OpenRaiser/Intern-Atlas huggingface dataset:
A benchmark for the comparison of 3-d motion segmentation algorithms
Authors: Roberto Tron, René Vidal
Venue: Conference CVPR 2007
Year: 2007

Over the past few years, several methods for segmenting a scene containing multiple rigidly moving objects have been proposed. However, most existing methods have been tested on a handful of sequences only, and each method has been often tested on a different set of sequences. Therefore, the comparison of different methods has been fairly limited. In this paper, we compare four 3D motion segmentation algorithms for affine cameras on a benchmark of 155 motion sequences of checkerboard, traffic, and articulated scenes.
Modeling multi-way relations with hypergraph embedding
Authors: Chia-An Yu, Ching-Lun Tai, Tak-Shing Chan, Yi-Hsuan Yang
Venue: Conference CIKM 2018
Year: 2018

Hypergraph is a data structure commonly used to represent connections and relations between multiple objects. Embedding a hypergraph into a low-dimensional space and representing each vertex as a vector is useful in various tasks such as visualization, classification, and link prediction. However, most hypergraph embedding or learning algorithms reduce multi-way relations to pairwise ones, which turn hypergraphs into graphs and lose a lot of information. Inspired by Laplacian tensors of uniform hypergraphs, we propose in this paper a novel method that incorporates multi-way relations into an optimization problem. We design an objective that is applicable to both uniform and non-uniform hypergraphs with the constraint of having non-negative embedding vectors. For scalability, we apply negative sampling and use constrained stochastic gradient descent to solve the optimization problem. We test our method in a context-aware recommendation task on a real-world dataset. Experimental results show that our method outperforms a few well-known graph and hypergraph embedding methods.
Predicting completeness in knowledge bases
Authors: Luis Galárraga, Simon Razniewski, Antoine Amarilli, Fabian M. Suchanek
Venue: Conference WSDM 2017
Year: 2017

Knowledge bases such as Wikidata, DBpedia, or YAGO contain millions of entities and facts. In some knowledge bases, the correctness of these facts has been evaluated. However, much less is known about their completeness, i.e., the proportion of real facts that the knowledge bases cover. In this work, we investigate different signals to identify the areas where a knowledge base is complete. We show that we can combine these signals in a rule mining approach, which allows us to predict where facts may be missing. We also show that completeness predictions can help other applications such as fact prediction.
Estimating Rule Quality for Knowledge Base Completion with the Relationship Between Coverage Assumption
Authors: Kaja Zupanc, Jesse Davis
Venue: Conference WWW 2018
Year: 2018

Currently, there are many large, automatically constructed knowledge bases (KBs). One interesting task is learning from a knowledge base to generate new knowledge either in the form of inferred facts or rules that define regularities. One challenge for learning is that KBs are necessarily open world: we cannot assume anything about the truth values of tuples not included in the KB. When a KB only contains facts (i.e., true statements), which is typically the case, we lack negative examples, which are often needed by learning algorithms. To address this problem, we propose a novel score function for evaluating the quality of a first-order rule learned from a KB. Our metric attempts to include information about the tuples not in the KB when evaluating the quality of a potential rule. Empirically, we find that our metric results in more precise predictions than previous approaches.
Bi- LSTM-CRF Sequence Labeling for Keyphrase Extraction from Scholarly Documents
Authors: Rabah A. Al-Zaidy, Cornelia Caragea, C. Lee Giles
Venue: Conference WWW 2019
Year: 2019

In this paper, we address the keyphrase extraction problem as sequence labeling and propose a model that jointly exploits the complementary strengths of Conditional Random Fields that capture label dependencies through a transition parameter matrix consisting of the transition probabilities from one label to the neighboring label, and Bidirectional Long Short Term Memory networks that capture hidden semantics in text through the long distance dependencies. Our results on three datasets of scholarly documents show that the proposed model substantially outperforms strong baselines and previous approaches for keyphrase extraction.
Phrasier: a system for interactive document retrieval using keyphrases
Authors: Steve Jones, Mark S. Staveley
Venue: Conference SIGIR 1999
Year: 1999

Users information needs are often too complex to be effectively expressed in standard query interfaces to full-text retrieval systems. A typical need is to find documents that are similar to a given source document, yet describing the content of a document in a few terms is a difficult task. We describe Phrasier, an interactive system for browsing, querying and relating documents within a digital library. Phrasier exploits keyphrases that have been automatically extracted from source documents to create links to similar documents and to suggest appropriate query phrases to users. Phrasiers keyphrase-based retrieval engine returns ranked lists of documents that are similar to a given source text. Evaluation indicates that Phrasiers keyphrase-based retrieval performs as well as full-text retrieval when recall and relevance scores assigned by human assessors are considered.
Deep neural matching models for graph retrieval
Authors: Kunal Goyal, Utkarsh Gupta, Abir De, Soumen Chakrabarti
Venue: Conference SIGIR 2020
Year: 2020

