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

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

Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction

Click-Through Rate prediction is an important task in recommender systems, which aims to estimate the probability of a user to click on a given item. Recently, many deep models have been proposed to learn low-order and high-order feature interactions from original features. However, since useful interactions are always sparse, it is difficult for DNN to learn them effectively under a large number of parameters. In real scenarios, artificial features are able to improve the performance of deep models (such as Wide & Deep Learning), but feature engineering is expensive and requires domain knowledge, making it impractical in different scenarios. Therefore, it is necessary to augment feature space automatically. In this paper, We propose a novel Feature Generation by Convolutional Neural Network (FGCNN) model with two components: Feature Generation and Deep Classifier. Feature Generation leverages the strength of CNN to generate local patterns and recombine them to generate new features. Deep Classifier adopts the structure of IPNN to learn interactions from the augmented feature space. Experimental results on three large-scale datasets show that FGCNN significantly outperforms nine state-of-the-art models. Moreover, when applying some state-of-the-art models as Deep Classifier, better performance is always achieved, showing the great compatibility of our FGCNN model. This work explores a novel direction for CTR predictions: it is quite useful to reduce the learning difficulties of DNN by automatically identifying important features

Semi-Supervised Domain Generalization for Cardiac Magnetic Resonance Image Segmentation with High Quality Pseudo Labels

Developing a deep learning method for medical segmentation tasks heavily relies on a large amount of labeled data. However, the annotations require professional knowledge and are limited in number. Recently, semi-supervised learning has demonstrated great potential in medical segmentation tasks. Most existing methods related to cardiac magnetic resonance images only focus on regular images with similar domains and high image quality. A semi-supervised domain generalization method was developed in [2], which enhances the quality of pseudo labels on varied datasets. In this paper, we follow the strategy in [2] and present a domain generalization method for semi-supervised medical segmentation. Our main goal is to improve the quality of pseudo labels under extreme MRI Analysis with various domains. We perform Fourier transformation on input images to learn low-level statistics and cross-domain information. Then we feed the augmented images as input to the double cross pseudo supervision networks to calculate the variance among pseudo labels. We evaluate our method on the CMRxMotion dataset [1]. With only partially labeled data and without domain labels, our approach consistently generates accurate segmentation results of cardiac magnetic resonance images with different respiratory motions. Code is available at: https://github.com/MAWanqin2002/STACOM2022MaComment: Accepted by International Workshop on Statistical Atlases and Computational Models of the Heart (STACOM2022) of MICCAI202

Corpus of public writing and its interest for the history of spanish: votive paintings of the province of Guadalajara

El objetivo del presente trabajo es dar a conocer un especial corpus de escrituras expuestas populares ubicadas en los centros de devoción de la provincia de Guadalajara; en concreto un corpus de exvotos pintados datados entre los siglos XVII al XX. Los cuadros representan el suceso que dio origen al milagro o favor recibido con su cartela; pero en nuestro corpus solo se recogen aquellos exvotos que van acompañados de texto explicativo, en el que se da testimonio del favor recibido con datos precisos, como nombre, causa, fecha, lugar, etc. En el corpus que presentamos se incluye la reproducción del exvoto, la transcripción paleográfica del documento y su presentación crítica. El estudio detenido de estos tres elementos permitirá extraer interesantes conclusiones para la historia del español en aspectos relacionados con las grafías, la ortografía, la puntuación, las estructuras sintácticas y fórmulas empleadas, el léxico, etc.; se recogen en este artículo algunas muestras de ello.El objetivo del presente trabajo es dar a conocer un especial corpus de escrituras expuestas populares ubicadas en los centros de devoción de la provincia de Guadalajara; en concreto un corpus de exvotos pintados datados entre los siglos XVII al XX. Los cuadros representan el suceso que dio origen al milagro o favor recibido con su cartela; pero en nuestro corpus solo se recogen aquellos exvotos que van acompañados de texto explicativo, en el que se da testimonio del favor recibido con datos precisos, como nombre, causa, fecha, lugar, etc. En el corpus que presentamos se incluye la reproducción del exvoto, la transcripción paleográfica del documento y su presentación crítica. El estudio detenido de estos tres elementos permitirá extraer interesantes conclusiones para la historia del español en aspectos relacionados con las grafías, la ortografía, la puntuación, las estructuras sintácticas y fórmulas empleadas, el léxico, etc.; se recogen en este artículo algunas muestras de ello.El objetivo del presente trabajo es dar a conocer un especial corpus de escrituras expuestas populares ubicadas en los centros de devoción de la provincia de Guadalajara; en concreto un corpus de exvotos pintados datados entre los siglos XVII al XX. Los cuadros representan el suceso que dio origen al milagro o favor recibido con su cartela; pero en nuestro corpus solo se recogen aquellos exvotos que van acompañados de texto explicativo, en el que se da testimonio del favor recibido con datos precisos, como nombre, causa, fecha, lugar, etc. En el corpus que presentamos se incluye la reproducción del exvoto, la transcripción paleográfica del documento y su presentación crítica. El estudio detenido de estos tres elementos permitirá extraer interesantes conclusiones para la historia del español en aspectos relacionados con las grafías, la ortografía, la puntuación, las estructuras sintácticas y fórmulas empleadas, el léxico, etc.; se recogen en este artículo algunas muestras de ello.The objective of this work is to present a special corpus of popular public writing in the centres of devotion of the province of Guadalajara; in particular a corpus of painted votive offering dating from the 17th to the 20th centuries. The pictures represent the event that gave rise to the miracle or favour received with his card; but in our corpus are only included those votive offerings that are accompanied by explanatory text, which testifies to the favour received with precise information, as name, cause, date, place, etc.The Corpus includes the reproduction of the votive offering, the paleographic transcription of the document and its critical presentation. The careful study of these three elements will allow us to extract interesting conclusions for the history of Spanish in aspects related to spelling, punctuation, syntactic structures and formulas used, lexicon, etc.; some samples that are collected in this article