nils-herrmann/clean_scirepeval huggingface dataset:
Perceptually Organized Em: a Framework for Motion Segmentation That Combines Information about Form and Motion

Recent progress in motion analysis has been achieved with systems that estimate global pa-rameterized motion by integrating multiple constraints. The success of these approaches depends critically on the ability to segment constraints derived from diierent motions. Hence the problems of motion estimation and segmen-tation are tightly coupled. We believe it is impossible to solve these problems solely in the motion domain, and that mechanisms of spatial form analysis must be incorporated into the motion estimation procedure. We present a new framework which allows the incorporation of form information in a graceful manner. It combines concepts from perceptual organization with the powerful optimization technique of EM. We show that the algorithm is guaranteed to decrease a cost function at every iteration, and that in the absence of form information the cost function reduces to the one minimized by EM. We demonstrate that the approach can achieve good motion estimation and segmentation with challenging motion sequences. Recent progress in motion analysis has been achieved with systems that estimate global parameterized mo-These methods have advantages over local optic ow in that they overcome the local ill-posedness of the motion estimation problem by integrating multiple constraints. The sucess of these approaches , however, depends critically on the ability to segment constraints derived from diierent motions. Hence the problems of motion estimation and segmentation have become tightly coupled. The joint solution of these problems remains diicult, even for scenes that are very simple. Consider, for example, the scene shown in g 1(a) (see also Bergen et al., 1990]). Two bars of diierent grey shades are moving, one to the left and one to the right. We will consider how several kinds of motion analyses treat this input. First, the output of a standard least-squares optic ow routine is shown in g. 1(b), as an arrow plot; the x and y components of velocity are shown in g. 1(c) and (d) (velocities below some threshold conndence are set to zero, a b c d Figure 1: a A simple image sequence which causes problems for traditional motion estimation algorithms. b Least squares optical ow shown as an arrow plot c Least squares optical ow horizontal component. d Least squares optical ow vertical component. the algorithm is an implementation of Lucas and Kanade (1981) modiied according to Simoncelli et al., 1991]). Although this sequence is a synthetic one, it illustrates problems that occur frequently in analyzing real …
Robust acoustic object detection.

We consider a novel approach to the problem of detecting phonological objects like phonemes, syllables, or words, directly from the speech signal. We begin by defining local features in the time-frequency plane with built in robustness to intensity variations and time warping. Global templates of phonological objects correspond to the coincidence in time and frequency of patterns of the local features. These global templates are constructed by using the statistics of the local features in a principled way. The templates have clear phonetic interpretability, are easily adaptable, have built in invariances, and display considerable robustness in the face of additive noise and clutter from competing speakers. We provide a detailed evaluation of the performance of some diphone detectors and a word detector based on this approach. We also perform some phonetic classification experiments based on the edge-based features suggested here.
A Geometric Perspective on Speech Sounds

In order to effectively approach high dimensional pattern recognition problems, one seeks to understand and exploit any inherent low dimensional structure. Recently, a number of manifold learning algorithms have been motivated by a geometric point of view that models high dimensional data as lying near a low dimensional submanifold of the original space. Our paper has two main goals: (i) to investigate this manifold assumption for natural speech data. It seems intuitive that a human speech producing apparatus with few degrees of freedom would not produce sounds that fill up the acoustic space. We formalize this intuition by considering a concatenated acoustic tube model of the vocal tract and showing that the sounds generated by such a system lie on a low dimensional curved submanifold of the ambient acoustic space. To the extent that this model captures the essence of human speech production, the manifold assumption is true of natural speech data. (ii) to explore the implications of this geometric point of view towards human speech. We show that the manifold structure of speech sounds may be exploited for dimensionality reduction, semi-supervised learning, and speech representation with sometimes striking perfomance improvements in simulated and real speech data. The non-linear geometry of speech sounds suggests new interpretations of phenomena such as the perceptual magnet effect or quantal theory.
Stability and Generalization of Bipartite Ranking Algorithms

The problem of ranking, in which the goal is to learn a real-valued ranking function that induces a ranking or ordering over an instance space, has recently gained attention in machine learning. We study generalization properties of ranking algorithms, in a particular setting of the ranking problem known as the bipartite ranking problem, using the notion of algorithmic stability. In particular, we derive generalization bounds for bipartite ranking algorithms that have good stability properties. We show that kernel-based ranking algorithms that perform regularization in a reproducing kernel Hilbert space have such stability properties, and therefore our bounds can be applied to these algorithms; this is in contrast with previous generalization bounds for ranking, which are based on uniform convergence and in many cases cannot be applied to these algorithms. A comparison of the bounds we obtain with corresponding bounds for classification algorithms yields some interesting insights into the difference in generalization behaviour between ranking and classification.
Face recognition using Laplacianfaces

