OpenRaiser/Intern-Atlas huggingface dataset:
Comparisons of sequence labeling algorithms and extensions
Authors: Nam Nguyen 0001, Yunsong Guo
Venue: Conference ICML 2007
Year: 2007

In this paper, we survey the current state-of-art models for structured learning problems, including Hidden Markov Model (HMM), Conditional Random Fields (CRF), Averaged Perceptron (AP), Structured SVMs (SVMstruct), Max Margin Markov Networks (M3N), and an integration of search and learning algorithm (SEARN). With all due tuning efforts of various parameters of each model, on the data sets we have applied the models to, we found that SVMstruct enjoys better performance compared with the others. In addition, we also propose a new method which we call the Structured Learning Ensemble (SLE) to combine these structured learning models. Empirical results show that our SLE algorithm provides more accurate solutions compared with the best results of the individual models.
A Social Welfare Optimal Sequential Allocation Procedure
Authors: Thomas Kalinowski, Nina Narodytska, Toby Walsh
Venue: Conference IJCAI 2013
Year: 2013

We consider a simple sequential allocation procedure for sharing indivisible items between agents in which agents take turns to pick items. Supposing additive utilities and independence between the agents, we show that the expected utility of each agent is computable in polynomial time. Using this result, we prove that the expected utilitarian social welfare is maximized when agents take alternate turns. We also argue that this mechanism remains optimal when agents behave strategically
Data fusion through cross-modality metric learning using similarity-sensitive hashing
Authors: Michael M. Bronstein, Alexander M. Bronstein, Fabrice Michel, Nikos Paragios
Venue: Conference CVPR 2010
Year: 2010

Visual understanding is often based on measuring similarity between observations. Learning similarities specific to a certain perception task from a set of examples has been shown advantageous in various computer vision and pattern recognition problems. In many important applications, the data that one needs to compare come from different representations or modalities, and the similarity between such data operates on objects that may have different and often incommensurable structure and dimensionality. In this paper, we propose a framework for supervised similarity learning based on embedding the input data from two arbitrary spaces into the Hamming space. The mapping is expressed as a binary classification problem with positive and negative examples, and can be efficiently learned using boosting algorithms. The utility and efficiency of such a generic approach is demonstrated on several challenging applications including cross-representation shape retrieval and alignment of multi-modal medical images. 1.
Nonparametric factor analysis with beta process priors
Authors: John W. Paisley, Lawrence Carin
Venue: Conference ICML 2009
Year: 2009

We propose a nonparametric extension to the factor analysis problem using a beta process prior. This beta process factor analysis (BP-FA) model allows for a dataset to be decomposed into a linear combination of a sparse set of factors, providing information on the underlying structure of the observations. As with the Dirichlet process, the beta process is a fully Bayesian conjugate prior, which allows for analytical posterior calculation and straightforward inference. We derive a variational Bayes inference algorithm and demonstrate the model on the MNIST digits and HGDP-CEPH cell line panel datasets. 1.
Connecting comments and tags: improved modeling of social tagging systems
Authors: Dawei Yin, Shengbo Guo, Boris Chidlovskii, Brian D. Davison, Cédric Archambeau, Guillaume Bouchard
Venue: Conference WSDM 2013
Year: 2013

Collaborative tagging systems are now deployed extensively to help users share and organize resources. Tag prediction and recommendation can simplify and streamline the user experience, and by modeling user preferences, predictive accuracy can be significantly improved. However, previous methods typically model user behavior based only on a log of prior tags, neglecting other behaviors and information in social tagging systems, e.g., commenting on items and connecting with other users. On the other hand, little is known about the connection and correlations among these behaviors and contexts in social tagging systems.
Deterministic annealing for semi-supervised kernel machines
Authors: Vikas Sindhwani, S. Sathiya Keerthi, Olivier Chapelle
Venue: Conference ICML 2006
Year: 2006

An intuitive approach to utilizing unlabeled data in kernel-based classification algorithms is to simply treat unknown labels as additional optimization variables. For margin-based loss functions, one can view this approach as attempting to learn low-density separators. However, this is a hard optimization problem to solve in typical semi-supervised settings where unlabeled data is abundant. The popular Transductive SVM algorithm is a label-switching-retraining procedure that is known to be susceptible to local minima. In this paper, we present a global optimization framework for semi-supervised Kernel machines where an easier problem is parametrically deformed to the original hard problem and minimizers are smoothly tracked. Our approach is motivated from deterministic annealing techniques and involves a sequence of convex optimization problems that are exactly and efficiently solved. We present empirical results on several synthetic and real world datasets that demonstrate the effectiveness of our approach.
Feature-based recommendation system
Authors: Eui-Hong Han, George Karypis
Venue: Conference CIKM 2005
Year: 2005

