OpenRaiser/Intern-Atlas huggingface dataset:
React: Online multimodal embedding for recency-aware spatiotemporal activity modeling
Authors: Chao Zhang, Keyang Zhang, Quan Yuan, Fangbo Tao, Luming Zhang, Tim Hanratty, Jiawei Han
Venue: Conference SIGIR 2017
Year: 2017

Spatiotemporalactivity modeling is an important task for applications like tour recommendation and place search. The recently developed geographical topic models have demonstrated compelling results in using geo-tagged social media (GTSM) for spatiotemporal activity modeling. Nevertheless, they all operate in batch and cannot dynamically accommodate the latest information in the GTSM stream to reveal up-to-date spatiotemporal activities. We propose ReAct, a method that processes continuous GTSM streams and obtains recency-aware spatiotemporal activity models on the fly. Distinguished from existing topic-based methods, ReAct embeds all the regions, hours, and keywords into the same latent space to capture their correlations. To generate high-quality embeddings, it adopts a novel semi-supervised multimodal embedding paradigm that leverages the activity category information to guide the embedding process. Furthermore, as new records arrive continuously, it employs strategies to effectively incorporate the new information while preserving the knowledge encoded in previous embeddings. Our experiments on the geo-tagged tweet streams in two major cities have shown that ReAct significantly outperforms existing methods for location and activity retrieval tasks.
Creating a Children-Friendly Reading Environment via Joint Learning of Content and Human Attention
Authors: Guoxiu He, Yangyang Kang, Zhuoren Jiang, Jiawei Liu 0002, Changlong Sun, Xiaozhong Liu 0001, Wei Lu 0019
Venue: Conference SIGIR 2020
Year: 2020

Technological advancements have led to increasing availability of erotic literature and pornography novels online, which can be alluring to adolescence and children. Unfortunately, because of the inherent complexity of these indecent contents and training data sparseness, it is a challenging task to detect these readings in the Cyberspace while children can easily access them. In this study, we propose a novel framework, Joint Learning of Content and Human Attention (GoodMan), to identify indecent readings by augmenting natural language understanding models with large scale human reading behaviors (dwell time per page) on portable devices. From the text modeling viewpoint, the innovative joint attention trained by joint learning is employed to orchestrate the content attention and human behavior attention via the BiGRU. From the data augmentation perspective, various users' reading behaviors on the same text can generate considerable training instances with joint attention, which can be effective to address the cold start problem. We conduct an extensive set of experiments on an online ebook dataset (with human reading behaviors on portable devices). The experimental results show insights into the task and demonstrate the superiority of the proposed model against alternative solutions.
Returning is Believing: Optimizing Long-term User Engagement in Recommender Systems
Authors: Qingyun Wu, Hongning Wang, Liangjie Hong, Yue Shi
Venue: Conference CIKM 2017
Year: 2017

In this work, we propose to improve long-term user engagement in a recommender system from the perspective of sequential decision optimization, where users' click and return behaviors are directly modeled for online optimization. A bandit-based solution is formulated to balance three competing factors during online learning, including exploitation for immediate click, exploitation for expected future clicks, and exploration of unknowns for model estimation. We rigorously prove that with a high probability our proposed solution achieves a sublinear upper regret bound in maximizing cumulative clicks from a population of users in a given period of time, while a linear regret is inevitable if a user's temporal return behavior is not considered when making the recommendations. Extensive experimentation on both simulations and a large-scale real-world dataset collected from Yahoo frontpage news recommendation log verified the effectiveness and significant improvement of our proposed algorithm compared with several state-of-the-art online learning baselines for recommendation.
Semantic texton forests for image categorization and segmentation
Authors: Jamie Shotton, Matthew Johnson 0003, Roberto Cipolla
Venue: Conference CVPR 2008
Year: 2008