Graph retrieval from a large corpus of graphs has a wide variety of applications, e.g., sentence retrieval using words and dependency parse trees for question answering, image retrieval using scene graphs, and molecule discovery from a set of existing molecular graphs. In such graph search applications, nodes, edges and associated features bear distinctive physical significance. Therefore, a unified, trainable search model that efficiently returns corpus graphs that are highly relevant to a query graph has immense potential impact. In this paper, we present an effective, feature and structure-aware, end-to-end trainable neural match scoring system for graphs. We achieve this by constructing the product graph between the query and a candidate graph in the corpus, and then conduct a family of random walks on the product graph, which are then aggregated into the match score, using a network whose parameters can be trained. Experiments show the efficacy of our method, compared to competitive baseline approaches.
A spectral technique for correspondence problems using pairwise constraints
Authors: Marius Leordeanu, Martial Hebert
Venue: Conference ICCV 2005
Year: 2005

We present an efficient spectral method for finding consistent correspondences between two sets of features. We build the adjacency matrix M of a graph whose nodes represent the potential correspondences and the weights on the links represent pairwise agreements between potential correspondences. Correct assignments are likely to establish links among each other and thus form a strongly connected cluster. Incorrect correspondences establish links with the other correspondences only accidentally, so they are unlikely to belong to strongly connected clusters. We recover the correct assignments based on how strongly they belong to the main cluster of M, by using the principal eigenvector of M and imposing the mapping constraints required by the overall correspondence mapping (one-to-one or one-to-many). The experimental evaluation shows that our method is robust to outliers, accurate in terms of matching rate, while being much faster than existing methods
Optuna: A Next-generation Hyperparameter Optimization Framework
Authors: Takuya Akiba, Shotaro Sano, Toshihiko Yanase, Takeru Ohta, Masanori Koyama
Venue: Conference KDD 2019
Year: 2019

The purpose of this study is to introduce new design-criteria for next-generation hyperparameter optimization software. The criteria we propose include (1) define-by-run API that allows users to construct the parameter search space dynamically, (2) efficient implementation of both searching and pruning strategies, and (3) easy-to-setup, versatile architecture that can be deployed for various purposes, ranging from scalable distributed computing to light-weight experiment conducted via interactive interface. In order to prove our point, we will introduce Optuna, an optimization software which is a culmination of our effort in the development of a next generation optimization software. As an optimization software designed with define-by-run principle, Optuna is particularly the first of its kind. We will present the design-techniques that became necessary in the development of the software that meets the above criteria, and demonstrate the power of our new design through experimental results and real world applications. Our software is available under the MIT license (https://github.com/pfnet/optuna/).
LayoutLM: Pre-training of Text and Layout for Document Image Understanding
Authors: Yiheng Xu, Minghao Li 0004, Lei Cui 0001, Shaohan Huang, Furu Wei, Ming Zhou 0001
Venue: Conference KDD 2020
Year: 2020

Pre-training techniques have been verified successfully in a variety of NLP tasks in recent years. Despite the widespread use of pre-training models for NLP applications, they almost exclusively focus on text-level manipulation, while neglecting layout and style information that is vital for document image understanding. In this paper, we propose the \textbf{LayoutLM} to jointly model interactions between text and layout information across scanned document images, which is beneficial for a great number of real-world document image understanding tasks such as information extraction from scanned documents. Furthermore, we also leverage image features to incorporate words' visual information into LayoutLM. To the best of our knowledge, this is the first time that text and layout are jointly learned in a single framework for document-level pre-training. It achieves new state-of-the-art results in several downstream tasks, including form understanding (from 70.72 to 79.27), receipt understanding (from 94.02 to 95.24) and document image classification (from 93.07 to 94.42). The code and pre-trained LayoutLM models are publicly available at \url{https://aka.ms/layoutlm}.
Automated hypothesis generation based on mining scientific literature
Authors: W. Scott Spangler, Angela D. Wilkins, Benjamin J. Bachman, Meena Nagarajan, Tajhal Dayaram, Peter J. Haas, Sam Regenbogen, Curtis R. Pickering, Austin Comer, Jeffrey N. Myers, Ioana Stanoi, Linda Kato, Ana Lelescu, Jacques J. Labrie, Neha Parikh, Andreas Martin Lisewski, Lawrence A. Donehower, Ying Chen, Olivier Lichtarge
Venue: Conference KDD 2014
Year: 2014

