nils-herrmann/clean_scirepeval huggingface dataset:
An image database browser that learns from user interaction

Digital libraries of images and video are rapidly growing in size and availability. To avoid the expense and limitations of text, there is considerable interest in navigation by perceptual and other automatically extractable attributes. Unfortunately, the relevance of an attribute for a query is not always obvious. Queries which go beyond explicit color, shape, and positional cues must incorporate multiple features in complex ways. This dissertation uses machine learning to automatically select and combine features to satisfy a query, based on positive and negative examples from the user. The learning algorithm does not just learn during the course of one session: it learns continuously, across sessions. The learner improves its learning ability by dynamically modifying its inductive bias, based on experience over multiple sessions. Experiments demonstrate the ability to assist image classiication, segmentation, and annotation (labeling of image regions). The common theme of this work, applied to computer vision, database retrieval, and machine learning, is building in enough exibility to allow adaptation to changing goals.
Modeling user subjectivity in image libraries

In addition to the problem of which image analysis models to use in digital libraries, e.g. wavelet, Wold, color histograms, is the problem of how to combine these models with their different strengths. Most present systems place the burden of combination on the user, e.g. the user specifies 50% texture features, 20% color features, etc. This is a problem since most users do not know how to best pick the settings for the given data and search problem. The paper addresses this problem, describing research in progress for a system that: (1) automatically infers which combination of models best represents the data of interest to the user; and (2) learns continuously during interaction with each user. In particular, these two components-inference and learning-provide a solution that adapts to the subjective and hard to predict behaviors frequently seen when people query or browse image libraries.
Interactive learning with a "Society of Models"

Digital library access is driven by features, but the relevance of a feature for a query is not always obvious. This paper describes an approach for integrating a large number of context-dependent features into a semi-automated tool. Instead of requiring universal similarity measures or manual selection of relevant features, the approach provides a learning algorithm for selecting and combining groupings of the data, where groupings can be induced by highly specialized features. The selection process is guided by positive and negative examples from the user. The inherent combinatorics of using multiple features is reduced by a multistage grouping generation, weighting, and collection process. The stages closest to the user are trained fastest and slowly propagate their adaptations back to earlier stages. The weighting stage adapts the collection stage's search space across uses, so that, in later interactions, good groupings are found given few examples from the user.
Submitted to Special Issue of Pattern Recognition on Image Database: Classiication and Retrieval Interactive Learning Using a \society of Models"

Digital library access is driven by features, but features are often context-dependent and noisy, and their relevance for a query is not always obvious. This paper describes an approach for utilizing many data-dependent, user-dependent, and task-dependent features in a semi-automated tool. Instead of requiring universal similarity measures or manual selection of relevant features, the approach provides a learning algorithm for selecting and combining groupings of the data, where groupings can be induced by highly specialized and context-dependent features. The selection process is guided by a rich example-based interaction with the user. The inherent com-binatorics of using multiple features is reduced by a multistage grouping generation, weighting, and collection process. The stages closest to the user are trained fastest and slowly propagate their adaptations back to earlier stages. The weighting stage adapts the collection stage's search space across uses, so that, in later interactions, good groupings are found given few examples from the user. Described is an interactive-time implementation of this architecture for semi-automatic within-image segmentation and across-image labeling , driven by concurrently active color models , texture models, or manually-provided groupings. 1 Issues for digital libraries Digital libraries of images, video, and sound are a rich area for pattern recognition research. They also introduce a host of new problems and requirements, since the range of possible queries is immense and requires the utilization of many specialized features. Also, systems for retrieval, browsing, and annotation, i.e. classifying regions, often must perform with only a small number of examples from a user, i.e. an insuf-cient amount of training data by traditional requirements. Thus the area is doubly exciting since it presents the eld of pattern recognition with new challenges while beckoning in new applications. One important issue for digital libraries is nding good models and similarity measures for comparing database entries. A part of this diiculty is that feature extraction and comparison methods are highly data-dependent; see Figure 2 for an example with texture. Similarity measures are also user and task dependent, as demonstrated by Figure 3. Unfortunately , these dependencies are not, at this point, understood well enough, especially by the typical digital library user, to permit careful selection of the optimal measure beforehand. Note that the multi-resolution simultaneous auto-regressive (MRSAR) model of 1], which fares poorly compared to the shift-invariant eigenvector (EV) model in the above two examples, scores clearly above the EV model on the standard Brodatz database 2] …
Multimedia Systems: Special Issue on Content-based Retrieval Vision Texture for Annotation

