OpenRaiser/Intern-Atlas huggingface dataset:
Automatic differentiation facilitates of-integration into steering-angle-based road vehicle tracking
Authors: Holger Leuck, Hans-Hellmut Nagel
Venue: Conference CVPR 1999
Year: 1999

The interpretation of road traffic scenes recorded by a stationary video camera requires a sufficiently large field of view in order to capture non-trivial maneuvers. Vehicle images will thus be small. Their reliable tracking needs to combine edge-based approaches for precision with area-oriented approaches, for example based on a match between estimated and predicted Optic Flow fields, for stability. The approach reported here estimates the steering angle of vehicles to be tracked and compares this as well as a velocity estimate with actual measurements performed in a suitably equipped test vehicle recorded while crossing a road intersection. In addition, tracking results for numerous other vehicles recorded in the same scene have been assessed interactively and are reported. Computation of the Jacobi matrix for the Kalman-Filter is facilitated by a "dual number" representation for partial derivatives.
Signatory: differentiable computations of the signature and logsignature transforms, on both CPU and GPU
Authors: Patrick Kidger, Terry J. Lyons
Venue: Conference ICLR 2021
Year: 2021

Signatory is a library for calculating and performing functionality related to the signature and logsignature transforms. The focus is on machine learning, and as such includes features such as CPU parallelism, GPU support, and backpropagation. To our knowledge it is the first GPU-capable library for these operations. Signatory implements new features not available in previous libraries, such as efficient precomputation strategies. Furthermore, several novel algorithmic improvements are introduced, producing substantial real-world speedups even on the CPU without parallelism. The library operates as a Python wrapper around C++, and is compatible with the PyTorch ecosystem. It may be installed directly via \texttt{pip}. Source code, documentation, examples, benchmarks and tests may be found at \texttt{\url{https://github.com/patrick-kidger/signatory}}. The license is Apache-2.0.
Scalable global alignment graph kernel using random features: From node embedding to graph embedding
Authors: Lingfei Wu, Ian En-Hsu Yen, Zhen Zhang, Kun Xu, Liang Zhao, Xi Peng, Yinglong Xia, Charu C. Aggarwal
Venue: Conference KDD 2019
Year: 2019

Graph kernels are widely used for measuring the similarity between graphs. Many existing graph kernels, which focus on local patterns within graphs rather than their global properties, suffer from significant structure information loss when representing graphs. Some recent global graph kernels, which utilizes the alignment of geometric node embeddings of graphs, yield state-of-the-art performance. However, these graph kernels are not necessarily positive-definite. More importantly, computing the graph kernel matrix will have at least quadratic {time} complexity in terms of the number and the size of the graphs. In this paper, we propose a new family of global alignment graph kernels, which take into account the global properties of graphs by using geometric node embeddings and an associated node transportation based on earth mover's distance. Compared to existing global kernels, the proposed kernel is positive-definite. Our graph kernel is obtained by defining a distribution over \emph{random graphs}, which can naturally yield random feature approximations. The random feature approximations lead to our graph embeddings, which is named as "random graph embeddings" (RGE). In particular, RGE is shown to achieve \emph{(quasi-)linear scalability} with respect to the number and the size of the graphs. The experimental results on nine benchmark datasets demonstrate that RGE outperforms or matches twelve state-of-the-art graph classification algorithms.
HySAD: A semi-supervised hybrid shilling attack detector for trustworthy product recommendation
Authors: Zhiang Wu, Junjie Wu, Jie Cao, Dacheng Tao
Venue: Conference KDD 2012
Year: 2012

Shilling attackers apply biased rating profiles to recommender systems for manipulating online product recommendations. Although many studies have been devoted to shilling attack detection, few of them can handle the hybrid shilling attacks that usually happen in practice, and the studies for real-life applications are rarely seen. Moreover, little attention has yet been paid to modeling both labeled and unlabeled user profiles, although there are often a few labeled but numerous unlabeled users available in practice. This paper presents a Hybrid Shilling Attack Detector, or HySAD for short, to tackle these problems. In particular, HySAD introduces MC-Relief to select effective detection metrics, and Semi-supervised Naive Bayes (SNB_lambda) to precisely separate Random-Filler model attackers and Average-Filler model attackers from normal users. Thorough experiments on MovieLens and Netflix datasets demonstrate the effectiveness of HySAD in detecting hybrid shilling attacks, and its robustness for various obfuscated strategies. A real-life case study on product reviews of Amazon.cn is also provided, which further demonstrates that HySAD can effectively improve the accuracy of a collaborative-filtering based recommender system, and provide interesting opportunities for in-depth analysis of attacker behaviors. These, in turn, justify the value of HySAD for real-world applications.
Detecting absurd conversations from intelligent assistant logs by exploiting user feedback utterances
Authors: Chikara Hashimoto, Manabu Sassano
Venue: Conference WWW 2018
Year: 2018