The explosive growth of the world-wide-web and the emergence of e-commerce has led to the development of recommender systems--a personalized information filtering technology used to identify a set of N items that will be of interest to a certain user. User-based and model-based collaborative filtering are the most successful technology for building recommender systems to date and is extensively used in many commercial recommender systems. The basic assumption in these algorithms is that there are sufficient historical data for measuring similarity between products or users. However, this assumption does not hold in various application domains such as electronics retail, home shopping network, on-line retail where new products are introduced and existing products disappear from the catalog. Another such application domains is home improvement retail industry where a lot of products (such as window treatments, bathroom, kitchen or deck) are custom made. Each product is unique and there are very little duplicate products. In this domain, the probability of the same exact two products bought together is close to zero. In this paper, we discuss the challenges of providing recommendation in the domains where no sufficient historical data exist for measuring similarity between products or users. We present feature-based recommendation algorithms that overcome the limitations of the existing top-n recommendation algorithms. The experimental evaluation of the proposed algorithms in the real life data sets shows a great promise. The pilot project deploying the proposed feature-based recommendation algorithms in the on-line retail web site shows 75% increase in the recommendation revenue for the first 2 month period.
Tagommenders: connecting users to items through tags
Authors: Shilad Sen, Jesse Vig, John Riedl
Venue: Conference WWW 2009
Year: 2009

Tagging has emerged as a powerful mechanism that enables users to find, organize, and understand online entities. Recommender systems similarly enable users to efficiently navigate vast collections of items. Algorithms combining tags with recommenders may deliver both the automation inherent in recommenders, and the flexibility and conceptual comprehensibility inherent in tagging systems. In this paper we explore tagommenders, recommender algorithms that predict users' preferences for items based on their inferred preferences for tags. We describe tag preference inference algorithms based on users' interactions with tags and movies, and evaluate these algorithms based on tag preference ratings collected from 995 MovieLens users. We design and evaluate algorithms that predict users' ratings for movies based on their inferred tag preferences. Our tag-based algorithms generate better recommendation rankings than state-of-the-art algorithms, and they may lead to flexible recommender systems that leverage the characteristics of items users find most important.
Dyadic event attribution in social networks with mixtures of hawkes processes
Authors: Liangda Li, Hongyuan Zha
Venue: Conference CIKM 2013
Year: 2013

In many applications in social network analysis, it is important to model the interactions and infer the influence between pairs of actors, leading to the problem of dyadic event modeling which has attracted increasing interests recently. In this paper we focus on the problem of dyadic event attribution, an important missing data problem in dyadic event modeling where one needs to infer the missing actor-pairs of a subset of dyadic events based on their observed timestamps. Existing works either use fixed model parameters and heuristic rules for event attribution, or assume the dyadic events across actor-pairs are independent. To address those shortcomings we propose a probabilistic model based on mixtures of Hawkes processes that simultaneously tackles event attribution and network parameter inference, taking into consideration the dependency among dyadic events that share at least one actor. We also investigate using additive models to incorporate regularization to avoid overfitting. Our experiments on both synthetic and real-world data sets on international armed conflicts suggest that the proposed new method is capable of significantly improve accuracy when compared with the state-of-the-art for dyadic event attribution.
Identifying and labeling search tasks via query-based Hawkes processes
Authors: Liangda Li, Hongbo Deng, Anlei Dong, Yi Chang, Hongyuan Zha
Venue: Conference KDD 2014
Year: 2014

We consider a search task as a set of queries that serve the same user information need. Analyzing search tasks from user query streams plays an important role in building a set of modern tools to improve search engine performance. In this paper, we propose a probabilistic method for identifying and labeling search tasks based on the following intuitive observations: queries that are issued temporally close by users in many sequences of queries are likely to belong to the same search task, meanwhile, different users having the same information needs tend to submit topically coherent search queries. To capture the above intuitions, we directly model query temporal patterns using a special class of point processes called Hawkes processes, and combine topic models with Hawkes processes for simultaneously identifying and labeling search tasks. Essentially, Hawkes processes utilize their self-exciting properties to identify search tasks if influence exists among a sequence of queries for individual users, while the topic model exploits query co-occurrence across different users to discover the latent information needed for labeling search tasks. More importantly, there is mutual reinforcement between Hawkes processes and the topic model in the unified model that enhances the performance of both. We evaluate our method based on both synthetic data and real-world query log data. In addition, we also apply our model to query clustering and search task identification. By comparing with state-of-the-art methods, the results demonstrate that the improvement in our proposed approach is consistent and promising.
Illumination cones for recognition under variable lighting: Faces
Authors: Athinodoros S. Georghiades, David J. Kriegman, Peter N. Belhumeur
Venue: Conference CVPR 1998
Year: 1998