This paper demonstrates a new application of computer vision to digital libraries { the use of texture for annotation, the description of content. Vision-based annotation assists the user in attaching descriptions to large sets of images and video. If a user labels a piece of an image as \water," a texture model can be used to propagate this label to other \visually similar" regions. However, a serious problem is that no single model has been found to be good enough to reliably match human perception of similarity in pictures. Rather than using one model, the system described here knows several texture models, and is equipped with the ability to choose the one which \best explains" the regions selected by the user for annotating. If none of these models suuces, then it creates new explanations by combining models. Examples are given of annotations propagated by the system on natural scenes. The system provides an average gain of four to one in label prediction over a set of 98 images.
Fast Inference and Learning with Sparse Belief Propagation

Even in trees, exact probabilistic inference can be expensi v when the cardinality of the variables is large. This is especially tr oublesome for learning, because many standard estimation techniques, su ch a EM and conditional maximum likelihood, require calling an infere nce algorithm many times. In max-product inference, a standard heuristic for ontrolling this complexity in linear chains is beam search, that is , to ignore variable configurations during inference once their estima ted probability becomes sufficiently low. Although quite effective for m ax-product, during sum-product inference beam search discards probabi lity mass in a way that makes learning unstable. In this paper, we introdu ce a variational perspective on beam search that uses a approximating mixture of Kronecker delta functions. This motivates a novel variatio nal approximation for arbitrary tree-structured models, which maintain s a adaptivelysized sparse belief state—thus extending beam search from m ax-product to sum-product, and from linear chains to arbitrary trees. W e report efficiency improvements for max-product inference over other b eam search techniques. Also, unlike heuristic methods for discarding probability mass, our method can be used effectively for conditional max i um likelihood training. On both synthetic and real-world problems , we report four-fold increases in learning speed with no loss in accura cy.
Expectation Propagation in Factor Graphs: A Tutorial

Expectation propagation is an important variational inference algorithm for graphical models, especially if some of the variables are continuous. This tutorial presents two views EP: as repeatedly projecting into an approximating family, and as a message-passing algorithm. We present EP in terms of factor graphs, which simplifies some of the presentation and provides concreteness, while remaining completely general. We give special emphasis to explaining why belief propagation is a special case of EP, and how EP can be used to approximate a factor graph’s partition function.
Joint Parsing and Semantic Role Labeling

A striking feature of human syntactic processing is that it is context-dependent, that is, it seems to take into account semantic information from the discourse context and world knowledge. In this paper, we attempt to use this insight to bridge the gap between SRL results from gold parses and from automatically-generated parses. To do this, we jointly perform parsing and semantic role labeling, using a probabilistic SRL system to rerank the results of a probabilistic parser. Our current results are negative, because a locally-trained SRL model can return inaccurate probability estimates.
Constrained Kronecker Deltas for Fast Approximate Inference and Estimation

We are interested in constructing fast, principled algorithms for scaling up decoding and parameter estimation in probability models such as Hidden Markov Models (HMMs) and linear-chain Conditional Random Fields (CRFs) with large state spaces. In this paper we present a principled extension of beam search to beam inference and learning. We present a new approach for approximate inference based on approximating single variable potentials with a constrained, lower complexity, adaptively sized sum or mixture of Kronecker delta functions. We present inference and optimization results on synthetic and real data for HMMs and CRFs. In a number of cases, we demonstrate potential speed increases of close to an order of magnitude with little or no degradation in accuracy over exact methods for decoding and parameter estimation. We also demonstrate speed, accuracy and robustness improvements over traditional, n-best and threshold based beam methods. While we focus experiments on HMMs and CRFs, these methods should be widely applicable for inference in many factorized representations of probability distributions with large state spaces.
Feature Bagging: Preventing Weight Undertraining in Structured Discriminative Learning

Discriminatively-trained probabilistic models are very popular in NLP because of the latitude they afford in designing features. But training involves complex trade-offs among weights, whichcanbedangerous: afew highlyindicative features can swamp the contribution of many individually weaker features, causing their weights to be undertrained. Such a model is less robust, for the highly-indicative features may be noisy or missing in the test data. To ameliorate this weight undertraining, we propose a new training method, called feature bagging, in which separate models are trained on subsets of the original features, and combined using a mixture model or a product of experts. We evaluate feature bagging on linear-chain conditional random fields for two natural-language tasks. On both tasks, the feature-bagged CRF performs better than simply training a single CRF on all the features.
Piecewise Training for Undirected Models

