OpenRaiser/Intern-Atlas huggingface dataset:
Multi-label learning with incomplete class assignments
Authors: Serhat Selcuk Bucak, Rong Jin 0001, Anil K. Jain 0001
Venue: Conference CVPR 2011
Year: 2011

We consider a special type of multi-label learning where class assignments of training examples are incomplete. As an example, an instance whose true class assignment is (c <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> , c <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , c <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> ) is only assigned to class c <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> when it is used as a training sample. We refer to this problem as multi-label learning with incomplete class assignment. Incompletely labeled data is frequently encountered when the number of classes is very large (hundreds as in MIR Flickr dataset) or when there is a large ambiguity between classes (e.g., jet vs plane). In both cases, it is difficult for users to provide complete class assignments for objects. We propose a ranking based multi-label learning framework that explicitly addresses the challenge of learning from incompletely labeled data by exploiting the group lasso technique to combine the ranking errors. We present a learning algorithm that is empirically shown to be efficient for solving the related optimization problem. Our empirical study shows that the proposed framework is more effective than the state-of-the-art algorithms for multi-label learning in dealing with incompletely labeled data.
TagProp: Discriminative metric learning in nearest neighbor models for image auto-annotation
Authors: Matthieu Guillaumin, Thomas Mensink, Jakob Verbeek, Cordelia Schmid
Venue: Conference ICCV 2009
Year: 2009

Image auto-annotation is an important open problem in computer vision. For this task we propose TagProp, a discriminatively trained nearest neighbor model. Tags of test images are predicted using a weighted nearest-neighbor model to exploit labeled training images. Neighbor weights are based on neighbor rank or distance. TagProp allows the integration of metric learning by directly maximizing the log-likelihood of the tag predictions in the training set. In this manner, we can optimally combine a collection of image similarity metrics that cover different aspects of image content, such as local shape descriptors, or global color histograms. We also introduce a word specific sigmoidal modulation of the weighted neighbor tag predictions to boost the recall of rare words. We investigate the performance of different variants of our model and compare to existing work. We present experimental results for three challenging data sets. On all three, TagProp makes a marked improvement as compared to the current state-of-the-art.
Using fast weights to improve persistent contrastive divergence
Authors: Tijmen Tieleman, Geoffrey E. Hinton
Venue: Conference ICML 2009
Year: 2009

The most commonly used learning algorithm for restricted Boltzmann machines is contrastive divergence which starts a Markov chain at a data point and runs the chain for only a few iterations to get a cheap, low variance estimate of the sufficient statistics under the model. Tieleman (2008) showed that better learning can be achieved by estimating the model's statistics using a small set of persistent "fantasy particles" that are not reinitialized to data points after each weight update. With sufficiently small weight updates, the fantasy particles represent the equilibrium distribution accurately but to explain why the method works with much larger weight updates it is necessary to consider the interaction between the weight updates and the Markov chain. We show that the weight updates force the Markov chain to mix fast, and using this insight we develop an even faster mixing chain that uses an auxiliary set of "fast weights" to implement a temporary overlay on the energy landscape. The fast weights learn rapidly but also decay rapidly and do not contribute to the normal energy landscape that defines the model.
Self-taught learning: transfer learning from unlabeled data
Authors: Rajat Raina, Alexis J. Battle, Honglak Lee, Benjamin Packer, Andrew Y. Ng
Venue: Conference ICML 2007
Year: 2007

We present a new machine learning framework called "self-taught learning" for using unlabeled data in supervised classification tasks. We do not assume that the unlabeled data follows the same class labels or generative distribution as the labeled data. Thus, we would like to use a large number of unlabeled images (or audio samples, or text documents) randomly downloaded from the Internet to improve performance on a given image (or audio, or text) classification task. Such unlabeled data is significantly easier to obtain than in typical semi-supervised or transfer learning settings, making self-taught learning widely applicable to many practical learning problems. We describe an approach to self-taught learning that uses sparse coding to construct higher-level features using the unlabeled data. These features form a succinct input representation and significantly improve classification performance. When using an SVM for classification, we further show how a Fisher kernel can be learned for this representation.
Bellman goes relational
Authors: Kristian Kersting, Martijn van Otterlo, Luc De Raedt
Venue: Conference ICML 2004
Year: 2004

