515004018