Click-through rate (CTR) prediction plays a critical role in recommender systems, in which the task is to forecast the probability of the user clicking on recommended items. Many models have been proposed in this field, such as logistic regression, factorization machine based models and deep learning based models. However, many current works calculate the feature interactions in a simple way such as inner product. They care less about the different importance and computational requirements of different feature interactions. The general idea is that some complex feature interactions might require more computations to produce a final result, while some simple or unimportant feature interactions might require less. In this paper, we propose adaptive depth attention (ADA) model, a new model that automatically learns the high-order feature interactions of raw data. The core of ADA is a multi-head self-attention neural network that learns feature interaction and a network depth control module that controls the network depth required for interaction of different feature fields. We conduct extensive experiments on two realworld datasets and results demonstrate the superior predictive performance of ADA against the state-of-the-art methods.

This paper constructs a set partition coding system (SPACS) to combine the advantages of different types of set partition coding algorithms. General tree (GT) is an important conception introduced in this paper, which can represent tree set and square set simultaneously. With the help of GT, SPIHT is generalized to construct degree- k SPIHT based on the analysis of two kinds of set partition operations. Using the same coding mechanism, SPACS (k,p) is constructed, aided with virtual subbands that are generated by recursive division on the LL band. SPACS belongs to tree-set partition coding algorithms if k and p take smaller values. In particular, SPACS(2,1) is the classical SPIHT. SPACS tends toward a block-set partition coding algorithm as k,p increases. Location bit, amplitude bit, and unnecessary bit are presented, which can be used to analyze the coding efficiency of SPACS. We compress 256 images with 512×512 using SPACS. The numerical results show SPACS achieves some improvements in coding efficiency over SPIHT, especially at very low bitrate. On average, to code every image, SPACS(3,1) (at an average of 3.93 bpp) needs 7792 more location bits but saves 10 218 unnecessary bits, compared with SPIHT (3.94 bpp).

A new probabilistic model called ATR-Seg for automated target tracking, recognition and segmentation is proposed that incorporates a shape constrained level set with a shape generative model along with motion model. The shape model involves a view-independent identity manifold and infinite identity-dependent view manifolds for multi-view and multi-target shape modeling. ATR-Seg applies the motion model to predict the state of the target (i.e., 3D position, pose and identity), and then uses a shape-aware level set energy functional to evaluate the tracking and segmentation results. A particle filtering-based method is used for sequential inference, where the level set energy functional is treated as the likelihood function. Experimental results obtained against the SENSIAC ATR database demonstrate the advantages of the proposed method compared with the two recent techniques that require target pre-segmentation via background subtraction.

A new joint view-identity manifold (JVIM) is proposed for multiview shape modeling that is applied to automated target tracking and recognition (ATR). This work improves our recent work where the view and identity manifolds are assumed to be independent for multi-view multi-target modeling. A local linear Gaussian process latent variable model (LL-GPLVM) is used to learn a probabilistic JVIM which can capture both inter-class and intra-class variability of 2D target shapes under arbitrary view point jointly in one coexisted latent space. A particle filter-based ATR algorithm is developed to simultaneously infer the view and identity parameters along JVIM so that target tracking and recognition can be achieved jointly in a seamlessly fashion. The experimental results using SENSIAC ATR database demonstrate the advantages of our method both qualitatively and quantitatively compared with existing methods using template matching or separate view and identity manifolds.