OpenRaiser/Intern-Atlas huggingface dataset:
Optimal Texture Map Reconstruction from Multiple Views
Authors: Lifeng Wang 0001, Sing Bing Kang, Richard Szeliski, Heung-Yeung Shum
Venue: Conference CVPR 2001
Year: 2001

The recovery of 3D models from multiple reference images involves not only the extraction of 3D shape, but also of texture. Assuming that all surfaces are Lambertian, the resulting final texture is typically computed as a linear combination of reference textures. This is, however, not the optimal means for reconstructing textures, since this does not model the anisotropy in the texture projection. Furthermore, the spatial image sampling may be quite variable within a fore-shortened surface. This also has important implications for computer vision techniques that involve analysis by synthesis and the image-based rendering (IBR) technique of view-dependent texture mapping (VDTM). Starting with sampling theory, we show how weights should be spatially distributed for optimal texture construction. The local weights take into consideration the effects of anisotropy and variable spatial image sampling. We also present experimental results to verify our analysis.
Recurrent Recommendation with Local Coherence
Authors: Jianling Wang, James Caverlee
Venue: Conference WSDM 2019
Year: 2019

We propose a new time-dependent predictive model of user-item ratings centered around local coherence -- that is, while both users and items are constantly in flux, within a short-term sequence, the neighborhood of a particular user or item is likely to be coherent. Three unique characteristics of the framework are: (i) it incorporates both implicit and explicit feedbacks by extracting the local coherence hidden in the feedback sequences; (ii) it uses parallel recurrent neural networks to capture the evolution of users and items, resulting in a dual factor recommendation model; and (iii) it combines both coherence-enhanced consistent latent factors and dynamic latent factors to balance short-term changes with long-term trends for improved recommendation. Through experiments on Goodreads and Amazon, we find that the proposed model can outperform state-of-the-art models in predicting users' preferences.
Distant Supervision for Multi- Stage Fine-Tuning in Retrieval-Based Question Answering
Authors: Yuqing Xie, Wei Yang, Luchen Tan, Kun Xiong, Nicholas Jing Yuan, Baoxing Huai, Ming Li, Jimmy Lin
Venue: Conference WWW 2020
Year: 2020

We tackle the problem of question answering directly on a large document collection, combining simple “bag of words” passage retrieval with a BERT-based reader for extracting answer spans. In the context of this architecture, we present a data augmentation technique using distant supervision to automatically annotate paragraphs as either positive or negative examples to supplement existing training data, which are then used together to fine-tune BERT. We explore a number of details that are critical to achieving high accuracy in this setup: the proper sequencing of different datasets during fine-tuning, the balance between “difficult” vs. “easy” examples, and different approaches to gathering negative examples. Experimental results show that, with the appropriate settings, we can achieve large gains in effectiveness on two English and two Chinese QA datasets. We are able to achieve results at or near the state of the art without any modeling advances, which once again affirms the cliché “there’s no data like more data”.
Learning attention map from images
Authors: Yao Lu 0028, Wei Zhang 0016, Cheng Jin 0001, Xiangyang Xue 0001
Venue: Conference CVPR 2012
Year: 2012

While bottom-up and top-down processes have shown effectiveness during predicting attention and eye fixation maps on images, in this paper, inspired by the perceptual organization mechanism before attention selection, we propose to utilize figure-ground maps for the purpose. So as to take both pixel-wise and region-wise interactions into consideration when predicting label probabilities for each pixel, we develop a context-aware model based on multiple segmentation to obtain final results. The MIT attention dataset [14] is applied finally to evaluate both new features and model. Quantitative experiments demonstrate that figure-ground cues are valid in predicting attention selection, and our proposed model produces improvements over baseline method.
Finding subgraphs with maximum total density and limited overlap
Authors: Oana Denisa Balalau, Francesco Bonchi, T.-H. Hubert Chan, Francesco Gullo, Mauro Sozio
Venue: Conference WSDM 2015
Year: 2015

