Query: rnn with attention

title: SeqViews2SeqLabels: Learning 3D Global Features via Aggregating Sequential Views by RNN With Attention
abstract: Learning 3D global features by aggregating multiple views has been introduced as a successful strategy for 3D shape analysis. In recent deep learning models with end to end training, pooling is a widely adopted procedure for view aggregation. However, pooling merely retains the max or mean value over all views, which disregards the content information of almost all views and also the spatial information among the views. To resolve these issues, we propose Sequential Views To Sequential Labels (SeqViews2SeqLabels) as a novel deep learning model with an encoder decoder structure based on recurrent neural networks (RNNs) with attention. SeqViews2SeqLabels consists of two connected parts, an encoder RNN followed by a decoder RNN, that aim to learn the global features by aggregating sequential views and then performing shape classification from the learned global features, respectively. Specifically, the encoder RNN learns the global features by simultaneously encoding the spatial and content information of sequential views, which captures the semantics of the view sequence. With the proposed prediction of sequential labels, the decoder RNN performs more accurate classification using the learned global features by predicting sequential labels step by step. Learning to predict sequential labels provides more and finer discriminative information among shape classes to learn, which alleviates the overfitting problem inherent in training using a limited number of 3D shapes. Moreover, we introduce an attention mechanism to further improve the discriminative ability of SeqViews2SeqLabels. This mechanism increases the weight of views that are distinctive to each shape class, and it dramatically reduces the effect of selecting the first view position. Shape classification and retrieval results under three large scale benchmarks verify that SeqViews2SeqLabels learns more discriminative global features by more effectively aggregating sequential views than state of the art methods.

title: Radar HRRP Target Recognition Model Based on a Stacked CNN Bi RNN With Attention Mechanism

title: Improving RNN with Attention and Embedding for Adverse Drug Reactions
abstract: Electronic Health Records (EHR) narratives are a rich source of information, embedding high resolution information of value to secondary research use. However, because the EHRs are mostly in natural language free text and highly ambiguity ridden, many natural language processing algorithms have been devised around them to extract meaningful structured information about clinical entities. The performance of the algorithms however, largely varies depending on the training dataset as well as the effectiveness of the use of background knowledge to steer the learning process. In this paper we study the impact of initializing the training of a neural network natural language processing algorithm with pre defined clinical word embeddings to improve feature extraction and relationship classification between entities. We add our embedding framework to a bi directional long short term memory (Bi LSTM) neural network, and further study the effect of using attention weights in neural networks for sequence labelling tasks to extract knowledge of Adverse Drug Reactions (ADRs) We incorporate unsupervised word embeddings using Word2Vec and GloVe from widely available medical resources such as Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) II corpora, Unified Medical Language System (UMLS) as well as embed pharmaco lexicon from available EHRs. Our algorithm, implemented using two datasets, shows that our architecture outperforms baseline Bi LSTM or Bi LSTM networks using linear chain and Skip Chain conditional random fields (CRF)

title: Dynamic Personalized Search Based on RNN with Attention Mechanism
abstract: Traditional search engines always return the same result when facing the same query for different users. Therefore, when a user issues an ambiguous query, the search result can't fully meet the user's demands. Personalized search is a practicable method to solve this problem. It has been proven to be able to improve the search quality of search engines. The majority of existing personalized search algorithms re rank the original results by comparing the similarity between documents and user profiles built based on historical user behaviors. These behaviors contain a wealth of information to reflect the user personalized preferences. However, most of them generally believes that user interests are stable and unchanged for a long time, which is inconsistent with the actual situation. In this paper, we argue that user information needs might dynamically change over time and sometimes the user information needs are random. Therefore, the users' recent search behaviors and the historical queries that are more similar to the current query contribute more to personalization than the others. This paper will take advantage of deep learning in feature extraction to build better user profiles. For solving the problem of Ji Suan Xue Bao Wang Luo 2019 06 25 10:46:50 Wang Luo http:/kns.cnki.net/kcms/detail/11.1826.TP.20190624.1709.002.html

title: Automotive Radar Signal Interference Mitigation Using RNN with Self Attention
abstract: Radar is a key component of autonomous driving. We can capture the target range and velocity using radar signal that is transmitted and reflected by a target. However, the noise floor increases when interference signals exist, which severely affects the detectability of target objects. For these reasons, many previous studies have proposed methods for cancelling interference or reconstructing original signals. However, little work has been done using deep learning algorithm on interference mitigation. In this paper, we propose a new method using deep learning. We improve the performance of the existing deep learning algorithm using attention mechanism. We applied our algorithm to the OFDM radar environment as well as the existing frequency modulated continuous wave(FMCW) radar. The experiment show our deep learning based method outperforms existing methods. The implementation of our paper is available at https:/github.com/jwmun/Radar.