Keeping up with the ever-expanding flow of data and publications is untenable and poses a fundamental bottleneck to scientific progress. Current search technologies typically find many relevant documents, but they do not extract and organize the information content of these documents or suggest new scientific hypotheses based on this organized content. We present an initial case study on KnIT, a prototype system that mines the information contained in the scientific literature, represents it explicitly in a queriable network, and then further reasons upon these data to generate novel and experimentally testable hypotheses. KnIT combines entity detection with neighbor-text feature analysis and with graph-based diffusion of information to identify potential new properties of entities that are strongly implied by existing relationships. We discuss a successful application of our approach that mines the published literature to identify new protein kinases that phosphorylate the protein tumor suppressor p53. Retrospective analysis demonstrates the accuracy of this approach and ongoing laboratory experiments suggest that kinases identified by our system may indeed phosphorylate p53. These results establish proof of principle for automated hypothesis generation and discovery based on text mining of the scientific literature.
Growing story forest online from massive breaking news
Authors: Bang Liu, Di Niu, Kunfeng Lai, Linglong Kong, Yu Xu
Venue: Conference CIKM 2017
Year: 2017

We describe our experience of implementing a news content organization system at Tencent that discovers events from vast streams of breaking news and evolves news story structures in an online fashion. Our real-world system has distinct requirements in contrast to previous studies on topic detection and tracking (TDT) and event timeline or graph generation, in that we 1) need to accurately and quickly extract distinguishable events from massive streams of long text documents that cover diverse topics and contain highly redundant information, and 2) must develop the structures of event stories in an online manner, without repeatedly restructuring previously formed stories, in order to guarantee a consistent user viewing experience. In solving these challenges, we propose Story Forest, a set of online schemes that automatically clusters streaming documents into events, while connecting related events in growing trees to tell evolving stories. We conducted extensive evaluation based on 60 GB of real-world Chinese news data, although our ideas are not language-dependent and can easily be extended to other languages, through detailed pilot user experience studies. The results demonstrate the superior capability of Story Forest to accurately identify events and organize news text into a logical structure that is appealing to human readers, compared to multiple existing algorithm frameworks.
Augmix: A simple data processing method to improve robustness and uncertainty
Authors: Dan Hendrycks, Norman Mu, Ekin Dogus Cubuk, Barret Zoph, Justin Gilmer, Balaji Lakshminarayanan
Venue: Conference ICLR 2020
Year: 2020

Modern deep neural networks can achieve high accuracy when the training distribution and test distribution are identically distributed, but this assumption is frequently violated in practice. When the train and test distributions are mismatched, accuracy can plummet. Currently there are few techniques that improve robustness to unforeseen data shifts encountered during deployment. In this work, we propose a technique to improve the robustness and uncertainty estimates of image classifiers. We propose AugMix, a data processing technique that is simple to implement, adds limited computational overhead, and helps models withstand unforeseen corruptions. AugMix significantly improves robustness and uncertainty measures on challenging image classification benchmarks, closing the gap between previous methods and the best possible performance in some cases by more than half.
Demystifying mmd gans
Authors: Mikolaj Binkowski, Danica J. Sutherland, Michael Arbel, Arthur Gretton
Venue: Conference ICLR 2018
Year: 2018

We investigate the training and performance of generative adversarial networks using the Maximum Mean Discrepancy (MMD) as critic, termed MMD GANs. As our main theoretical contribution, we clarify the situation with bias in GAN loss functions raised by recent work: we show that gradient estimators used in the optimization process for both MMD GANs and Wasserstein GANs are unbiased, but learning a discriminator based on samples leads to biased gradients for the generator parameters. We also discuss the issue of kernel choice for the MMD critic, and characterize the kernel corresponding to the energy distance used for the Cramer GAN critic. Being an integral probability metric, the MMD benefits from training strategies recently developed for Wasserstein GANs. In experiments, the MMD GAN is able to employ a smaller critic network than the Wasserstein GAN, resulting in a simpler and faster-training algorithm with matching performance. We also propose an improved measure of GAN convergence, the Kernel Inception Distance, and show how to use it to dynamically adapt learning rates during GAN training.
Mining Twitter for Fine-Grained Political Opinion Polarity Classification, Ideology Detection and Sarcasm Detection
Authors: Sandeepa Kannangara
Venue: Conference WSDM 2018
Year: 2018

In this paper, we propose three models for socio-political opinion polarity classification of microblog posts. Firstly, a novel probabilistic model, Joint-Entity-Sentiment-Topic (JEST) model, which captures opinions as a combination of the target entity, sentiment and topic, will be proposed. Secondly, a model for ideology detection called JEST-Ideology will be proposed to identify an individual»s orientation towards topics/issues and target entities by extending the proposed opinion polarity classification framework. Finally, we propose a novel method to accurately detect sarcastic opinions by utilizing detected fine-grained opinion and ideology.
Local color transfer via probabilistic segmentation by expectation-maximization
Authors: Yu-Wing Tai, Jiaya Jia, Chi-Keung Tang
Venue: Conference CVPR 2005
Year: 2005