Finding dense subgraphs in large graphs is a key primitive in a variety of real-world application domains, encompassing social network analytics, event detection, biology, and finance. In most such applications, one typically aims at finding several (possibly overlapping) dense subgraphs which might correspond to communities in social networks or interesting events. While a large amount of work is devoted to finding a single densest subgraph, perhaps surprisingly, the problem of finding several dense subgraphs with limited overlap has not been studied in a principled way, to the best of our knowledge. In this work we define and study a natural generalization of the densest subgraph problem, where the main goal is to find at most $k$ subgraphs with maximum total aggregate density, while satisfying an upper bound on the pairwise Jaccard coefficient between the sets of nodes of the subgraphs. After showing that such a problem is NP-Hard, we devise an efficient algorithm that comes with provable guarantees in some cases of interest, as well as, an efficient practical heuristic. Our extensive evaluation on large real-world graphs confirms the efficiency and effectiveness of our algorithms.
Deephoops: Evaluating micro-actions in basketball using deep feature representations of spatio-temporal data
Authors: Anthony Sicilia, Konstantinos Pelechrinis, Kirk Goldsberry
Venue: Conference KDD 2019
Year: 2019

Basketball is one of a number of sports which, within the past decade, have seen an explosion in quantitative metrics and methods for evaluating players and teams. However, it is still challenging to evaluate individual off-ball events (e.g., screens, cuts away from the ball etc.) in terms of how they contribute to the success of a possession. In this study, we develop a deep learning framework DeepHoops to process a unique dataset composed of spatio-temporal tracking data from NBA games in order to generate a running stream of predictions on the expected points to be scored as a possession progresses. We frame the problem as a multi-class sequence classification problem in which our model estimates probabilities of terminal actions taken by players (e.g. take field goal, turnover, foul etc.) at each moment of a possession based on a sequence of ball and player court locations preceding the said moment. Each of these terminal actions is associated with an expected point value, which is used to estimate the expected points to be scored. One of the challenges associated with this problem is the high imbalance in the action classes. To solve this problem, we parameterize a downsampling scheme for the training phase. We demonstrate that DeepHoops is well-calibrated, estimating accurately the probabilities of each terminal action and we further showcase the model's capability to evaluate individual actions (potentially off-ball) within a possession that are not captured by boxscore statistics.
Diverse retrieval via greedy optimization of expected 1-call@k in a latent subtopic relevance model
Authors: Scott Sanner, Shengbo Guo, Thore Graepel, Sadegh Kharazmi, Sarvnaz Karimi
Venue: Conference CIKM 2011
Year: 2011

It has been previously observed that optimization of the [email protected] relevance objective (i.e., a set-based objective that is 1 if at least one document is relevant, otherwise 0) empirically correlates with diverse retrieval. In this paper, we proceed one step further and show theoretically that greedily optimizing expected [email protected] w.r.t. a latent subtopic model of binary relevance leads to a diverse retrieval algorithm sharing many features of existing diversification approaches. This new result is complementary to a variety of diverse retrieval algorithms derived from alternate rank-based relevance criteria such as average precision and reciprocal rank. As such, the derivation presented here for expected [email protected] provides a novel theoretical perspective on the emergence of diversity via a latent subtopic model of relevance --- an idea underlying both ambiguous and faceted subtopic retrieval that have been used to motivate diverse retrieval.
Extremely fast text feature extraction for classification and indexing
Authors: George Forman, Evan Kirshenbaum
Venue: Conference CIKM 2008
Year: 2008