We propose semantic texton forests, efficient and powerful new low-level features. These are ensembles of decision trees that act directly on image pixels, and therefore do not need the expensive computation of filter-bank responses or local descriptors. They are extremely fast to both train and test, especially compared with k-means clustering and nearest-neighbor assignment of feature descriptors. The nodes in the trees provide (i) an implicit hierarchical clustering into semantic textons, and (ii) an explicit local classification estimate. Our second contribution, the bag of semantic textons, combines a histogram of semantic textons over an image region with a region prior category distribution. The bag of semantic textons is computed over the whole image for categorization, and over local rectangular regions for segmentation. Including both histogram and region prior allows our segmentation algorithm to exploit both textural and semantic context. Our third contribution is an image-level prior for segmentation that emphasizes those categories that the automatic categorization believes to be present. We evaluate on two datasets including the very challenging VOC 2007 segmentation dataset. Our results significantly advance the state-of-the-art in segmentation accuracy, and furthermore, our use of efficient decision forests gives at least a five-fold increase in execution speed.
Embedding Node Structural Role Identity into Hyperbolic Space
Authors: Lili Wang, Ying Lu, Chenghan Huang, Soroush Vosoughi
Venue: Conference CIKM 2020
Year: 2020

Recently, there has been an interest in embedding networks in hyperbolic space, since hyperbolic space has been shown to work well in capturing graph/network structure as it can naturally reflect some properties of complex networks. However, the work on network embedding in hyperbolic space has been focused on microscopic node embedding. In this work, we are the first to present a framework to embed the structural roles of nodes into hyperbolic space. Our framework extends struct2vec, a well-known structural role preserving embedding method, by moving it to a hyperboloid model. We evaluated our method on four real-world and one synthetic network. Our results show that hyperbolic space is more effective than euclidean space in learning latent representations for the structural role of nodes.
Shape-based 3D surface correspondence using geodesics and local geometry
Authors: Yongmei Wang, Bradley S. Peterson, Lawrence H. Staib
Venue: Conference CVPR 2000
Year: 2000

This paper describes a new method for determining correspondence between points on pairs of surfaces based on shape using a combination of geodesic distance and surface curvature. An initial sparse set of corresponding points is generated using a shape-based matching procedure. Geodesic interpolation is employed in order to capture the complex surface. In addition, surface correspondence and triangulation are computed simultaneously in a hierarchical way. Results applied to human cerebral cortical surfaces are shown to evaluate the approach.
On the regularization of wasserstein gans
Authors: Henning Petzka, Asja Fischer, Denis Lukovnikov
Venue: Conference ICLR 2018
Year: 2018

Since their invention, generative adversarial networks (GANs) have become a popular approach for learning to model a distribution of real (unlabeled) data. Convergence problems during training are overcome by Wasserstein GANs which minimize the distance between the model and the empirical distribution in terms of a different metric, but thereby introduce a Lipschitz constraint into the optimization problem. A simple way to enforce the Lipschitz constraint on the class of functions, which can be modeled by the neural network, is weight clipping. It was proposed that training can be improved by instead augmenting the loss by a regularization term that penalizes the deviation of the gradient of the critic (as a function of the network's input) from one. We present theoretical arguments why using a weaker regularization term enforcing the Lipschitz constraint is preferable. These arguments are supported by experimental results on toy data sets.
Data-driven simulation and optimization for COVID-19 exit strategies
Authors: Salah Ghamizi, Renaud Rwemalika, Maxime Cordy, Lisa Veiber, Tegawendé F. Bissyandé, Mike Papadakis, Jacques Klein, Yves Le Traon
Venue: Conference KDD 2020
Year: 2020