Motivated by the interest in relational reinforcement learning, we introduce a novel relational Bellman update operator called ReBel. It employs a constraint logic programming language to compactly represent Markov decision processes over relational domains. Using ReBel, a novel value iteration algorithm is developed in which abstraction (over states and actions) plays a major role. This framework provides new insights into relational reinforcement learning. Convergence results as well as experiments are presented. 1.
Methods for mining and summarizing text conversations
Authors: Giuseppe Carenini, Gabriel Murray
Venue: Conference SIGIR 2012
Year: 2012

More and more today, people are engaging in conversations via email, blogs, discussion forums, text messaging and other social media. A person may want to archive these conversations and later retrieve information about what was discussed, or analyze a conversation in real-time. What topics are covered in these conversations? What opinions are people expressing? Have any decisions been made? Have action items been assigned? This tutorial will present various natural language processing (NLP) techniques that can help answer these questions, thus creating numerous new and valuable applications that can support people in more effectively participating in these conversation. The tutorial is based on a book that we have recently published, Methods for Mining and Summarizing Text Conversations.
Towards query log based personalization using topic models
Authors: Mark James Carman, Fabio Crestani, Morgan Harvey, Mark Baillie
Venue: Conference CIKM 2010
Year: 2010

We investigate the utility of topic models for the task of personalizing search results based on information present in a large query log. We define generative models that take both the user and the clicked document into account when estimating the probability of query terms. These models can then be used to rank documents by their likelihood given a particular query and user pair.
Active objects: actions for entity-centric search
Authors: Thomas Lin, Patrick Pantel, Michael Gamon, Anitha Kannan, Ariel Fuxman
Venue: Conference WWW 2012
Year: 2012

We introduce an entity-centric search experience, called Active Objects, in which entity-bearing queries are paired with actions that can be performed on the entities. For example, given a query for a specific flashlight, we aim to present actions such as reading reviews, watching demo videos, and finding the best price online. In an annotation study conducted over a random sample of user query sessions, we found that a large proportion of queries in query logs involve actions on entities, calling for an automatic approach to identifying relevant actions for entity-bearing queries. In this paper, we pose the problem of finding actions that can be performed on entities as the problem of probabilistic inference in a graphical model that captures how an entity bearing query is generated. We design models of increasing complexity that capture latent factors such as entity type and intended actions that determine how a user writes a query in a search box, and the URL that they click on. Given a large collection of real-world queries and clicks from a commercial search engine, the models are learned efficiently through maximum likelihood estimation using an EM algorithm. Given a new query, probabilistic inference enables recommendation of a set of pertinent actions and hosts. We propose an evaluation methodology for measuring the relevance of our recommended actions, and show empirical evidence of the quality and the diversity of the discovered actions.
Building implicit links from content for forum search
Authors: Gu Xu, Wei-Ying Ma
Venue: Conference SIGIR 2006
Year: 2006

The objective of Web forums is to create a shared space for open communications and discussions of specific topics and issues. The tremendous information behind forum sites is not fully-utilized yet. Most links between forum pages are automatically created, which means the link-based ranking algorithm cannot be applied efficiently. In this paper, we proposed a novel ranking algorithm which tries to introduce the content information into link-based methods as implicit links. The basic idea is derived from the more focused random surfer: the surfer may more likely jump to a page which is similar to what he is reading currently. In this manner, we are allowed to introduce the content similarities into the link graph as a personalization bias. Our method, named Fine-grained Rank (FGRank), can be efficiently computed based on an automatically generated topic hierarchy. Not like the topic-sensitive PageRank, our method only need to compute single PageRank score for each page. Another contribution of this paper is to present a very efficient algorithm for automatically generating topic hierarchy and map each page in a large-scale collection onto the computed hierarchy. The experimental results show that the proposed method can improve retrieval performance, and reveal that content-based link graph is also important compared with the hyper-link graph.
Debugging owl ontologies
Authors: Bijan Parsia, Evren Sirin, Aditya Kalyanpur
Venue: Conference WWW 2005
Year: 2005

As an increasingly large number of OWL ontologies become available on the Semantic Web and the descriptions in the ontologies become more complicated, finding the cause of errors becomes an extremely hard task even for experts. Existing ontology development environments provide some limited support, in conjunction with a reasoner, for detecting and diagnosing errors in OWL ontologies. Typically these are restricted to the mere detection of, for example, unsatisfiable concepts. We have integrated a number of simple debugging cues generated from our description logic reasoner, Pellet, in our hypertextual ontology development environment, Swoop. These cues, in conjunction with extensive undo/redo and Annotea based collaboration support in Swoop, significantly improve the OWL debugging experience, and point the way to more general improvements in the presentation of an ontology to new users.
Truthful incentives in crowdsourcing tasks using regret minimization mechanisms
Authors: Adish Singla, Andreas Krause
Venue: Conference WWW 2013
Year: 2013