Intelligent assistants, such as Siri, are expected to converse comprehensibly with users. To facilitate improvement of their conversational ability, we have developed a method that detects absurd conversations recorded in intelligent assistant logs by identifying user feedback utterances that indicate users' favorable and unfavorable evaluations of intelligent assistant responses; e.g., "great!" is favorable, whereas "what are you talking about?" is unfavorable. Assuming that absurd/comprehensible conversations tend to be followed by unfavorable/favorable utterances, our method extracts some absurd/comprehensible conversations from the log to train a conversation classifier that sorts all the conversations recorded in the log as either absurd or not. The challenge is that user feedback utterances are often ambiguous; e.g., a user may give an unfavorable utterance (e.g., "don't be silly!") to a comprehensible conversation in which the intelligent assistant was attempting to make a joke. An utterance classifier is thus used to score the feedback utterances in accordance with how unambiguously they indicate absurdity. Experiments showed that our method significantly outperformed methods that lacked a conversation and/or utterance classifier, indicating the effectiveness of the two classifiers. Our method only requires user feedback utterances, which would be independent of domains. Experiments focused on chitchat, web search, and weather domains indicated that our method is likely domain-independent.
Alpha seeding for support vector machines
Authors: Dennis DeCoste, Kiri Wagstaff
Venue: Conference KDD 2000
Year: 2000

A key pra\nti\nal obsta\nle in applying support ve\ntor ma-hines to many large-s\nale data mining tasks is that SVM training time generally s\nales quadrati\nally (or worse) in the number of examples or support ve\ntors. This \nomplexity is further ompounded when a spe\ni\n SVM training is but one of many, su\nh as in Leave-One-Out-Cross-Validation (LOOCV) for determining optimal SVM parameters or as in wrapper-based feature sele\ntion. In this paper we explore new te\nhniques for redu\ning the amortized \nost of ea\nh su\nh SVM training, by seeding su\n\nessive SVM trainings with the results of previous similar trainings.
Online context-aware recommendation with time varying multi-armed bandit
Authors: Chunqiu Zeng, Qing Wang, Shekoofeh Mokhtari, Tao Li
Venue: Conference KDD 2016
Year: 2016

Contextual multi-armed bandit problems have gained increasing popularity and attention in recent years due to their capability of leveraging contextual information to deliver online personalized recommendation services (e.g., online advertising and news article selection). To predict the reward of each arm given a particular context, existing relevant research studies for contextual multi-armed bandit problems often assume the existence of a fixed yet unknown reward mapping function. However, this assumption rarely holds in practice, since real-world problems often involve underlying processes that are dynamically evolving over time.
Short-text representation using diffusion wavelets
Authors: Vidit Jain, Jay Mahadeokar
Venue: Conference WWW 2014
Year: 2014

Usual text document representations such as tf-idf do not work well in classification tasks for short-text documents and across diverse data domains. Optimizing different representations for different data domains is infeasible in a practical setting on the Internet. Mining such representations from the data in an unsupervised manner is desirable. In this paper, we study a representation based on the multi-scale harmonic analysis of term-term co-occurrence graph. This representation is not only sparse, but also leads to the discovery of semantically coherent topics in data. In our experiments on user-generated short documents e.g., newsgroup messages, user comments, and meta-data, we found this representation to outperform other representations across different choice of classifiers. Similar improvements were also observed for data sets in Chinese and Portuguese languages.
Towards evolutionary compression
Authors: Yunhe Wang, Chang Xu, Jiayan Qiu, Chao Xu, Dacheng Tao
Venue: Conference KDD 2018
Year: 2018

Compressing convolutional neural networks (CNNs) is essential for transferring the success of CNNs to a wide variety of applications to mobile devices. In contrast to directly recognizing subtle weights or filters as redundant in a given CNN, this paper presents an evolutionary method to automatically eliminate redundant convolution filters. We represent each compressed network as a binary individual of specific fitness. Then, the population is upgraded at each evolutionary iteration using genetic operations. As a result, an extremely compact CNN is generated using the fittest individual, which has the original network structure and can be directly deployed in any off-the-shelf deep learning libraries. In this approach, either large or small convolution filters can be redundant, and filters in the compressed network are more distinct. In addition, since the number of filters in each convolutional layer is reduced, the number of filter channels and the size of feature maps are also decreased, naturally improving both the compression and speed-up ratios. Experiments on benchmark deep CNN models suggest the superiority of the proposed algorithm over the state-of-the-art compression methods, e.g. combined with the parameter refining approach, we can reduce the storage requirement and the floating-point multiplications of ResNet-50 by a factor of 14.64x and 5.19x, respectively, without affecting its accuracy.
Online matrix completion for signed link prediction
Authors: Jing Wang, Jie Shen, Ping Li, Huan Xu
Venue: Conference WSDM 2017
Year: 2017

