Online classifier adaptation for cost-sensitive learning

In this paper, we propose the problem of online cost-sensitive clas- sifier adaptation and the first algorithm to solve it. We assume we have a base classifier for a cost-sensitive classification problem, but it is trained with respect to a cost setting different to the desired one. Moreover, we also have some training data samples streaming to the algorithm one by one. The prob- lem is to adapt the given base classifier to the desired cost setting using the steaming training samples online. To solve this problem, we propose to learn a new classifier by adding an adaptation function to the base classifier, and update the adaptation function parameter according to the streaming data samples. Given a input data sample and the cost of misclassifying it, we up- date the adaptation function parameter by minimizing cost weighted hinge loss and respecting previous learned parameter simultaneously. The proposed algorithm is compared to both online and off-line cost-sensitive algorithms on two cost-sensitive classification problems, and the experiments show that it not only outperforms them one classification performances, but also requires significantly less running time

FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction

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, factorization machine based models and deep learning based CTR models. However, many current works calculate the feature interactions in a simple way such as Hadamard product and inner product and they care less about the importance of features. In this paper, a new model named FiBiNET as an abbreviation for Feature Importance and Bilinear feature Interaction NETwork is proposed to dynamically learn the feature importance and fine-grained feature interactions. On the one hand, the FiBiNET can dynamically learn the importance of features via the Squeeze-Excitation network (SENET) mechanism; on the other hand, it is able to effectively learn the feature interactions via bilinear function. We conduct extensive experiments on two real-world datasets and show that our shallow model outperforms other shallow models such as factorization machine(FM) and field-aware factorization machine(FFM). In order to improve performance further, we combine a classical deep neural network(DNN) component with the shallow model to be a deep model. The deep FiBiNET consistently outperforms the other state-of-the-art deep models such as DeepFM and extreme deep factorization machine(XdeepFM).Comment: 8 pages,5 figure

Modified Cramer-Rao bound for M-FSK signal parameter estimation in Cauchy and Gaussian noise

The Cramer-Rao bound (CRB) provides an efficient standard for evaluating the quality of standard parameter estimators. In this paper, a modified Cramer-Rao bounds (MCRB) for modulation parameter estimations of frequency-shift-keying (FSK) signals is proposed under the condition of the Gaussian and non-Gaussian additive interference. We extend the MCRB to the estimation of a vector of non-random parameters in the presence of nuisance parameters. Moreover, the MCRB is applied to the joint estimation of phase offset, frequency offsets, frequency deviation, and symbol period of FSK signal with two important special cases of alpha stable distributions, namely, the Cauchy and the Gaussian. The extensive simulation studies are conducted to contrast the MCRB for the modulation parameter vector in different noise environments

Magnetic domain wall engineering in a nanoscale permalloy junction

Nanoscale magnetic junctions provide a useful approach to act as building blocks for magnetoresistive random access memories (MRAM), where one of the key issues is to control the magnetic domain configuration. Here, we study the domain structure and the magnetic switching in the Permalloy (Fe20Ni80) nanoscale magnetic junctions with different thicknesses by using micromagnetic simulations. It is found that both the 90-° and 45-° domain walls can be formed between the junctions and the wire arms depending on the thickness of the device. The magnetic switching fields show distinct thickness dependencies with a broad peak varying from 7 nm to 22 nm depending on the junction sizes, and the large magnetic switching fields favor the stability of the MRAM operation

MemoNet:Memorizing Representations of All Cross Features Efficiently via Multi-Hash Codebook Network for CTR Prediction

New findings in natural language processing(NLP) demonstrate that the strong memorization capability contributes a lot to the success of large language models.This inspires us to explicitly bring an independent memory mechanism into CTR ranking model to learn and memorize all cross features'representations. In this paper,we propose multi-Hash Codebook NETwork(HCNet) as the memory mechanism for efficiently learning and memorizing representations of all cross features in CTR tasks.HCNet uses multi-hash codebook as the main memory place and the whole memory procedure consists of three phases: multi-hash addressing,memory restoring and feature shrinking.HCNet can be regarded as a general module and can be incorporated into any current deep CTR model.We also propose a new CTR model named MemoNet which combines HCNet with a DNN backbone.Extensive experimental results on three public datasets show that MemoNet reaches superior performance over state-of-the-art approaches and validate the effectiveness of HCNet as a strong memory module.Besides, MemoNet shows the prominent feature of big models in NLP,which means we can enlarge the size of codebook in HCNet to sustainably obtain performance gains.Our work demonstrates the importance and feasibility of learning and memorizing representations of all cross features ,which sheds light on a new promising research direction

Jacobi pseudo-spectral Galerkin method for second kind Volterra integro-differential equations with a weakly singular kernel

The Jacobi pseudo-spectral Galerkin method for the Volterra integro-differential equations of the second kind with a weakly singular kernel is proposed in this paper. We provide a rigorous error analysis for the proposed method, which indicates that the numerical errors (in the Lωα,β2-norm and the L∞-norm) will decay exponentially provided that the source function is sufficiently smooth. Numerical examples are given to illustrate the theoretical results

FiBiNet++: Reducing Model Size by Low Rank Feature Interaction Layer for CTR Prediction

Click-Through Rate (CTR) estimation has become one of the most fundamental tasks in many real-world applications and various deep models have been proposed. Some research has proved that FiBiNet is one of the best performance models and outperforms all other models on Avazu dataset. However, the large model size of FiBiNet hinders its wider application. In this paper, we propose a novel FiBiNet++ model to redesign FiBiNet's model structure, which greatly reduces model size while further improves its performance. One of the primary techniques involves our proposed "Low Rank Layer" focused on feature interaction, which serves as a crucial driver of achieving a superior compression ratio for models. Extensive experiments on three public datasets show that FiBiNet++ effectively reduces non-embedding model parameters of FiBiNet by 12x to 16x on three datasets. On the other hand, FiBiNet++ leads to significant performance improvements compared to state-of-the-art CTR methods, including FiBiNet