The rapid spread of the Coronavirus SARS-2 is a major challenge that led almost all governments worldwide to take drastic measures to respond to the tragedy. Chief among those measures is the massive lockdown of entire countries and cities, which beyond its global economic impact has created some deep social and psychological tensions within populations. While the adopted mitigation measures (including the lockdown) have generally proven useful, policymakers are now facing a critical question: how and when to lift the mitigation measures? A carefully-planned exit strategy is indeed necessary to recover from the pandemic without risking a new outbreak. Classically, exit strategies rely on mathematical modeling to predict the effect of public health interventions. Such models are unfortunately known to be sensitive to some key parameters, which are usually set based on rules-of-thumb.In this paper, we propose to augment epidemiological forecasting with actual data-driven models that will learn to fine-tune predictions for different contexts (e.g., per country). We have therefore built a pandemic simulation and forecasting toolkit that combines a deep learning estimation of the epidemiological parameters of the disease in order to predict the cases and deaths, and a genetic algorithm component searching for optimal trade-offs/policies between constraints and objectives set by decision-makers. Replaying pandemic evolution in various countries, we experimentally show that our approach yields predictions with much lower error rates than pure epidemiological models in 75% of the cases and achieves a 95% R2 score when the learning is transferred and tested on unseen countries. When used for forecasting, this approach provides actionable insights into the impact of individual measures and strategies.
Understanding the urban pandemic spreading of covid-19 with real world mobility data
Authors: Qianyue Hao, Lin Chen 0002, Fengli Xu, Yong Li 0008
Venue: Conference KDD 2020
Year: 2020

Facing the worldwide rapid spreading of COVID-19 pandemic, we need to understand its diffusion in the urban environments with heterogeneous population distribution and mobility. However, challenges exist in the choice of proper spatial resolution, integration of mobility data into epidemic modelling, as well as incorporation of unique characteristics of COVID-19.
Competitive analysis for points of interest
Authors: Shuangli Li, Jingbo Zhou 0003, Tong Xu 0001, Hao Liu 0026, Xinjiang Lu, Hui Xiong 0001
Venue: Conference KDD 2020
Year: 2020

The competitive relationship of Points of Interest (POIs) refers to the degree of competition between two POIs for business opportunities from third parties in an urban area. Existing studies for competitive analysis usually focus on mining competitive relationships of entities, such as companies or products, from textual data. However, there are few studies which have a focus on competitive analysis for POIs. Indeed, the growing availability of user behavior data about POIs, such as POI reviews and human mobility data, enables a new paradigm for understanding the competitive relationships among POIs. To this end, in this paper, we study how to predict the POI competitive relationship. Along this line, a very first challenge is how to integrate heterogeneous user behavior data with the spatial features of POIs. As a solution, we first build a heterogeneous POI information network (HPIN) from POI reviews and map search data. Then, we develop a graph neural network-based deep learning framework, named DeepR, for POI competitive relationship prediction based on HPIN. Specifically, DeepR contains two components: a spatial adaptive graph neural network (SA-GNN) and a POI pairwise knowledge extraction learning (PKE) model. The SA-GNN is a novel GNN architecture with incorporating POI's spatial information and location distribution by a specially designed spatial oriented aggregation layer and spatial-dependency attentive propagation mechanism. In addition, PKE is devised to distill the POI pairwise knowledge in HPIN being useful for relationship prediction into condensate vectors with relational graph convolution and cross attention. Finally, extensive experiments on two real-world datasets demonstrate the effectiveness of our method.
Poly-encoders: Architectures and Pre training Strategies for Fast and Accurate Multi sentence Scoring
Authors: Samuel Humeau 0001, Kurt Shuster 0001, Marie-Anne Lachaux, Jason Weston
Venue: Conference ICLR 2020
Year: 2020

The use of deep pre-trained transformers has led to remarkable progress in a number of applications (Devlin et al., 2018). For tasks that make pairwise comparisons between sequences, matching a given input with a corresponding label, two approaches are common: Cross-encoders performing full self-attention over the pair and Bi-encoders encoding the pair separately. The former often performs better, but is too slow for practical use. In this work, we develop a new transformer architecture, the Poly-encoder, that learns global rather than token level self-attention features. We perform a detailed comparison of all three approaches, including what pre-training and fine-tuning strategies work best. We show our models achieve state-of-the-art results on four tasks; that Poly-encoders are faster than Cross-encoders and more accurate than Bi-encoders; and that the best results are obtained by pre-training on large datasets similar to the downstream tasks.
Scalable private learning with PATE
Authors: Nicolas Papernot, Shuang Song 0001, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, Úlfar Erlingsson
Venue: Conference ICLR 2018
Year: 2018