Most research in speeding up text mining involves algorithmic improvements to induction algorithms, and yet for many large scale applications, such as classifying or indexing large document repositories, the time spent extracting word features from texts can itself greatly exceed the initial training time. This paper describes a fast method for text feature extraction that folds together Unicode conversion, forced lowercasing, word boundary detection, and string hash computation. We show empirically that our integer hash features result in classifiers with equivalent statistical performance to those built using string word features, but require far less computation and less memory.
Characterizing and Learning Representation on Customer Contact Journeys in Cellular Services
Authors: Shuai Zhao 0008, Wen-Ling Hsu, George Ma, Tan Xu, Guy Jacobson, Raif M. Rustamov
Venue: Conference KDD 2020
Year: 2020

Corporations spend billions of dollars annually caring for customers across multiple contact channels. A customer journey is the complete sequence of contacts that a given customer has with a company across multiple channels of communication. While each contact is important and contains rich information, studying customer journeys provides a better context to understand customers' behavior in order to improve customer satisfaction and loyalty, and to reduce care costs. However, journey sequences have a complex format due to the heterogeneity of user behavior: they are variable-length, multi-attribute, and exhibit a large cardinality in categories (e.g. contact reasons). The question of how to characterize and learn representations of customer journeys has not been studied in the literature. We propose to learn journey embeddings using a sequence-to-sequence framework that converts each customer journey into a fixed-length latent embedding. In order to improve the disentanglement and distributional properties of embeddings, the model is further modified by incorporating a Wasserstein autoencoder inspired regularization on the distribution of embeddings. Experiments conducted on an enterprise-scale dataset demonstrate the effectiveness of the proposed model and reveal significant improvements due to the regularization in both distinguishing journey pattern characteristics and predicting future customer engagement.
The WDC training dataset and gold standard for large-scale product matching
Authors: Anna Primpeli, Ralph Peeters, Christian Bizer
Venue: Conference WWW 2019
Year: 2019

A current research question in the area of entity resolution (also called link discovery or duplicate detection) is whether and in which cases embeddings and deep neural network based matching methods outperform traditional symbolic matching methods. The problem with answering this question is that deep learning based matchers need large amounts of training data. The entity resolution benchmark datasets that are currently available to the public are too small to properly evaluate this new family of matching methods. The WDC Training Dataset for Large-Scale Product Matching fills this gap. The English language subset of the training dataset consists of 20 million pairs of offers referring to the same products. The offers were extracted from 43 thousand e-shops which provide schema.org annotations including some form of product ID such as a GTIN or MPN. We also created a gold standard by manually verifying 2200 pairs of offers belonging to four product categories. Using a subset of our training dataset together with this gold standard, we are able to publicly replicate the recent result of Mudgal et al. that embeddings and deep neural network based matching methods outperform traditional symbolic matching methods on less structured data.
Multi-context attention for entity matching
Authors: Dongxiang Zhang, Yuyang Nie, Sai Wu, Yanyan Shen, Kian-Lee Tan
Venue: Conference WWW 2020
Year: 2020

Entity matching (EM) is a classic research problem that identifies data instances referring to the same real-world entity. Recent technical trend in this area is to take advantage of deep learning (DL) to automatically extract discriminative features. DeepER and DeepMatcher have emerged as two pioneering DL models for EM. However, these two state-of-the-art solutions simply incorporate vanilla RNNs and straightforward attention mechanisms. In this paper, we fully exploit the semantic context of embedding vectors for the pair of entity text descriptions. In particular, we propose an integrated multi-context attention framework that takes into account self-attention, pair-attention and global-attention from three types of context. The idea is further extended to incorporate attribute attention in order to support structured datasets. We conduct extensive experiments with 7 benchmark datasets that are publicly accessible. The experimental results clearly establish our superiority over DeepER and DeepMatcher in all the datasets.
Sponsored search auctions with rich ads
Authors: Ruggiero Cavallo, Prabhakar Krishnamurthy, Maxim Sviridenko, Christopher A. Wilkens
Venue: Conference WWW 2017
Year: 2017