We address the problem of regional color transfer between two natural images by probabilistic segmentation. We use a new expectation-maximization (EM) scheme to impose both spatial and color smoothness to infer natural connectivity among pixels. Unlike previous work, our method takes local color information into consideration, and segment image with soft region boundaries for seamless color transfer and compositing. Our modified EM method has two advantages in color manipulation: first, subject to different levels of color smoothness in image space, our algorithm produces an optimal number of regions upon convergence, where the color statistics in each region can be adequately characterized by a component of a Gaussian mixture model (GMM). Second, we allow a pixel to fall in several regions according to our estimated probability distribution in the EM step, resulting in a transparency-like ratio for compositing different regions seamlessly. Hence, natural color transition across regions can be achieved, where the necessary intra-region and inter-region smoothness are enforced without losing original details. We demonstrate results on a variety of applications including image deblurring, enhanced color transfer, and colorizing gray scale images. Comparisons with previous methods are also presented.
Context-aware citation recommendation
Authors: Qi He 0002, Jian Pei 0001, Daniel Kifer, Prasenjit Mitra, C. Lee Giles
Venue: Conference WWW 2010
Year: 2010

When you write papers, how many times do you want to make some citations at a place but you are not sure which papers to cite? Do you wish to have a recommendation system which can recommend a small number of good candidates for every place that you want to make some citations? In this paper, we present our initiative of building a context-aware citation recommendation system. High quality citation recommendation is challenging: not only should the citations recommended be relevant to the paper under composition, but also should match the local contexts of the places citations are made. Moreover, it is far from trivial to model how the topic of the whole paper and the contexts of the citation places should affect the selection and ranking of citations. To tackle the problem, we develop a context-aware approach. The core idea is to design a novel non-parametric probabilistic model which can measure the context-based relevance between a citation context and a document. Our approach can recommend citations for a context effectively. Moreover, it can recommend a set of citations for a paper with high quality. We implement a prototype system in CiteSeerX. An extensive empirical evaluation in the CiteSeerX digital library against many baselines demonstrates the effectiveness and the scalability of our approach.
Recommending citations: translating papers into references
Authors: Wenyi Huang, Saurabh Kataria, Cornelia Caragea, Prasenjit Mitra, C. Lee Giles, Lior Rokach
Venue: Conference CIKM 2012
Year: 2012

When we write or prepare to write a research paper, we always have appropriate references in mind. However, there are most likely references we have missed and should have been read and cited. As such a good citation recommendation system would not only improve our paper but, overall, the efficiency and quality of literature search.
AutoCite: Multi-modal representation fusion for contextual citation generation
Authors: Qingqin Wang, Yun Xiong, Yao Zhang, Jiawei Zhang, Yangyong Zhu
Venue: Conference WSDM 2021
Year: 2021

Citing comprehensive and correct related work is crucial in academic writing. It can not only support the author's claims but also help readers trace other related research papers. Nowadays, with the rapid increase in the number of scientific literatures, it has become increasingly challenging to search for high-quality citations and write the manuscript. In this paper, we present an automatic writing assistant model, AutoCite, which not only infers potentially related work but also automatically generates the citation context at the same time. Specifically, AutoCite involves a novel multi-modal encoder and a multi-task decoder architecture. Based on the multi-modal inputs, the encoder in AutoCite learns paper representations with both citation network structure and textual contexts. The multi-task decoder in AutoCite couples and jointly learns citation prediction and context generation in a unified manner. To effectively join the encoder and decoder, we introduce a novel representation fusion component, i.e., gated neural fusion, which feeds the multi-modal representation inputs from the encoder and creates outputs for the downstream multi-task decoder adaptively. Extensive experiments on five real-world citation network datasets validate the effectiveness of our model.
Scaling graph neural networks with approximate pagerank
Authors: Aleksandar Bojchevski, Johannes Klicpera, Bryan Perozzi, Amol Kapoor, Martin Blais, Benedek Rózemberczki, Michal Lukasik, Stephan Günnemann
Venue: Conference KDD 2020
Year: 2020

Graph neural networks (GNNs) have emerged as a powerful approach for solving many network mining tasks. However, learning on large graphs remains a challenge -- many recently proposed scalable GNN approaches rely on an expensive message-passing procedure to propagate information through the graph. We present the PPRGo model which utilizes an efficient approximation of information diffusion in GNNs resulting in significant speed gains while maintaining state-of-the-art prediction performance. In addition to being faster, PPRGo is inherently scalable, and can be trivially parallelized for large datasets like those found in industry settings.