This work studies the binary matrix completion problem underlying a large body of real-world applications such as signed link prediction and information propagation. That is, each entry of the matrix indicates a binary preference such as "like" or "dislike", "trust" or "distrust". However, the performance of existing matrix completion methods may be hindered owing to three practical challenges: 1) the observed data are with binary label (i.e., not real value); 2) the data are typically sampled non-uniformly (i.e., positive links dominate the negative ones) and 3) a network may have a huge volume of data (i.e., memory and computational issue).
Semi-supervised sparse metric learning using alternating linearization optimization
Authors: Wei Liu, Shiqian Ma, Dacheng Tao, Jianzhuang Liu, Peng Liu
Venue: Conference KDD 2010
Year: 2010

In plenty of scenarios, data can be represented as vectors and then mathematically abstracted as points in a Euclidean space. Because a great number of machine learning and data mining applications need proximity measures over data, a simple and universal distance metric is desirable, and metric learning methods have been explored to produce sensible distance measures consistent with data relationship. However, most existing methods suffer from limited labeled data and expensive training. In this paper, we address these two issues through employing abundant unlabeled data and pursuing sparsity of metrics, resulting in a novel metric learning approach called semi-supervised sparse metric learning. Two important contributions of our approach are: 1) it propagates scarce prior affinities between data to the global scope and incorporates the full affinities into the metric learning; and 2) it uses an efficient alternating linearization method to directly optimize the sparse metric. Compared with conventional methods, ours can effectively take advantage of semi-supervision and automatically discover the sparse metric structure underlying input data patterns. We demonstrate the efficacy of the proposed approach with extensive experiments carried out on six datasets, obtaining clear performance gains over the state-of-the-arts.
Epideep: Exploiting embeddings for epidemic forecasting
Authors: Bijaya Adhikari, Xinfeng Xu, Naren Ramakrishnan, B. Aditya Prakash
Venue: Conference KDD 2019
Year: 2019

Influenza leads to regular losses of lives annually and requires careful monitoring and control by health organizations. Annual influenza forecasts help policymakers implement effective countermeasures to control both seasonal and pandemic outbreaks. Existing forecasting techniques suffer from problems such as poor forecasting performance, lack of modeling flexibility, data sparsity, and/or lack of intepretability. We propose EpiDeep, a novel deep neural network approach for epidemic forecasting which tackles all of these issues by learning meaningful representations of incidence curves in a continuous feature space and accurately predicting future incidences, peak intensity, peak time, and onset of the upcoming season. We present extensive experiments on forecasting ILI (influenza-like illnesses) in the United States, leveraging multiple metrics to quantify success. Our results demonstrate that EpiDeep is successful at learning meaningful embeddings and, more importantly, that these embeddings evolve as the season progresses. Furthermore, our approach outperforms non-trivial baselines by up to 40%.
A deep neural framework for sales forecasting in e-commerce
Authors: Yan Qi, Chenliang Li, Han Deng, Min Cai, Yunwei Qi, Yuming Deng
Venue: Conference CIKM 2019
Year: 2019

Product sales forecasting plays a fundamental role in enhancing timeliness of product delivery in E-Commerce. Among many heterogeneous features relevant to sales forecasting, promotion campaigns held in E-Commerce and competing relation between substitutable products would greatly complicate the matter. Unfortunately, these factors are usually overlooked in the existing literature, since the conventional time series analysis based techniques mainly consider the sales records alone. In this paper, we propose a novel deep neural framework for sales forecasting in E-Commerce, named DSF. In DSF, sales forecasting is formulated as a sequence-to-sequence learning problem where the sales is estimated in a recurrent fashion. On top of the decoder, we introduce a sales residual network to explicitly model the impact of competing relation when a promotion campaign is launched for a target item or some substitutable counterparts. Extensive experiments are conducted over two real-world datasets in different domains from Taobao E-Commerce platform. Our results demonstrate that the proposed DSF obtains substantial performance gain over the traditional baselines and up-to-date deep learning alternatives in terms of forecasting accuracy. Further comparison shows that DSF has also surpassed the deep learning based solution currently depolyed in Taobao platform.
A multi-class boosting method with direct optimization
Authors: Shaodan Zhai, Tian Xia, Shaojun Wang
Venue: Conference KDD 2014
Year: 2014

