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[1702.06106] An Attention-Based Deep Net for Learning to Rank
In information retrieval, learning to rank constructs a machine-based ranking model which given a query, sorts the search results by their degree of relevance or importance to the query. Neural networks have been successfully applied to this problem, and in this paper, we propose an attention-based deep neural network which better incorporates different embeddings of the queries and search results with an attention-based mechanism. This model also applies a decoder mechanism to learn the ranks of the search results in a listwise fashion. The embeddings are trained with convolutional neural networks or the word2vec model. We demonstrate the performance of this model with image retrieval and text querying data sets.
22 days ago by foodbaby
DeepTest: automated testing of deep-neural-network-driven autonomous cars | the morning paper
In this paper, we design, implement and evaluate DeepTest, a systematic testing tool for automatically detecting erroneous behaviors of DNN-driven vehicles that can potentially lead to fatal crashes. First, our tool is designed to automatically generated test cases leveraging real-world changes in driving conditions like rain, fog, lighting conditions, etc. DeepTest systematically explores different parts of the DNN logic by generating test inputs that maximize the numbers of activated neurons. DeepTest found thousands of erroneous behaviors under different realistic driving conditions (e.g., blurring, rain, fog, etc.) many of which lead to potentially fatal crashes in three top performing DNNs in the Udacity self-driving car challenge.
DNN  testing 
27 days ago by foodbaby
The mostly complete chart of Neural Networks, explained
The zoo of neural network types grows exponentially. One needs a map to navigate between many emerging architectures and approaches. Fortunately, Fjodor van Veen from Asimov institute compiled a…
ml  ai  analytics  big_data  chart  cheatsheet  data_science  deep-learning  deep_learning  dnn 
7 weeks ago by tranqy
Apollo Scape
RGB vidoes/images with per-pixel segmentation, 3D data
ml  dnn  data 
march 2018 by nrrd
[1802.10078] A Fast Deep Learning Model for Textual Relevance in Biomedical Information Retrieval
Publications in the life sciences are characterized by a large technical vocabulary, with many lexical and semantic variations for expressing the same concept. Towards addressing the problem of relevance in biomedical literature search, we introduce a deep learning model for the relevance of a document's text to a keyword style query. Limited by a relatively small amount of training data, the model uses pre-trained word embeddings. With these, the model first computes a variable-length Delta matrix between the query and document, representing a difference between the two texts, which is then passed through a deep convolution stage followed by a deep feed-forward network to compute a relevance score. This results in a fast model suitable for use in an online search engine. The model is robust and outperforms comparable state-of-the-art deep learning approaches.
IR  neural  papers  DNN 
march 2018 by foodbaby
RT : Intel Launches AI: In Production Programme for Movidius Devs.

MachineLearning  AI  DNN  from twitter
february 2018 by 9600

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