Due to illumination variability, the same object can appear dramatically different even when viewed in fixed pose. To handle this variability, an object recognition system must employ a representation that is either invariant to, or models this variability. This paper presents an appearance-based method for modeling the variability due to illumination in the images of objects. The method differs from past appearance-based methods, however, in that a small set of training images is used to generate a representation-the illumination cone-which models the complete set of images of an object with Lambertian reflectance map under an arbitrary combination of point light sources at infinity. This method is both an implementation and extension (an extension in that it models cast shadows) of the illumination cone representation proposed in Belhumeur and Kriegman (1996). The method is tested on a database of 660 images of 10 faces, and the results exceed those of popular existing methods.
What, where and who? classifying events by scene and object recognition
Authors: Li-Jia Li 0001, Li Fei-Fei 0001
Venue: Conference ICCV 2007
Year: 2007

We propose a first attempt to classify events in static images by integrating scene and object categorizations. We define an event in a static image as a human activity taking place in a specific environment. In this paper, we use a number of sport games such as snow boarding, rock climbing or badminton to demonstrate event classification. Our goal is to classify the event in the image as well as to provide a number of semantic labels to the objects and scene environment within the image. For example, given a rowing scene, our algorithm recognizes the event as rowing by classifying the environment as a lake and recognizing the critical objects in the image as athletes, rowing boat, water, etc. We achieve this integrative and holistic recognition through a generative graphical model. We have assembled a highly challenging database of 8 widely varied sport events. We show that our system is capable of classifying these event classes at 73.4% accuracy. While each component of the model contributes to the final recognition, using scene or objects alone cannot achieve this performance.
Fisher discrimination dictionary learning for sparse representation
Authors: Meng Yang 0001, Lei Zhang 0006, Xiangchu Feng, David Zhang 0001
Venue: Conference ICCV 2011
Year: 2011

Sparse representation based classification has led to interesting image recognition results, while the dictionary used for sparse coding plays a key role in it. This paper presents a novel dictionary learning (DL) method to improve the pattern classification performance. Based on the Fisher discrimination criterion, a structured dictionary, whose dictionary atoms have correspondence to the class labels, is learned so that the reconstruction error after sparse coding can be used for pattern classification. Meanwhile, the Fisher discrimination criterion is imposed on the coding coefficients so that they have small within-class scatter but big between-class scatter. A new classification scheme associated with the proposed Fisher discrimination DL (FDDL) method is then presented by using both the discriminative information in the reconstruction error and sparse coding coefficients. The proposed FDDL is extensively evaluated on benchmark image databases in comparison with existing sparse representation and DL based classification methods.
Probabilistic models for topic learning from images and captions in online biomedical literatures
Authors: Xin Chen, Caimei Lu, Yuan An, Palakorn Achananuparp
Venue: Conference CIKM 2009
Year: 2009

Biomedical images and captions are one of the major sources of information in online biomedical publications. They often contain the most important results to be reported, and provide rich information about the main themes in published papers. In the data mining and information retrieval community, there has been much effort on using text mining and language modeling algorithms to extract knowledge from the text content of online biomedical publications; however, the problem of knowledge extraction from biomedical images and captions has not been fully studied yet. In this paper, a hierarchical probabilistic topic model with background distribution (HPB) is introduced to uncover the latent semantic topics from the co-occurrence patterns of caption words, visual words and biomedical concepts. With downloaded biomedical figures, restricted captions are extracted with regard to each individual image panel. During the indexing stage, the 'bag-of-words' representation of captions is supplemented by an ontology-based concept indexing to alleviate the synonym and polysemy problems. As the visual counterpart of text words, the visual words are extracted and indexed from corresponding image panels. The model is estimated via collapsed Gibbs sampling algorithm. We compare the performance of our model with the extension of the Correspondence LDA (Corr-LDA) model under the same biomedical image annotation scenario using cross-validation. Experimental results demonstrate that our model is able to accurately extract latent patterns from complicated biomedical image-caption pairs and facilitate knowledge organization and understanding in online biomedical literatures.
Translating related words to videos and back through latent topics
Authors: Pradipto Das, Rohini K. Srihari, Jason J. Corso
Venue: Conference WSDM 2013
Year: 2013

Documents containing video and text are becoming more and more widespread and yet content analysis of those documents depends primarily on the text. Although automated discovery of semantically related words from text improves free text query understanding, translating videos into text summaries facilitates better video search particularly in the absence of accompanying text. In this paper, we propose a multimedia topic modeling framework suitable for providing a basis for automatically discovering and translating semantically related words obtained from textual metadata of multimedia documents to semantically related videos or frames from videos.
Action bank: A high-level representation of activity in video
Authors: Sreemanananth Sadanand, Jason J. Corso
Venue: Conference CVPR 2012
Year: 2012