What price should be offered to a worker for a task in an online labor market? How can one enable workers to express the amount they desire to receive for the task completion? Designing optimal pricing policies and determining the right monetary incentives is central to maximizing requester's utility and workers' profits. Yet, current crowdsourcing platforms only offer a limited capability to the requester in designing the pricing policies and often rules of thumb are used to price tasks. This limitation could result in inefficient use of the requester's budget or workers becoming disinterested in the task.
Inferring and using location metadata to personalize web search
Authors: Paul N. Bennett, Filip Radlinski, Ryen W. White, Emine Yilmaz
Venue: Conference SIGIR 2011
Year: 2011

Personalization of search results offers the potential for significant improvements in Web search. Among the many observable user attributes, approximate user location is particularly simple for search engines to obtain and allows personalization even for a first-time Web search user. However, acting on user location information is difficult, since few Web documents include an address that can be interpreted as constraining the locations where the document is relevant. Furthermore, many Web documents -- such as local news stories, lottery results, and sports team fan pages -- may not correspond to physical addresses, but the location of the user still plays an important role in document relevance. In this paper, we show how to infer a more general location relevance which uses not only physical location but a more general notion of locations of interest for Web pages. We compute this information using implicit user behavioral data, characterize the most location-centric pages, and show how location information can be incorporated into Web search ranking. Our results show that a substantial fraction of Web search queries can be significantly improved by incorporating location-based features.
Regularized locality preserving indexing via spectral regression
Authors: Deng Cai, Xiaofei He, Wei Vivian Zhang, Jiawei Han
Venue: Conference CIKM 2007
Year: 2007

We consider the problem of document indexing and representation. Recently, Locality Preserving Indexing (LPI) was proposed for learning a compact document subspace. Different from Latent Semantic Indexing (LSI) which is optimal in the sense of global Euclidean structure, LPI is optimal in the sense of local manifold structure. However, LPI is not efficient in time and memory which makes it difficult to be applied to very large data set. Specifically, the computation of LPI involves eigen-decompositions of two dense matrices which is expensive. In this paper, we propose a new algorithm called Regularized Locality Preserving Indexing (RLPI). Benefit from recent progresses on spectral graph analysis, we cast the original LPI algorithm into a regression framework which enable us to avoid eigen-decomposition of dense matrices. Also, with the regression based framework, different kinds of regularizers can be naturally incorporated into our algorithm which makes it more flexible. Extensive experimental results show that RLPI obtains similar or better results comparing to LPI and it is significantly faster, which makes it an efficient and effective data preprocessing method for large scale text clustering, classification and retrieval.
What you saw is not what you get: Domain adaptation using asymmetric kernel transforms
Authors: Brian Kulis, Kate Saenko, Trevor Darrell
Venue: Conference CVPR 2011
Year: 2011

In real-world applications, “what you saw” during training is often not “what you get” during deployment: the distribution and even the type and dimensionality of features can change from one dataset to the next. In this paper, we address the problem of visual domain adaptation for transferring object models from one dataset or visual domain to another. We introduce ARC-t, a flexible model for supervised learning of non-linear transformations between domains. Our method is based on a novel theoretical result demonstrating that such transformations can be learned in kernel space. Unlike existing work, our model is not restricted to symmetric transformations, nor to features of the same type and dimensionality, making it applicable to a significantly wider set of adaptation scenarios than previous methods. Furthermore, the method can be applied to categories that were not available during training. We demonstrate the ability of our method to adapt object recognition models under a variety of situations, such as differing imaging conditions, feature types and codebooks.
Finding high-quality content in social media
Authors: Eugene Agichtein, Carlos Castillo, Debora Donato, Aristides Gionis, Gilad Mishne
Venue: Conference WSDM 2008
Year: 2008

