scholarly journals DeepFM: A Factorization-Machine based Neural Network for CTR Prediction

2017 ◽  
Author(s):  
Huifeng Guo ◽  
Ruiming TANG ◽  
Yunming Ye ◽  
Zhenguo Li ◽  
Xiuqiang He
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Feature Learning ◽  
High Order ◽  
Feature Engineering ◽  
Neural Network Architecture ◽  
User Behaviors ◽  
Feature Interactions ◽  
Deep Model ◽  
Proposed Model

Learning sophisticated feature interactions behind user behaviors is critical in maximizing CTR for recommender systems. Despite great progress, existing methods seem to have a strong bias towards low- or high-order interactions, or require expertise feature engineering. In this paper, we show that it is possible to derive an end-to-end learning model that emphasizes both low- and high-order feature interactions. The proposed model, DeepFM, combines the power of factorization machines for recommendation and deep learning for feature learning in a new neural network architecture. Compared to the latest Wide & Deep model from Google, DeepFM has a shared input to its "wide" and "deep" parts, with no need of feature engineering besides raw features. Comprehensive experiments are conducted to demonstrate the effectiveness and efficiency of DeepFM over the existing models for CTR prediction, on both benchmark data and commercial data.

Predicting Drug-target Interactions via FM-DNN Learning

2020 ◽  
Vol 15 (1) ◽  
pp. 68-76 ◽  
Author(s):  
Jihong Wang ◽  
Hao Wang ◽  
Xiaodan Wang ◽  
Huiyou Chang
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Drug Target ◽  
Expert Knowledge ◽  
Drug Repositioning ◽  
Feature Learning ◽  
Second Order ◽  
High Order ◽  
Feature Engineering ◽  
Basic Features

Background: Identifying Drug-Target Interactions (DTIs) is a major challenge for current drug discovery and drug repositioning. Compared to traditional experimental approaches, in silico methods are fast and inexpensive. With the increase in open-access experimental data, numerous computational methods have been applied to predict DTIs. Methods: In this study, we propose an end-to-end learning model of Factorization Machine and Deep Neural Network (FM-DNN), which emphasizes both low-order (first or second order) and high-order (higher than second order) feature interactions without any feature engineering other than raw features. This approach combines the power of FM and DNN learning for feature learning in a new neural network architecture. Results: The experimental DTI basic features include drug characteristics (609), target characteristics (1819), plus drug ID, target ID, total 2430. We compare 8 models such as SVM, GBDT, WIDE-DEEP etc, the FM-DNN algorithm model obtains the best results of AUC(0.8866) and AUPR(0.8281). Conclusion: Feature engineering is a job that requires expert knowledge, it is often difficult and time-consuming to achieve good results. FM-DNN can auto learn a lower-order expression by FM and a high-order expression by DNN.FM-DNN model has outstanding advantages over other commonly used models.

scholarly journals A Deep Convolutional Neural Network Architecture for Cancer Diagnosis using Histopathological Images

2021 ◽  
Vol 10 (12) ◽  
pp. 7-12
Author(s):  
Karthika Gidijala ◽  
◽  
Mansa Devi Pappu ◽  
Manasa Vavilapalli ◽  
Mahesh Kothuru ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Neural Networks ◽  
Decision Making ◽  
Deep Learning ◽  
Cancer Diagnosis ◽  
Network Architecture ◽  
Neural Network Architecture ◽  
Proposed Model ◽  
Histopathological Images

Many different models of Convolution Neural Networks exist in the Deep Learning studies. The application and prudence of the algorithms is known only when they are implemented with strong datasets. The histopathological images of breast cancer are considered as to have much number of haphazard structures and textures. Dealing with such images is a challenging issue in deep learning. Working on wet labs and in coherence to the results many research have blogged with novel annotations in the research. In this paper, we are presenting a model that can work efficiently on the raw images with different resolutions and alleviating with the problems of the presence of the structures and textures. The proposed model achieves considerably good results useful for decision making in cancer diagnosis.

scholarly journals Modified Neural Architecture Search (NAS) Using the Chromosome Non-Disjunction

2021 ◽  
Vol 11 (18) ◽  
pp. 8628
Author(s):  
Kang-Moon Park ◽  
Donghoon Shin ◽  
Sung-Do Chi
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Simulation Studies ◽  
Neural Network Architecture ◽  
Conventional Model ◽  
Neural Architecture ◽  
The Neural Network ◽  
Proposed Model ◽  
Improve Cost