The generalized second price (GSP) auction has served as the core selling mechanism for sponsored search ads for over a decade. However, recent trends expanding the set of allowed ad formats---to include a variety of sizes, decorations, and other distinguishing features---have raised critical problems for GSP-based platforms. Alternatives such as the Vickrey-Clarke-Groves (VCG) auction raise different complications because they fundamentally change the way prices are computed. In this paper we report on our efforts to redesign a search ad selling system from the ground up in this new context, proposing a mechanism that optimizes an entire slate of ads globally and computes prices that achieve properties analogous to those held by GSP in the original, simpler setting of uniform ads. A careful algorithmic coupling of allocation-optimization and pricing-computation allows our auction to operate within the strict timing constraints inherent in real-time ad auctions. We report performance results of the auction in Yahoo's Gemini Search platform.
All models are useful: Bayesian ensembling for robust high resolution covid-19 forecasting
Authors: Aniruddha Adiga, Lijing Wang, Benjamin Hurt, Akhil Sai Peddireddy, Przemyslaw J. Porebski, Srinivasan Venkatramanan, Bryan Leroy Lewis, Madhav V. Marathe
Venue: Conference KDD 2021
Year: 2021

Timely, high-resolution forecasts of infectious disease incidence are useful for policy makers in deciding intervention measures and estimating healthcare resource burden. In this paper, we consider the task of forecasting COVID-19 confirmed cases at the county level for the United States. Although multiple methods have been explored for this task, their performance has varied across space and time due to noisy data and the inherent dynamic nature of the pandemic. We present a forecasting pipeline which incorporates probabilistic forecasts from multiple statistical, machine learning and mechanistic methods through a Bayesian ensembling scheme, and has been operational for nearly 6 months serving local, state and federal policymakers in the United States. While showing that the Bayesian ensemble is at least as good as the individual methods, we also show that each individual method contributes significantly for different spatial regions and time points. We compare our model's performance with other similar models being integrated into CDC-initiated COVID-19 Forecast Hub, and show better performance at longer forecast horizons. Finally, we also describe how such forecasts are used to increase lead time for training mechanistic scenario projections. Our work demonstrates that such a real-time high resolution forecasting pipeline can be developed by integrating multiple methods within a performance-based ensemble to support pandemic response.
Intrinsic images using optimization
Authors: Jianbing Shen, Xiaoshan Yang, Yunde Jia, Xuelong Li 0001
Venue: Conference CVPR 2011
Year: 2011

In this paper, we present a novel intrinsic image recovery approach using optimization. Our approach is based on the assumption of in a local window in natural images. Our method adopts a premise that neighboring pixels in a local window of a single image having similar intensity values should have similar reflectance values. Thus the intrinsic image decomposition is formulated by optimizing an energy function with adding a weighting constraint to the local image properties. In order to improve the intrinsic image extraction results, we specify local constrain cues by integrating the user strokes in our energy formulation, including constant-reflectance, constant-illumination and fixed-illumination brushes. Our experimental results demonstrate that our approach achieves a better recovery of intrinsic reflectance and illumination components than by previous approaches.
Tolerant markov boundary discovery for feature selection
Authors: Xingyu Wu, Bingbing Jiang, Yan Zhong, Huanhuan Chen
Venue: Conference CIKM 2020
Year: 2020

Due to the interpretability and robustness, Markov boundary (MB) has received much attention and been widely applied to causal feature selection. However, enormous empirical studies show that, existing algorithms achieve outstanding performance only on the standard Bayesian network data. While on the real-world data, they could not identify some of the relevant features since the large conditioning set and the ignored multivariate dependence lead to performance degradation. In this paper, we propose a tolerant MB discovery algorithm (TLMB), which maps the feature space and target space to a reproducing kernel Hilbert space through the conditional covariance operator, to measure the causal information carried by a feature. Specifically, TLMB uses a score function to filter the redundant features first and then minimize the trace of the conditional covariance operator, where both of the score function and the optimization problem work in the reproducing kernel Hilbert space so that TLMB can select features with not only pairwise dependence but also multivariate dependence. Moreover, as a MB-based method, TLMB can automatically determine the number of selected features due to the property of MB.
Taxi driving behavior analysis in latent vehicle-to-vehicle networks: A social influence perspective
Authors: Tong Xu, Hengshu Zhu, Xiangyu Zhao, Qi Liu, Hao Zhong, Enhong Chen, Hui Xiong
Venue: Conference KDD 2016
Year: 2016