For many large undirected models that arise in real-world applications, exact maximum-likelihood training is intractable, because it requires computing marginal distributions of the model. Conditional training is even more difficult, because the partition function depends not only on the parameters, but also on the observed input, requiring repeated inference over each training example. An appealing idea for such models is to independently train a local undirected classifier over each clique, afterwards combining the learned weights into a single global model. In this paper, we show that this piecewise method can be justified as minimizing a new family of upper bounds on the log partition function. On three natural-language data sets, piecewise training is more accurate than pseudolikelihood, and often performs comparably to global training using belief propagation.
Composition of Conditional Random Fields for Transfer Learning

Many learning tasks have subtasks for which much training data exists. Therefore, we want to transfer learning from the old, general-purpose subtask to a more specific new task, for which there is often less data. While work in transfer learning often considers how the old task should affect learning on the new task, in this paper we show that it helps to take into account how the new task affects the old. Specifically, we perform joint decoding of separately-trained sequence models, preserving uncertainty between the tasks and allowing information from the new task to affect predictions on the old task. On two standard text data sets, we show that joint decoding outperforms cascaded decoding.
Fast, Piecewise Training for Discriminative Finite-state and Parsing Models

Discriminitive models for sequences and trees—such as linear-chain conditional random fields (CRFs) and max-margin parsing—have shown great promise because they combine the ability to incorporate arbitrary input features and the benefits of principled global inference over their structured outputs. However, since parameter estimation in these models involves repeatedly performing this global inference, training can be very slow. We present piecewise training, a new training method that combines the speed of local training with the accuracy of global training by incorporating a limited amount of global information derived from previous errors of the model. On namedentity and part-of-speech data, we show that our new method not only trains in less than one-fifth the time of a CRF and yields improved accuracy over the MEMM, but surprisingly also provides a statistically-significant gain in accuracy over the CRF. Also, we present preliminary results showing a potential application to efficient training of discriminative parsers.
Learning in Markov Random Fields with Contrastive Free Energies

Learning Markov random field (MRF) models is notoriously hard due to the presence of a global normalization factor. In this paper we present a new framework for learning MRF models based on the contrastive free energy (CF) objective function. In this scheme the parameters are updated in an attempt to match the average statistics of the data distribution and a distribution which is (partially or approximately) “relaxed” to the equilibrium distribution. We show that maximum likelihood, mean field, contrastive divergence and pseudo-likelihood objectives can be understood in this paradigm. Moreover, we propose and study a new learning algorithm: the “kstep Kikuchi/Bethe approximation”. This algorithm is then tested on a conditional random field model with “skip-chain” edges to model long range interactions in text data. It is demonstrated that with no loss in accuracy, the training time is brought down on average from 19 hours (BP based learning) to 83 minutes, an order of magnitude improvement.
Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data

In sequence modeling, we often wish to represent complex interaction between labels, such as when performing multiple, cascaded labeling tasks on the same sequence, or when long-range dependencies exist. We present dynamic conditional random fields (DCRFs), a generalization of linear-chain conditional random fields (CRFs) in which each time slice contains a set of state variables and edges---a distributed state representation as in dynamic Bayesian networks (DBNs)---and parameters are tied across slices. Since exact inference can be intractable in such models, we perform approximate inference using several schedules for belief propagation, including tree-based reparameterization (TRP). On a natural-language chunking task, we show that a DCRF performs better than a series of linear-chain CRFs, achieving comparable performance using only half the training data.
A Bayesian Blackboard for Information Fusion

A Bayesian blackboard is just a conventional, knowledge-based blackboard system in which knowledge sources modify Bayesian networks on the blackboard. As an architecture for intelligence analysis and data fusion this has many advantages: the blackboard is a shared workspace or "corporate memory" for collaborating analysts; analyses can be developed over long periods of time with information that arrives in dribs and drabs; the computer's contribution to analysis can range from data-driven statistical algorithms up to domain-specific, knowledge-based inference; and perhaps most important, the control of intelligence-gathering in the world and inference on the blackboard can be rational, that is, grounded in probability and utility theory. The Bayesian blackboard architecture presented here, called AIID, serves both as a prototype system for intelligence analysis and as a laboratory for testing mathematical models of the economics of intelligence analysis.
Collective Segmentation and Labeling of Distant Entities in Information Extraction