This paper proposes a deep neural network structuring methodology through a genetic algorithm (GA) using chromosome non-disjunction. The proposed model includes methods for generating and tuning the neural network architecture without the aid of human experts. Since the original neural architecture search (henceforth, NAS) was announced, NAS techniques, such as NASBot, NASGBO and CoDeepNEAT, have been widely adopted in order to improve cost- and/or time-effectiveness for human experts. In these models, evolutionary algorithms (EAs) are employed to effectively enhance the accuracy of the neural network architecture. In particular, CoDeepNEAT uses a constructive GA starting from minimal architecture. This will only work quickly if the solution architecture is small. On the other hand, the proposed methodology utilizes chromosome non-disjunction as a new genetic operation. Our approach differs from previous methodologies in that it includes a destructive approach as well as a constructive approach, and is similar to pruning methodologies, which realizes tuning of the previous neural network architecture. A case study applied to the sentence word ordering problem and AlexNet for CIFAR-10 illustrates the applicability of the proposed methodology. We show from the simulation studies that the accuracy of the model was improved by 0.7% compared to the conventional model without human expert.

scholarly journals Dynamic Malware Analysis with Feature Engineering and Feature Learning

2020 ◽  
Vol 34 (01) ◽  
pp. 1210-1217
Author(s):  
Zhaoqi Zhang ◽  
Panpan Qi ◽  
Wei Wang
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Deep Neural Network ◽  
Short Term Memory ◽  
Expert Knowledge ◽  
Malware Detection ◽  
Feature Representation ◽  
Feature Engineering ◽  
Malware Analysis ◽  
Neural Network Architecture

Dynamic malware analysis executes the program in an isolated environment and monitors its run-time behaviour (e.g. system API calls) for malware detection. This technique has been proven to be effective against various code obfuscation techniques and newly released (“zero-day”) malware. However, existing works typically only consider the API name while ignoring the arguments, or require complex feature engineering operations and expert knowledge to process the arguments. In this paper, we propose a novel and low-cost feature extraction approach, and an effective deep neural network architecture for accurate and fast malware detection. Specifically, the feature representation approach utilizes a feature hashing trick to encode the API call arguments associated with the API name. The deep neural network architecture applies multiple Gated-CNNs (convolutional neural networks) to transform the extracted features of each API call. The outputs are further processed through bidirectional LSTM (long-short term memory networks) to learn the sequential correlation among API calls. Experiments show that our solution outperforms baselines significantly on a large real dataset. Valuable insights about feature engineering and architecture design are derived from the ablation study.

scholarly journals Suggestion and invention of recipes using bi-directional LSTMs-based frameworks

2021 ◽  
Vol 3 (5) ◽  
Author(s):  
Sai Nikhil Rao Gona ◽  
Himamsu Marellapudi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Loss Function ◽  
Recurrent Neural Networks ◽  
Network Architecture ◽  
Loss Functions ◽  
Neural Network Architecture ◽  
Calorie Content ◽  
Proposed Model ◽  
Variational Autoencoder

AbstractChoosing which recipe to eat and which recipe to avoid isn’t that simple for anyone. It takes strenuous efforts and a lot of time for people to calculate the number of calories and P.H level of the dish. In this paper, we propose an ensemble neural network architecture that suggests recipes based on the taste of the person, P.H level and calorie content of the recipes. We also propose a bi-directional LSTMs-based variational autoencoder for generating new recipes. We have ensembled three bi-directional LSTM-based recurrent neural networks which can classify the recipes based on the taste of the person, P.H level of the recipe and calorie content of the recipe. The proposed model also predicts the taste ratings of the recipes for which we proposed a custom loss function which gave better results than the standard loss functions and the model also predicts the calorie content of the recipes. The bi-directional LSTMs-based variational autoencoder after being trained with the recipes which are fit for the person generates new recipes from the existing recipes. After training and testing the recurrent neural networks and the variational autoencoder, we have tested the model with 20 new recipes and got overwhelming results in the experimentation, the variational autoencoders generated a couple of new recipes, which are healthy to the specific person and will be liked by the specific person.

scholarly journals A Deep Graph Neural Network Architecture for Modelling Spatio-temporal Dynamics in resting-state functional MRI Data