Activity recognition in video is dominated by low- and mid-level features, and while demonstrably capable, by nature, these features carry little semantic meaning. Inspired by the recent object bank approach to image representation, we present Action Bank, a new high-level representation of video. Action bank is comprised of many individual action detectors sampled broadly in semantic space as well as viewpoint space. Our representation is constructed to be semantically rich and even when paired with simple linear SVM classifiers is capable of highly discriminative performance. We have tested action bank on four major activity recognition benchmarks. In all cases, our performance is better than the state of the art, namely 98.2% on KTH (better by 3.3%), 95.0% on UCF Sports (better by 3.7%), 57.9% on UCF50 (baseline is 47.9%), and 26.9% on HMDB51 (baseline is 23.2%). Furthermore, when we analyze the classifiers, we find strong transfer of semantics from the constituent action detectors to the bank classifier.
Rapid object detection using a boosted cascade of simple features
Authors: Paul A. Viola, Michael J. Jones 0001
Venue: Conference CVPR 2001
Year: 2001

This paper describes a machine learning approach for visual object detection which is capable of processing images extremely rapidly and achieving high detection rates. This work is distinguished by three key contributions. The first is the introduction of a new image representation called the "integral image" which allows the features used by our detector to be computed very quickly. The second is a learning algorithm, based on AdaBoost, which selects a small number of critical visual features from a larger set and yields extremely efficient classifiers. The third contribution is a method for combining increasingly more complex classifiers in a "cascade" which allows background regions of the image to be quickly discarded while spending more computation on promising object-like regions. The cascade can be viewed as an object specific focus-of-attention mechanism which unlike previous approaches provides statistical guarantees that discarded regions are unlikely to contain the object of interest. In the domain of face detection the system yields detection rates comparable to the best previous systems. Used in real-time applications, the detector runs at 15 frames per second without resorting to image differencing or skin color detection.
BRISK: binary robust invariant scalable keypoints
Authors: Stefan Leutenegger, Margarita Chli, Roland Siegwart
Venue: Conference ICCV 2011
Year: 2011

Effective and efficient generation of keypoints from an image is a well-studied problem in the literature and forms the basis of numerous Computer Vision applications. Established leaders in the field are the SIFT and SURF algorithms which exhibit great performance under a variety of image transformations, with SURF in particular considered as the most computationally efficient amongst the high-performance methods to date. In this paper we propose BRISK <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , a novel method for keypoint detection, description and matching. A comprehensive evaluation on benchmark datasets reveals BRISK's adaptive, high quality performance as in state-of-the-art algorithms, albeit at a dramatically lower computational cost (an order of magnitude faster than SURF in cases). The key to speed lies in the application of a novel scale-space FAST-based detector in combination with the assembly of a bit-string descriptor from intensity comparisons retrieved by dedicated sampling of each keypoint neighborhood.
PROST: Parallel Robust Online Simple Tracking
Authors: Jakob Santner, Christian Leistner, Amir Saffari, Thomas Pock, Horst Bischof
Venue: Conference CVPR 2010
Year: 2010

Tracking-by-detection is increasingly popular in order to tackle the visual tracking problem. Existing adaptive methods suffer from the drifting problem, since they rely on self-updates of an on-line learning method. In contrast to previous work that tackled this problem by employing semi-supervised or multiple-instance learning, we show that augmenting an on-line learning method with complementary tracking approaches can lead to more stable results. In particular, we use a simple template model as a non-adaptive and thus stable component, a novel optical-flow-based mean-shift tracker as highly adaptive element and an on-line random forest as moderately adaptive appearance-based learner. We combine these three trackers in a cascade. All of our components run on GPUs or similar multi-core systems, which allows for real-time performance. We show the superiority of our system over current state-of-the-art tracking methods in several experiments on publicly available data.
A deep hybrid model for weather forecasting
Authors: Aditya Grover, Ashish Kapoor, Eric Horvitz
Venue: Conference KDD 2015
Year: 2015

Weather forecasting is a canonical predictive challenge that has depended primarily on model-based methods. We explore new directions with forecasting weather as a data-intensive challenge that involves inferences across space and time. We study specifically the power of making predictions via a hybrid approach that combines discriminatively trained predictive models with a deep neural network that models the joint statistics of a set of weather-related variables. We show how the base model can be enhanced with spatial interpolation that uses learned long-range spatial dependencies. We also derive an efficient learning and inference procedure that allows for large scale optimization of the model parameters. We evaluate the methods with experiments on real-world meteorological data that highlight the promise of the approach.