The rapid adoption of machine learning has increased concerns about the privacy implications of machine learning models trained on sensitive data, such as medical records or other personal information. To address those concerns, one promising approach is Private Aggregation of Teacher Ensembles, or PATE, which transfers to a "student" model the knowledge of an ensemble of "teacher" models, with intuitive privacy provided by training teachers on disjoint data and strong privacy guaranteed by noisy aggregation of teachers' answers. However, PATE has so far been evaluated only on simple classification tasks like MNIST, leaving unclear its utility when applied to larger-scale learning tasks and real-world datasets. In this work, we show how PATE can scale to learning tasks with large numbers of output classes and uncurated, imbalanced training data with errors. For this, we introduce new noisy aggregation mechanisms for teacher ensembles that are more selective and add less noise, and prove their tighter differential-privacy guarantees. Our new mechanisms build on two insights: the chance of teacher consensus is increased by using more concentrated noise and, lacking consensus, no answer need be given to a student. The consensus answers used are more likely to be correct, offer better intuitive privacy, and incur lower-differential privacy cost. Our evaluation shows our mechanisms improve on the original PATE on all measures, and scale to larger tasks with both high utility and very strong privacy ($\varepsilon$ < 1.0).
Speaker-aware bert for multi-turn response selection in retrieval-based chatbots
Authors: Jia-Chen Gu, Tianda Li, Quan Liu, Zhen-Hua Ling, Zhiming Su, Si Wei, Xiaodan Zhu
Venue: Conference CIKM 2020
Year: 2020

In this paper, we study the problem of employing pre-trained language models for multi-turn response selection in retrieval-based chatbots. A new model, named Speaker-Aware BERT (SA-BERT), is proposed in order to make the model aware of the speaker change information, which is an important and intrinsic property of multi-turn dialogues. Furthermore, a speaker-aware disentanglement strategy is proposed to tackle the entangled dialogues. This strategy selects a small number of most important utterances as the filtered context according to the speakers' information in them. Finally, domain adaptation is performed to incorporate the in-domain knowledge into pre-trained language models. Experiments on five public datasets show that our proposed model outperforms the present models on all metrics by large margins and achieves new state-of-the-art performances for multi-turn response selection.
Raim: Recurrent attentive and intensive model of multimodal patient monitoring data
Authors: Yanbo Xu, Siddharth Biswal, Shriprasad R. Deshpande, Kevin O. Maher, Jimeng Sun
Venue: Conference KDD 2018
Year: 2018

With the improvement of medical data capturing, vast amount of continuous patient monitoring data, e.g., electrocardiogram (ECG), real-time vital signs and medications, become available for clinical decision support at intensive care units (ICUs). However, it becomes increasingly challenging to model such data, due to high density of the monitoring data, heterogeneous data types and the requirement for interpretable models. Integration of these high-density monitoring data with the discrete clinical events (including diagnosis, medications, labs) is challenging but potentially rewarding since richness and granularity in such multimodal data increase the possibilities for accurate detection of complex problems and predicting outcomes (e.g., length of stay and mortality). We propose Recurrent Attentive and Intensive Model (RAIM) for jointly analyzing continuous monitoring data and discrete clinical events. RAIM introduces an efficient attention mechanism for continuous monitoring data (e.g., ECG), which is guided by discrete clinical events (e.g, medication usage). We apply RAIM in predicting physiological decompensation and length of stay in those critically ill patients at ICU. With evaluations on MIMIC-III Waveform Database Matched Subset, we obtain an AUC-ROC score of $90.18%$ for predicting decompensation and an accuracy of $86.82%$ for forecasting length of stay with our final model, which outperforms our six baseline models.
Enabling deep spiking neural networks with hybrid conversion and spike timing dependent backpropagation
Authors: Nitin Rathi, Gopalakrishnan Srinivasan, Priyadarshini Panda, Kaushik Roy 0001
Venue: Conference ICLR 2020
Year: 2020