2020 ◽  
Author(s):  
Tiago Azevedo ◽  
Alexander Campbell ◽  
Rafael Romero-Garcia ◽  
Luca Passamonti ◽  
Richard A.I. Bethlehem ◽  
...  
Keyword(s):  
Neural Network ◽  
Resting State ◽  
Network Architecture ◽  
Temporal Dynamics ◽  
Feature Learning ◽  
Blood Oxygen Level Dependent ◽  
Fmri Data ◽  
Neural Network Architecture ◽  
Pairwise Correlation ◽  
Spatio Temporal

AbstractResting-state functional magnetic resonance imaging (rs-fMRI) has been successfully employed to understand the organisation of the human brain. For rs-fMRI analysis, the brain is typically parcellated into regions of interest (ROIs) and modelled as a graph where each ROI is a node and pairwise correlation between ROI blood-oxygen-level-dependent (BOLD) time series are edges. Recently, graph neural networks (GNNs) have seen a surge in popularity due to their successes in modelling unstructured relational data. The latest developments with GNNs, however, have not yet been fully exploited for the analysis of rs-fMRI data, particularly with regards to its spatio-temporal dynamics. Herein we present a novel deep neural network architecture, combining both GNNs and temporal convolutional networks (TCNs), which is able to learn from the spatial and temporal components of rs-fMRI data in an end-to-end fashion. In particular, this corresponds to intra-feature learning (i.e., learning temporal dynamics with TCNs) as well as inter-feature learning (i.e., leveraging spatial interactions between ROIs with GNNs). We evaluate our model with an ablation study using 35,159 samples from the UK Biobank rs-fMRI database. We also demonstrate explainability features of our architecture which map to realistic neurobiological insights. We hope our model could lay the groundwork for future deep learning architectures focused on leveraging the inherently and inextricably spatio-temporal nature of rs-fMRI data.

A NEURAL NETWORK MODEL FOR MINIMUM SPANNING CIRCLE: ITS CONVERGENCE, ARCHITECTURE DESIGN AND APPLICATIONS

2002 ◽  
Vol 16 (07) ◽  
pp. 797-815
Author(s):  
AMITAVA DATTA ◽  
S. K. PARUI
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Network Architecture ◽  
Parallel Implementation ◽  
Higher Dimensions ◽  
Neural Network Architecture ◽  
Rigorous Analysis ◽  
Proposed Model ◽  
Finite Set

A self-organizing neural network model that computes the smallest circle (also called minimum spanning circle) enclosing a finite set of given points was proposed by Datta.3 In the article,3 the algorithm is stated and it is demonstrated by simulation that the center of the smallest circle can be achieved with a given level of accuracy. No rigorous proof was given in support of the simulation results. In this paper, we make a rigorous analysis of the model and mathematically prove that the model converges to the desired center of the minimum spanning circle. A suitable neural network architecture is also designed for parallel implementation of the proposed model. Time complexity of the algorithm is worked out under the proposed architecture. Extension of the proposed model to higher dimensions is discussed and demonstrated with some applications.

SCORING MODELING BASED ON NEURAL NETWORKS FOR DETERMINING A BANK BORROWER'S RATING

2020 ◽  
Vol 2020 (10) ◽  
pp. 54-62
Author(s):  
Oleksii VASYLIEV ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Statistical Data ◽  
Activation Function ◽  
Decision Making Process ◽  
Neural Network Architecture ◽  
Acceptable Accuracy ◽  
The Neural Network ◽  
Sigmoid Activation Function

The problem of applying neural networks to calculate ratings used in banking in the decision-making process on granting or not granting loans to borrowers is considered. The task is to determine the rating function of the borrower based on a set of statistical data on the effectiveness of loans provided by the bank. When constructing a regression model to calculate the rating function, it is necessary to know its general form. If so, the task is to calculate the parameters that are included in the expression for the rating function. In contrast to this approach, in the case of using neural networks, there is no need to specify the general form for the rating function. Instead, certain neural network architecture is chosen and parameters are calculated for it on the basis of statistical data. Importantly, the same neural network architecture can be used to process different sets of statistical data. The disadvantages of using neural networks include the need to calculate a large number of parameters. There is also no universal algorithm that would determine the optimal neural network architecture. As an example of the use of neural networks to determine the borrower's rating, a model system is considered, in which the borrower's rating is determined by a known non-analytical rating function. A neural network with two inner layers, which contain, respectively, three and two neurons and have a sigmoid activation function, is used for modeling. It is shown that the use of the neural network allows restoring the borrower's rating function with quite acceptable accuracy.

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