With recent advances in mobile and sensor technologies, a large amount of efforts have been made on developing intelligent applications for taxi drivers, which provide beneficial guide and opportunity to improve the profit and work efficiency. However, limited scopes focus on the latent social interaction within cab drivers, and corresponding social propagation scheme to share driving behaviors has been largely ignored. To that end, in this paper, we propose a comprehensive study to reveal how the social propagation affects for better prediction of cab drivers' future behaviors. To be specific, we first investigate the correlation between drivers' skills and their mutual interactions in the latent vehicle-to-vehicle network, which intuitively indicates the effects of social influences. Along this line, by leveraging the classic social influence theory, we develop a two-stage framework for quantitatively revealing the latent driving pattern propagation within taxi drivers. Comprehensive experiments on a real-word data set collected from the New York City clearly validate the effectiveness of our proposed framework on predicting future taxi driving behaviors, which also support the hypothesis that social factors indeed improve the predictability of driving behaviors.
Deep Learning for Anomaly Detection: Challenges, Methods, and Opportunities
Authors: Guansong Pang, Longbing Cao, Charu Aggarwal
Venue: Conference WSDM 2021
Year: 2021

In this tutorial we aim to present a comprehensive survey of the advances in deep learning techniques specifically designed for anomaly detection (deep anomaly detection for short). Deep learning has gained tremendous success in transforming many data mining and machine learning tasks, but popular deep learning techniques are inapplicable to anomaly detection due to some unique characteristics of anomalies, e.g., rarity, heterogeneity, boundless nature, and prohibitively high cost of collecting large-scale anomaly data. Through this tutorial, audiences would gain a systematic overview of this area, learn the key intuitions, objective functions, underlying assumptions, advantages and disadvantages of different categories of state-of-the-art deep anomaly detection methods, and recognize its broad real-world applicability in diverse domains. We also discuss what challenges the current deep anomaly detection methods can address and envision this area from multiple different perspectives. Any audience who may be interested in deep learning, anomaly/outlier/novelty detection, out-of-distribution detection, representation learning with limited labeled data, and self-supervised representation learning would find it very helpful in attending this tutorial. Researchers and practitioners in finance, cybersecurity, healthcare would also find the tutorial helpful in practice.
Modeling User Behavior With Interaction Networks for Spam Detection
Authors: Prabhat Agarwal, Manisha Srivastava, Vishwakarma Singh, Charles Rosenberg
Venue: Conference SIGIR 2022
Year: 2022

Spam is a serious problem plaguing web-scale digital platforms which facilitate user content creation and distribution. It compromises platform's integrity, performance of services like recommendation and search, and overall business. Spammers engage in a variety of abusive and evasive behavior which are distinct from non-spammers. Users' complex behavior can be well represented by a heterogeneous graph rich with node and edge attributes. Learning to identify spammers in such a graph for a web-scale platform is challenging because of its structural complexity and size. In this paper, we propose SEINE (Spam DEtection using Interaction NEtworks), a spam detection model over a novel graph framework. Our graph simultaneously captures rich users' details and behavior and enables learning on a billion-scale graph. Our model considers neighborhood along with edge types and attributes, allowing it to capture a wide range of spammers. SEINE, trained on a real dataset of tens of millions of nodes and billions of edges, achieves a high performance of 80% recall with 1% false positive rate. SEINE achieves comparable performance to the state-of-the-art techniques on a public dataset while being pragmatic to be used in a large-scale production system.