Spiking Neural Networks (SNNs) operate with asynchronous discrete events (or spikes) which can potentially lead to higher energy-efficiency in neuromorphic hardware implementations. Many works have shown that an SNN for inference can be formed by copying the weights from a trained Artificial Neural Network (ANN) and setting the firing threshold for each layer as the maximum input received in that layer. These type of converted SNNs require a large number of time steps to achieve competitive accuracy which diminishes the energy savings. The number of time steps can be reduced by training SNNs with spike-based backpropagation from scratch, but that is computationally expensive and slow. To address these challenges, we present a computationally-efficient training technique for deep SNNs. We propose a hybrid training methodology: 1) take a converted SNN and use its weights and thresholds as an initialization step for spike-based backpropagation, and 2) perform incremental spike-timing dependent backpropagation (STDB) on this carefully initialized network to obtain an SNN that converges within few epochs and requires fewer time steps for input processing. STDB is performed with a novel surrogate gradient function defined using neuron's spike time. The proposed training methodology converges in less than 20 epochs of spike-based backpropagation for most standard image classification datasets, thereby greatly reducing the training complexity compared to training SNNs from scratch. We perform experiments on CIFAR-10, CIFAR-100, and ImageNet datasets for both VGG and ResNet architectures. We achieve top-1 accuracy of 65.19% for ImageNet dataset on SNN with 250 time steps, which is 10X faster compared to converted SNNs with similar accuracy.
A collaborative session-based recommendation approach with parallel memory modules
Authors: Meirui Wang, Pengjie Ren, Lei Mei, Zhumin Chen, Jun Ma, Maarten de Rijke
Venue: Conference SIGIR 2019
Year: 2019

Session-based recommendation is the task of predicting the next item to recommend when the only available information consists of anonymous behavior sequences. Previous methods for session-based recommendation focus mostly on the current session, ignoring collaborative information in so-called neighborhood sessions, sessions that have been generated previously by other users and reflect similar user intents as the current session. We hypothesize that the collaborative information contained in such neighborhood sessions may help to improve recommendation performance for the current session. We propose a Collaborative Session-based Recommendation Machine (CSRM), a novel hybrid framework to apply collaborative neighborhood information to session-based recommendations. CSRM consists of two parallel modules: an Inner Memory Encoder (IME) and an Outer Memory Encoder (OME). The IME models a user's own information in the current session with the help of Recurrent Neural Networks (RNNs) and an attention mechanism. The OME exploits collaborative information to better predict the intent of current sessions by investigating neighborhood sessions. Then, a fusion gating mechanism is used to selectively combine information from the IME and OME so as to obtain the final representation of the current session. Finally, CSRM obtains a recommendation score for each candidate item by computing a bilinear match with the final representation. Experimental results on three public datasets demonstrate the effectiveness of CSRM compared to state-of-the-art session-based recommender systems. Our analysis of CSRM's recommendation process shows when and how collaborative neighborhood information and the fusion gating mechanism positively impact the performance of session-based recommendations.
Model compression with two-stage multi-teacher knowledge distillation for web question answering system
Authors: Ze Yang, Linjun Shou, Ming Gong, Wutao Lin, Daxin Jiang
Venue: Conference WSDM 2020
Year: 2020

Deep pre-training and fine-tuning models (such as BERT and OpenAI GPT) have demonstrated excellent results in question answering areas. However, due to the sheer amount of model parameters, the inference speed of these models is very slow. How to apply these complex models to real business scenarios becomes a challenging but practical problem. Previous model compression methods usually suffer from information loss during the model compression procedure, leading to inferior models compared with the original one. To tackle this challenge, we propose a Two-stage Multi-teacher Knowledge Distillation (TMKD for short) method for web Question Answering system. We first develop a general Q&A distillation task for student model pre-training, and further fine-tune this pre-trained student model with multi-teacher knowledge distillation on downstream tasks (like Web Q&A task, MNLI, SNLI, RTE tasks from GLUE), which effectively reduces the overfitting bias in individual teacher models, and transfers more general knowledge to the student model. The experiment results show that our method can significantly outperform the baseline methods and even achieve comparable results with the original teacher models, along with substantial speedup of model inference.