title: Parallel Structure Deep Neural Network Using CNN and RNN with an Attention Mechanism for Breast Cancer Histology Image Classification
abstract: In this paper, we present a new deep learning model to classify hematoxylin eosin stained breast biopsy images into four classes (normal tissues, benign lesions, in situ carcinomas, and invasive carcinomas) Our model uses a parallel structure consist of a convolutional neural network (CNN) and a recurrent neural network (RNN) for image feature extraction, which is greatly different from the common existed serial method of extracting image features by CNN and then inputting them into RNN. Then, we introduce a special perceptron attention mechanism, which is derived from the natural language processing (NLP) field, to unify the features extracted by the two different neural network structures of the model. In the convolution layer, general batch normalization is replaced by the new switchable normalization method. And the latest regularization technology, targeted dropout, is used to substitute for the general dropout in the last three fully connected layers of the model. In the testing phase, we use the model fusion method and test time augmentation technology on three different datasets of hematoxylin eosin stained breast biopsy images. The results demonstrate that our model significantly outperforms state of the art methods.

title: Order Free RNN with Visual Attention for Multi Label Classification
abstract: In this paper, we propose the joint learning attention and recurrent neural network (RNN) models for multi label classification. While approaches based on the use of either model exist (e.g. for the task of image captioning) training such existing network architectures typically require pre defined label sequences. For multi label classification, it would be desirable to have a robust inference process, so that the prediction error would not propagate and thus affect the performance. Our proposed model uniquely integrates attention and Long Short Term Memory (LSTM) models, which not only addresses the above problem but also allows one to identify visual objects of interests with varying sizes without the prior knowledge of particular label ordering. More importantly, label co occurrence information can be jointly exploited by our LSTM model. Finally, by advancing the technique of beam search, prediction of multiple labels can be efficiently achieved by our proposed network model.

title: Personalizing Search Results Using Hierarchical RNN with Query aware Attention
abstract: Search results personalization has become an effective way to improve the quality of search engines. Previous studies extracted information such as past clicks, user topical interests, query click entropy and so on to tailor the original ranking. However, few studies have taken into account the sequential information underlying previous queries and sessions. Intuitively, the order of issued queries is important in inferring the real user interests. And more recent sessions should provide more reliable personal signals than older sessions. In addition, the previous search history and user behaviors should influence the personalization of the current query depending on their relatedness. To implement these intuitions, in this paper we employ a hierarchical recurrent neural network to exploit such sequential information and automatically generate user profile from historical data. We propose a query aware attention model to generate a dynamic user profile based on the input query. Significant improvement is observed in the experiment with data from a commercial search engine when compared with several traditional personalization models. Our analysis reveals that the attention model is able to attribute higher weights to more related past sessions after fine training.

title: Hierarchical RNN with Static Sentence Level Attention for Text Based Speaker Change Detection
abstract: Speaker change detection (SCD) is an important task in dialog modeling. Our paper addresses the problem of text based SCD, which differs from existing audio based studies and is useful in various scenarios, for example, processing dialog transcripts where speaker identities are missing (e.g. OpenSubtitle) and enhancing audio SCD with textual information. We formulate text based SCD as a matching problem of utterances before and after a certain decision point; we propose a hierarchical recurrent neural network (RNN) with static sentence level attention. Experimental results show that neural networks consistently achieve better performance than feature based approaches, and that our attention based model significantly outperforms non attention neural networks.

title: Generating Video Description using RNN with Semantic Attention
abstract: Being able to understand videos can have a great impact and can be useful to many other applications. However, generated descriptions by computers often fail to mention correct objects appearing in videos. This work aims to alleviate this problem by including external fine grained visual information detected from all video frames. In this paper, we propose an LSTM based sequence tosequence model with semantic attention for video description generation. The results show that using semantic attention to selectively focus on external fine grained visual information can guide the system to correctly mention objects in videos and have a better quality of video descriptions.