We present a direct multi-class boosting (DMCBoost) method for classification with the following properties: (i) instead of reducing the multi-class classification task to a set of binary classification tasks, DMCBoost directly solves the multi-class classification problem, and only requires very weak base classifiers; (ii) DMCBoost builds an ensemble classifier by directly optimizing the non-convex performance measures, including the empirical classification error and margin functions, without resorting to any upper bounds or approximations. As a non-convex optimization method, DMCBoost shows competitive or better results than state-of-the-art convex relaxation boosting methods, and it performs especially well on the noisy cases.
Cascade-based community detection
Authors: Nicola Barbieri, Francesco Bonchi, Giuseppe Manco
Venue: Conference WSDM 2013
Year: 2013

Given a directed social graph and a set of past informa- tion cascades observed over the graph, we study the novel problem of detecting modules of the graph (communities of nodes), that also explain the cascades. Our key observation is that both information propagation and social ties forma- tion in a social network can be explained according to the same latent factor, which ultimately guide a user behavior within the network. Based on this observation, we propose the Community-Cascade Network (CCN) model, a stochas- tic mixture membership generative model that can fit, at the same time, the social graph and the observed set of cas- cades. Our model produces overlapping communities and for each node, its level of authority and passive interest in each community it belongs. For learning the parameters of the CCN model, we devise a Generalized Expectation Maximization procedure. We then apply our model to real-world social networks and in- formation cascades: the results witness the validity of the proposed CCN model, providing useful insights on its signif- icance for analyzing social behavior.
Differentially private transit data publication: a case study on the montreal transportation system
Authors: Rui Chen, Benjamin C. M. Fung, Bipin C. Desai, Nériah M. Sossou
Venue: Conference KDD 2012
Year: 2012

With the wide deployment of smart card automated fare collection (SCAFC) systems, public transit agencies have been benefiting from huge volume of transit data, a kind of sequential data, collected every day. Yet, improper publishing and use of transit data could jeopardize passengers' privacy. In this paper, we present our solution to transit data publication under the rigorous differential privacy model for the Société de transport de Montréal (STM). We propose an efficient data-dependent yet differentially private transit data sanitization approach based on a hybrid-granularity prefix tree structure. Moreover, as a post-processing step, we make use of the inherent consistency constraints of a prefix tree to conduct constrained inferences, which lead to better utility. Our solution not only applies to general sequential data, but also can be seamlessly extended to trajectory data. To our best knowledge, this is the first paper to introduce a practical solution for publishing large volume of sequential data under differential privacy. We examine data utility in terms of two popular data analysis tasks conducted at the STM, namely count queries and frequent sequential pattern mining. Extensive experiments on real-life STM datasets confirm that our approach maintains high utility and is scalable to large datasets.
Motion based decompositing of video
Authors: Gabriel J. Brostow, Irfan A. Essa
Venue: Conference ICCV 1999
Year: 1999

We present a method to decompose video sequences into layers that represent the relative depths of complex scenes. Our method combines spatial information with temporal occlusions to determine relative depths of these layers. Spatial information is obtained through edge detection and a customized contour completion algorithm. Activity in a scene is used to extract temporal occlusion events, which are in turn, used to classify objects as occluders or occludes. The path traversed by the moving objects determines the segmentation of the scene. Several examples of decompositing and compositing of video are shown. This approach can be applied in the pre-processing of sequences for compositing or tracking purposes and to determine the approximate 3D structure of a scene.
AutoIAS: Automatic Integrated Architecture Searcher for Click-Trough Rate Prediction
Authors: Zhikun Wei, Xin Wang, Wenwu Zhu
Venue: Conference CIKM 2021
Year: 2021

Automating architecture design for recommendation tasks becomes a trending topic because expert efforts are saved, and better performance is expected. Neural Architecture Search (NAS) is introduced to discover powerful CTR prediction model architectures in recent works. CTR prediction model usually consists of three components: embedding layer, interaction layer, and deep neural network. However, existing automation works focus on searching single component and leaving other components hand-crafted. The isolated searching will cause incompatibility among components and lead to weak generalization ability. Moreover, there is not a unified framework for integrated CTR prediction model architecture searching. This paper presents Automatic Integrated Architecture Searcher (AutoIAS), a framework that provides a practical and general method to find optimal CTR prediction model architecture in an automatic manner. In AutoIAS, we unify existing interaction-based CTR prediction model architectures and propose an integrated search space for a complete CTR prediction model. We utilize a supernet to predict the performance of sub-architectures, and the supernet is trained with Knowledge Distillation(KD) to enhance consistency among sub-architectures. To efficiently explore the search space, we design an architecture generator network that explicitly models the architecture dependencies among components and generates conditioned architectures distribution for each component. Experiments on public datasets show the outstanding performance and generalization ability of AutoIAS. Ablation study shows the effectiveness of the KD-based supernet training method and the Architecture Generator Network.