In information extraction, we often wish to identify all mentions of an entity, such as a person or organization. Traditionally, a group of words is labeled as an entity based only on local information. But information from throughout a document can be useful; for example, if the same word is used multiple times, it is likely to have the same label each time. We present a CRF that explicitly represents dependencies between the labels of pairs of similar words in a document. On a standard information extraction data set, we show that learning these dependencies leads to a 13.7% reduction in error on the field that had caused the most repetition errors.
Piecewise Training with Parameter Independence Diagrams: Comparing Globally- and Locally-trained Linear-chain CRFs

We present a diagrammatic formalism and practial methods for introducing additional independence assumptions into parameter estimation, enabling efficient training of undirected graphical models in locally-normalized pieces. On two real-world data sets we demonstrate our locally-trained linear-chain CRFs outperforming traditional CRFs— training in less than one-fifth the time, and providing a statisticallysignificant gain in accuracy.
Guided Incremental Construction of Belief Networks

Because uncertain reasoning is often intractable, it is hard to reason with a large amount of knowledge. One solution to this problem is to specify a set of possible models, some simple and some complex, and choose which to use based on the problem. We present an architecture for interpreting temporal data, called AIID, that incrementally constructs belief networks based on data that arrives asynchronously. It synthesizes the opportunistic control of the blackboard architecture with recent work on constructing belief networks from fragments. We have implemented this architecture in the domain of military analysis.
Very Predictive Ngrams for Space-Limited Probabilistic Models

In sequential prediction tasks, one repeatedly tries to predict the next element in a sequence. A classical way to solve these problems is to fit an order-n Markov model to the data, but fixed-order models are often bigger than they need to be. In a fixed-order model, all predictors are of length n, even if a shorter predictor would work just as well. We present a greedy algorithm, vpr, for finding variable-length predictive rules. Although vpr is not optimal, we show that on English text, it performs similarly to fixed-order models but uses fewer parameters.
Information Theory and Representation in Associative Word Learning

A significant portion of early language learning
can be viewed as an associative learning
problem. We investigate the use of associative
language learning based on the principle that
words convey Shannon information about the
environment. We discuss the shortcomings
in representation used by previous associative
word learners and propose a functional representation
that not only denotes environmental
categories, but serves as the basis for activities
and interaction with the environment.
We present experimental results with an autonomous
agent acquiring language.
Learning effects of robot actions using temporal associations

Agents need to know the effects of their actions. Strong associations between actions and effects can be found by counting how often they co-occur. We present an algorithm that learns temporal patterns expressed as fluents, i.e. propositions with temporal extent. The fluent-learning algorithm is hierarchical and unsupervised. It works by maintaining co-occurrence statistics on pairs of fluents. In experiments on a mobile robot, the fluent-learning algorithm found temporal associations that correspond to effects of the robot's actions.
Computers and Octi: Report from the 2001 Tournament

Computers are strong players of many strategy games, but in some games, namely Go, success has been more elusive. Thus, it may be fruitful to explore games with intermediate complexity. Octi is such a game. I describe the game of Octi, report results from the 2001 Computer Octi Tournament, and explain why Octi may be a useful domain for research in game AI.
A Conditional Model of Deduplication for Multi-Type Relational Data

Record deduplication is the task of merging database records that refer to the same underlying entity. In relational databases, accurate deduplication for records of one type is often dependent on the merge decisions made for records of other types. Whereas nearly all previous approaches have merged records of different types independently, this work models these inter-dependencies explicitly to collectively deduplicate records of multiple types. We construct a conditional random field model of deduplication that captures these relational dependencies, and then employ a novel relational partitioning algorithm to jointly deduplicate records. We evaluate the system on two citation matching datasets, for which we deduplicate both papers and venues. We show that by collectively deduplicating paper and venue deduplication, and up to a 20% error reduction in paper deduplication over competing methods.
Predictive Random Fields : Latent Variable Models Fit by Multiway Conditional Probability with Applications to Document Analysis

We introduce predictive random fields, a framework for learning undirected graphical models based not on joint, generative likelihood, or on conditional likelihood, but based on a product of several conditional likelihoods each relying on common sets of parameters and predicting different subsets of variables conditioned on other subsets. When applied to models with latent variables, such as the Harmonium, this approach results in powerful clustering models that combine the advantages of conditional random fields with the unsupervised clustering ability of popular topic models, such as latent Dirichlet allocation and its successors. We present new algorithms for parameter estimation based on contrastive divergence. Experimental results show significant improvement in inferring hidden document categories, and learning models of authors, words, topics and time.