The quality of user-generated content varies drastically from excellent to abuse and spam. As the availability of such content increases, the task of identifying high-quality content sites based on user contributions --social media sites -- becomes increasingly important. Social media in general exhibit a rich variety of information sources: in addition to the content itself, there is a wide array of non-content information available, such as links between items and explicit quality ratings from members of the community. In this paper we investigate methods for exploiting such community feedback to automatically identify high quality content. As a test case, we focus on Yahoo! Answers, a large community question/answering portal that is particularly rich in the amount and types of content and social interactions available in it. We introduce a general classification framework for combining the evidence from different sources of information, that can be tuned automatically for a given social media type and quality definition. In particular, for the community question/answering domain, we show that our system is able to separate high-quality items from the rest with an accuracy close to that of humans
How opinions are received by online communities: A case study on Amazon.com helpfulness votes
Authors: Cristian Danescu-Niculescu-Mizil, Gueorgi Kossinets, Jon M. Kleinberg, Lillian Lee
Venue: Conference WWW 2009
Year: 2009

There are many on-line settings in which users publicly express opinions. A number of these offer mechanisms for other users to evaluate these opinions; a canonical example is Amazon.com, where reviews come with annotations like "26 of 32 people found the following review helpful." Opinion evaluation appears in many off-line settings as well, including market research and political campaigns. Reasoning about the evaluation of an opinion is fundamentally different from reasoning about the opinion itself: rather than asking, "What did Y think of X?", we are asking, "What did Z think of Y's opinion of X?" Here we develop a framework for analyzing and modeling opinion evaluation, using a large-scale collection of Amazon book reviews as a dataset. We find that the perceived helpfulness of a review depends not just on its content but also but also in subtle ways on how the expressed evaluation relates to other evaluations of the same product. As part of our approach, we develop novel methods that take advantage of the phenomenon of review "plagiarism" to control for the effects of text in opinion evaluation, and we provide a simple and natural mathematical model consistent with our findings. Our analysis also allows us to distinguish among the predictions of competing theories from sociology and social psychology, and to discover unexpected differences in the collective opinion-evaluation behavior of user populations from different countries.
Cross-generalization: Learning novel classes from a single example by feature replacement
Authors: Evgeniy Bart, Shimon Ullman
Venue: Conference CVPR 2005
Year: 2005

We develop an object classification method that can learn a novel class from a single training example. In this method, experience with already learned classes is used to facilitate the learning of novel classes. Our classification scheme employs features that discriminate between class and non-class images. For a novel class, new features are derived by selecting features that proved useful for already learned classification tasks, and adapting these features to the new classification task. This adaptation is performed by replacing the features from already learned classes with similar features taken from the novel class. A single example of a novel class is sufficient to perform feature adaptation and achieve useful classification performance. Experiments demonstrate that the proposed algorithm can learn a novel class from a single training example, using 10 additional familiar classes. The performance is significantly improved compared to using no feature adaptation. The robustness of the proposed feature adaptation concept is demonstrated by similar performance gains across 107 widely varying object categories.
Sharing features between objects and their attributes
Authors: Sung Ju Hwang, Fei Sha, Kristen Grauman
Venue: Conference CVPR 2011
Year: 2011

Visual attributes expose human-defined semantics to object recognition models, but existing work largely restricts their influence to mid-level cues during classifier training. Rather than treat attributes as intermediate features, we consider how learning visual properties in concert with object categories can regularize the models for both. Given a low-level visual feature space together with attribute-and object-labeled image data, we learn a shared lower-dimensional representation by optimizing a joint loss function that favors common sparsity patterns across both types of prediction tasks. We adopt a recent kernelized formulation of convex multi-task feature learning, in which one alternates between learning the common features and learning task-specific classifier parameters on top of those features. In this way, our approach discovers any structure among the image descriptors that is relevant to both tasks, and allows the top-down semantics to restrict the hypothesis space of the ultimate object classifiers. We validate the approach on datasets of animals and outdoor scenes, and show significant improvements over traditional multi-class object classifiers and direct attribute prediction models.
Learning from one example through shared densities on transforms
Authors: Erik G. Miller, Nicholas E. Matsakis, Paul A. Viola
Venue: Conference CVPR 2000
Year: 2000

We define a process called congealing in which elements of a dataset (images) are brought into correspondence with each other jointly, producing a data-defined model. It is based upon minimizing the summed component-wise (pixel-wise) entropies over a continuous set of transforms on the data. One of the biproducts of this minimization is a set of transform, one associated with each original training sample. We then demonstrate a procedure for effectively bringing test data into correspondence with the data-defined model produced in the congealing process. Subsequently; we develop a probability density over the set of transforms that arose from the congealing process. We suggest that this density over transforms may be shared by many classes, and demonstrate how using this density as "prior knowledge" can be used to develop a classifier based on only a single training example for each class.