FinalMLP: An Enhanced Two-Stream MLP Model for CTR Prediction

Click-through rate (CTR) prediction is one of the fundamental tasks for online advertising and recommendation. While multi-layer perceptron (MLP) serves as a core component in many deep CTR prediction models, it has been widely recognized that applying a vanilla MLP network alone is inefficient in learning multiplicative feature interactions. As such, many two-stream interaction models (e.g., DeepFM and DCN) have been proposed by integrating an MLP network with another dedicated network for enhanced CTR prediction. As the MLP stream learns feature interactions implicitly, existing research focuses mainly on enhancing explicit feature interactions in the complementary stream. In contrast, our empirical study shows that a well-tuned two-stream MLP model that simply combines two MLPs can even achieve surprisingly good performance, which has never been reported before by existing work. Based on this observation, we further propose feature gating and interaction aggregation layers that can be easily plugged to make an enhanced two-stream MLP model, FinalMLP. In this way, it not only enables differentiated feature inputs but also effectively fuses stream-level interactions across two streams. Our evaluation results on four open benchmark datasets as well as an online A/B test in our industrial system show that FinalMLP achieves better performance than many sophisticated two-stream CTR models. Our source code will be available at MindSpore/models.Comment: Accepted by AAAI 2023. Code available at https://xpai.github.io/FinalML

Accurate 3D maps from depth images and motion sensors via nonlinear Kalman filtering

This paper investigates the use of depth images as localisation sensors for 3D map building. The localisation information is derived from the 3D data thanks to the ICP (Iterative Closest Point) algorithm. The covariance of the ICP, and thus of the localization error, is analysed, and described by a Fisher Information Matrix. It is advocated this error can be much reduced if the data is fused with measurements from other motion sensors, or even with prior knowledge on the motion. The data fusion is performed by a recently introduced specific extended Kalman filter, the so-called Invariant EKF, and is directly based on the estimated covariance of the ICP. The resulting filter is very natural, and is proved to possess strong properties. Experiments with a Kinect sensor and a three-axis gyroscope prove clear improvement in the accuracy of the localization, and thus in the accuracy of the built 3D map.Comment: Submitted to IROS 2012. 8 page

Kaluza-Klein Gluons as a Diagnostic of Warped Models

We study the properties of $g^{1}$, the first excited state of the gluon in representative variants of the Randall Sundrum model with the Standard Model fields in the bulk. We find that measurements of the coupling to light quarks (from the inclusive cross-section for $pp\to g^{1} \to t\bar t$), the coupling to bottom quarks (from the rate of $pp\to g^{1} b$), as well as the overall width, can provide powerful discriminants between the models. In models with large brane kinetic terms, the $g^1$ resonance can even potentially be discovered decaying into dijets against the large QCD background. We also derive bounds based on existing Tevatron searches for resonant $t \bar{t}$ production and find that they require $M_{g^{1}} \gtrsim 950$ GeV. In addition we explore the pattern of interference between the $g^1$ signal and the non-resonant SM background, defining an asymmetry parameter for the invariant mass distribution. The interference probes the relative signs of the couplings of the $g^{1}$ to light quark pairs and to $t\bar t$, and thus provides an indication that the top is localized on the other side of the extra dimension from the light quarks, as is typical in the RS framework.Comment: 25 pages, 10 figure

Effects of unparticle on top spin correlation at the Large Hadron Collider

We study effects of the scale invariant hidden sector, unparticle, proposed by Georgi, on top spin correlation at the Large Hadron Collider. Assuming no flavor changing interaction between the unparticles and the Standard Model particles, there arises the top-antitop quark pair production process through virtual unparticle exchanges in the s-channel in addition to the Standard Model processes. In particular, we consider contributions of scalar and vector unparticles and find that these make sizable deviations of the top spin correlation from the Standard Model one.Comment: 29 pages, 1 table, 12 figures, 2 figures added, typos in captions corrected, version accepted for publication in PR