Click-through rate prediction is an essential task in industrial applications, such as online advertising. Recently deep learning based models have been proposed, which follow a similar Embedding&MLP paradigm. In these methods large scale sparse input features are first mapped into low dimensional embedding vectors, and then transformed fixed-length vectors group-wise manner, finally concatenated together to fed multilayer perceptron (MLP) learn the nonlinear relations among features. this...

Click-through rate (CTR) prediction, whose goal is to estimate the probability of a user clicking on item, has become one core tasks in advertising system. For CTR prediction model, it necessary capture latent interest behind behavior data. Besides, considering changing external environment and internal cognition, evolves over time dynamically. There are several methods for modeling, while most them regard representation as directly, lack specially modeling concrete behavior. Moreover,...

Rich user behavior data has been proven to be of great value for click-through rate prediction tasks, especially in industrial applications such as recommender systems and online advertising. Both industry academy have paid much attention this topic propose different approaches modeling with long sequential data. Among them, memory network based model MIMN proposed by Alibaba, achieves SOTA the co-design both learning algorithm serving system. is first solution that can length scaling up...

Models applied on real time response tasks, like click-through rate (CTR) prediction model, require high accuracy and rigorous time. Therefore, top-performing deep models of depth complexity are not well suited for these applications with the limitations inference In order to get neural networks better performance given limitations, we propose a universal framework that exploits booster net help train lightweight prediction. We dub whole process rocket launching, where is used guide learning...

Model reuse attempts to construct a model by utilizing existing available models, mostly trained for other tasks, rather than building from scratch. It is helpful reduce the time cost, data amount, and expertise required. Deep learning has achieved great success in various tasks involving images, voices videos. There are several studies have sense of reuse, trying pre-trained deep networks architectures or features train new model. They, however, neglect fact that there many fixed models...

Click-through rate prediction is an essential task in industrial applications, such as online advertising. Recently deep learning based models have been proposed, which follow a similar Embedding\&MLP paradigm. In these methods large scale sparse input features are first mapped into low dimensional embedding vectors, and then transformed fixed-length vectors group-wise manner, finally concatenated together to fed multilayer perceptron (MLP) learn the nonlinear relations among features....

Models applied on real time response task, like click-through rate (CTR) prediction model, require high accuracy and rigorous time. Therefore, top-performing deep models of depth complexity are not well suited for these applications with the limitations inference In order to further improve neural networks' performance given computational limitations, we propose an approach that exploits a cumbersome net help train lightweight prediction. We dub whole process rocket launching, where booster...

Abstract:

One of the difficulties conversion rate (CVR) prediction is that conversions can delay and take place long after clicks. The delayed feedback poses a challenge: fresh data are beneficial to continuous training but may not have complete label information at time they ingested into pipeline. To balance model freshness certainty, previous methods set short waiting window or even do wait for signal. If happens outside window, this sample will be duplicated pipeline with positive label. However,...

Click-through rate~(CTR) prediction, whose goal is to estimate the probability of user clicks, has become one core tasks in advertising systems. For CTR prediction model, it necessary capture latent interest behind behavior data. Besides, considering changing external environment and internal cognition, evolves over time dynamically. There are several methods for modeling, while most them regard representation as directly, lack specially modeling concrete behavior. Moreover, few work...

Indoor mobile robot navigation based on the vision system is a hot research field in recent years. Structured light method kind of three dimensional measuring technology which widely used system. In this paper, obstacle detection indoor environment designed, linear structured projected front assists camera to gather information. With image processing algorithm, we can detect change per frame, compare with standard reach conclusion that whether there an and then calculate characteristic value...

The widely known classifier chains method for multi-label classification, which is based on the binary relevance (BR) method, overcomes disadvantages of BR and achieves higher predictive performance, but still retains important advantages BR, most importantly low time complexity. Nevertheless, despite its advantages, it clear that a randomly arranged chain can be poorly ordered. We overcome this issue with different strategy: Several times K-means algorithms are employed to get correlations...

Virtualization can provide significant benefits in data centers by enabling virtual machine migration to eliminate hotspots. In order improve the overall resource utilization, we propose a new live strategy. strategy, use load characteristics implement hotspots detection, selection of and destination host according some multi-threshold patterns. Experimental results show that strategy effectively support migration.

Efficient Reinforcement Learning usually takes advantage of demonstration or good exploration strategy. By applying posterior sampling in model-free RL under the hypothesis GP, we propose Gaussian Process Posterior Sampling Learning(GPPSTD) algorithm continuous state space, giving theoretical justifications and empirical results. We also provide results that various could lower expected uncertainty benefit exploration. In this way, combined process together to achieve a more efficient...

Face liveness detection is designed to prevent the face recognition system from using attacking image as user's real image. At present, although there are many methods, most of methods have poor generalization ability in practical application. Because these based on datasets, and data cannot well simulate distribution environment. In order improve model, this paper, we build model deep neural network dataset collected by ourselves. The experimental results show that our has achieved...

The mobile robot vision system is composed of linear structured light generator, CMOS camera and image processor based on DSP TMS320DM642. We use digital signal to design the processing because its high-speed performance low power consumption. In this paper, a new coordinates establishing method proposed simplify calibration. With algorithm, we can detect change per frame, compare with standard reach conclusion that whether there an obstacle calculate characteristic value obstacle.

Rich user behavior data has been proven to be of great value for click-through rate prediction tasks, especially in industrial applications such as recommender systems and online advertising. Both industry academy have paid much attention this topic propose different approaches modeling with long sequential data. Among them, memory network based model MIMN proposed by Alibaba, achieves SOTA the co-design both learning algorithm serving system. is first solution that can length scaling up...

In this paper, a new type of multineural networks filter (MNNF) is presented that trained for restoration and enhancement the medical CR images. image, noise has been categorized as quantum mottle, which related to incident X-ray exposure artificial noise, caused by grid, etc. MNNF consists several neural network filters (NNFs). A novel analysis method proposed make characteristics clearly. method, judgement system decide NNF will be executed through estimation intensity calculated Maximum...

Determination of binding sites between proteins is widely applied in many fields, such as drug design and the structural functional analysis. The protein-protein can be formed by two subunits a complex. Understanding energetics mechanisms complexes remains one essential problems site prediction. We develop system, P-Binder, for identifying based on shape complementarity, side-chain conformations interacting amino acid information. P-Binder utilizes an enumeration method to generate all...

A deep learning approach is used in this study to provide insight into aerobics movement recognition, and the model for recognition. The complexity significantly reduced, while multi-scale features of target at fine-grained level are extracted, improving characterization target, by embedding lightweight convolution modules 3D convolutional residual networks increase local perceptual field range each layer network. Finally, using channel attention mechanism, key extracted from features. To...