arXiv research

Proposes FEFM and DeepFEFM for CTR prediction, outperforming state-of-the-art models.

NeuralFactors uses deep learning to improve factor analysis in equity modeling.

This paper introduces our solution to the 2018 Duolingo Shared Task on Second Language Acquisition Modeling (SLAM). We used deep factorization machines, a wide and deep learning model of pairwise relationships between users, items, skills, and other entities considered. Our solution (AUC 0.815) hopefully managed to bea…

Latent factor models have been used widely in collaborative filtering based recommender systems. In recent years, deep learning has been successful in solving a wide variety of machine learning problems. Motivated by the success of deep learning, we propose a deeper version of latent factor model. Experiments on benchm…

Advertising and feed ranking are essential to many Internet companies such as Facebook and Sina Weibo. Among many real-world advertising and feed ranking systems, click through rate (CTR) prediction plays a central role. There are many proposed models in this field such as logistic regression, tree based models, factor…

Study proposes a new method for deep portfolio optimization using residual factors.

Deep weight factorization improves neural network training through smooth optimization of sparse penalties.

We propose to represent a return model and risk model in a unified manner with deep learning, which is a representative model that can express a nonlinear relationship. Although deep learning performs quite well, it has significant disadvantages such as a lack of transparency and limitations to the interpretability of …

An efficient way to learn deep density models that have many layers of latent variables is to learn one layer at a time using a model that has only one layer of latent variables. After learning each layer, samples from the posterior distributions for that layer are used as training data for learning the next layer. Thi…

Recommendation systems and computing advertisements have gradually entered the field of academic research from the field of commercial applications. Click-through rate prediction is one of the core research issues because the prediction accuracy affects the user experience and the revenue of merchants and platforms. Fe…

Matrix completion is one of the key problems in signal processing and machine learning. In recent years, deep-learning-based models have achieved state-of-the-art results in matrix completion. Nevertheless, they suffer from two drawbacks: (i) they can not be extended easily to rows or columns unseen during training; an…

Newsroom in online ecosystem is difficult to untangle. With prevalence of social media, interactions between journalists and individuals become visible, but lack of understanding to inner processing of information feedback loop in public sphere leave most journalists baffled. Can we provide an organized view to charact…

Matrix factorization techniques have been widely used as a method for collaborative filtering for recommender systems. In recent times, different variants of deep learning algorithms have been explored in this setting to improve the task of making a personalized recommendation with user-item interaction data. The idea …

A linear multi-factor model is one of the most important tools in equity portfolio management. The linear multi-factor models are widely used because they can be easily interpreted. However, financial markets are not linear and their accuracy is limited. Recently, deep learning methods were proposed to predict stock re…

A new recommendation method combining deep learning and graph analysis improves performance.

The paper explores indeterminacy in latent factor projections and its implications for data representation.

The study compares different models for predicting factor premiums and finds neural networks perform better but have unstable weights.

This paper investigates the impact of normalization on deep neural networks for click-through rate prediction.

RVRAE combines deep learning and dynamic factor models for better stock returns prediction.

Dropout controls model capacity in deep learning and matrix completion.

ELM speeds up financial machine learning tasks.

The proliferation of healthcare data has brought the opportunities of applying data-driven approaches, such as machine learning methods, to assist diagnosis. Recently, many deep learning methods have been shown with impressive successes in predicting disease status with raw input data. However, the "black-box" nature o…

DeCom predicts post-COVID RSV timing and intensity with NPI consideration.

Survey on concept factorization methods for better feature learning.

This research predicts cryptocurrency price volatility using deep learning models.

Develops a deep multi-factor model for factor investing with clear financial insights.

Automated fatigue assessment using ECG and actigraphy sensors.

Deep tensor factorization benefits from implicit regularization with polynomial growth.

Stock return predictability is an important research theme as it reflects our economic and social organization, and significant efforts are made to explain the dynamism therein. Statistics of strong explanative power, called "factor" have been proposed to summarize the essence of predictive stock returns. Although mach…

Cost-effective models detect depression from speech.

Deep model learns complex latent codes without assuming factor structure.

Neural networks outperform single-hour models in day-ahead electricity price forecasting.

DPLS improves asset pricing by capturing non-linear risk factor structures.

Machine learning factors outperform traditional portfolio optimization methods.

The paper studies the loss landscape of regularized deep matrix factorization, revealing unique and sharp minimizers.

DF2M uses deep neural networks within a factor model for high-dimensional functional time series forecasting.

Scaling ResNets requires careful consideration of the layer depth and output scaling factors.

Paper finds exact Hessian sharpness in deep matrix factorization.

Efficiently compress overparameterized deep models by focusing on low-dimensional learning dynamics.

Proposes an end-to-end deep learning framework for active investing.

Deep learning improves Bayes factor computation for likelihood-free models.

SPIDER uses deep neural networks for streaming tensor factorization.

In this paper, we investigate the unsupervised deep representation learning issue and technically propose a novel framework called Deep Self-representative Concept Factorization Network (DSCF-Net), for clustering deep features. To improve the representation and clustering abilities, DSCF-Net explicitly considers discov…

Stock price prediction has been an important research theme both academically and practically. Various methods to predict stock prices have been studied until now. The feature that explains the stock price by a cross-section analysis is called a "factor" in the field of finance. Many empirical studies in finance have i…

DS2CF-Net learns hierarchical representations with deep coupled factorization and enriched prior.

Efficiently learns deep factor graphs using Gaussian belief propagation.

DMSTF models spatio-temporal data with deep Markov priors.

Deep neural networks decompose SDF into linear and nonlinear components.