We propose an appearance-based face recognition method called the Laplacianface approach. By using locality preserving projections (LPP), the face images are mapped into a face subspace for analysis. Different from principal component analysis (PCA) and linear discriminant analysis (LDA) which effectively see only the Euclidean structure of face space, LPP finds an embedding that preserves local information, and obtains a face subspace that best detects the essential face manifold structure. The Laplacianfaces are the optimal linear approximations to the eigenfunctions of the Laplace Beltrami operator on the face manifold. In this way, the unwanted variations resulting from changes in lighting, facial expression, and pose may be eliminated or reduced. Theoretical analysis shows that PCA, LDA, and LPP can be obtained from different graph models. We compare the proposed Laplacianface approach with Eigenface and Fisherface methods on three different face data sets. Experimental results suggest that the proposed Laplacianface approach provides a better representation and achieves lower error rates in face recognition.
Tensor Subspace Analysis

Previous work has demonstrated that the image variations of many objects (human faces in particular) under variable lighting can be effectively modeled by low dimensional linear spaces. The typical linear sub-space learning algorithms include Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Locality Preserving Projection (LPP). All of these methods consider an n1 x n2 image as a high dimensional vector in ℝn1 x n2, while an image represented in the plane is intrinsically a matrix. In this paper, we propose a new algorithm called Tensor Subspace Analysis (TSA). TSA considers an image as the second order tensor in Rn1 ⊗ Rn2, where Rn1 and Rn2 are two vector spaces. The relationship between the column vectors of the image matrix and that between the row vectors can be naturally characterized by TSA. TSA detects the intrinsic local geometrical structure of the tensor space by learning a lower dimensional tensor subspace. We compare our proposed approach with PCA, LDA and LPP methods on two standard databases. Experimental results demonstrate that TSA achieves better recognition rate, while being much more efficient.
Laplacian Score for Feature Selection

In supervised learning scenarios, feature selection has been studied widely in the literature. Selecting features in unsupervised learning scenarios is a much harder problem, due to the absence of class labels that would guide the search for relevant information. And, almost all of previous unsupervised feature selection methods are "wrapper" techniques that require a learning algorithm to evaluate the candidate feature subsets. In this paper, we propose a "filter" method for feature selection which is independent of any learning algorithm. Our method can be performed in either supervised or unsupervised fashion. The proposed method is based on the observation that, in many real world classification problems, data from the same class are often close to each other. The importance of a feature is evaluated by its power of locality preserving, or, Laplacian Score. We compare our method with data variance (unsupervised) and Fisher score (supervised) on two data sets. Experimental results demonstrate the effectiveness and efficiency of our algorithm.
General conditions for predictivity in learning theory

Developing theoretical foundations for learning is a key step towards understanding intelligence. ‘Learning from examples’ is a paradigm in which systems (natural or artificial) learn a functional relationship from a training set of examples. Within this paradigm, a learning algorithm is a map from the space of training sets to the hypothesis space of possible functional solutions. A central question for the theory is to determine conditions under which a learning algorithm will generalize from its finite training set to novel examples. A milestone in learning theory was a characterization of conditions on the hypothesis space that ensure generalization for the natural class of empirical risk minimization (ERM) learning algorithms that are based on minimizing the error on the training set. Here we provide conditions for generalization in terms of a precise stability property of the learning process: when the training set is perturbed by deleting one example, the learned hypothesis does not change much. This stability property stipulates conditions on the learning map rather than on the hypothesis space, subsumes the classical theory for ERM algorithms, and is applicable to more general algorithms. The surprising connection between stability and predictivity has implications for the foundations of learning theory and for the design of novel algorithms, and provides insights into problems as diverse as language learning and inverse problems in physics and engineering.
Comment on ‘A multiple process solution …’ (B. Macwhinney)

The central question posed by the so called ‘logical problem of language acquisition’ is how it comes to be that children are able to GENERALIZE from a finite set of linguistic data to acquire (learn, develop, grow) a computational system (grammar) that applies to novel examples not encountered before. The difficulty of this generalization problem was first posed cogently by Gold and while Macwhinney discusses the Gold framework and the linguistic literature on this matter, it is worth noting that the Gold framework is not the only one. There are at least two important new sources of insight from computational learning theory in the decades following Gold that need to be kept in mind. First, there is the development of empirical process theory that forms the basis of any analysis of statistical learning (see summary in Vapnik, 1998). Applying this approach to language (see Niyogi, 1998 for a treatment), one concludes that the family of learnable grammars must have a finite Vapnik Chervonenkis (VC) dimension. The VC dimension is a combinatorial measure of the complexity of a class of functions. Grammars may be viewed as functions mapping sentences to their grammaticality value. In this more sophisticated sense of the VC dimension, the class of grammars must be constrained. Second, there is the development of the theory of computational complexity suggesting that while a learning algorithm might exist, it may not be efficient, i.e. run in polynomial time. These two developments come together in the influential Probably Approximately Correct (PAC) model (Valiant, 1984).
WS04 Proposal: Landmark Detection

We propose to develop a landmark-based speech recognizer. We will experiment with the robustness and accuracy benefits provided by (1) diverse acoustic observations, and (2) multi-class label-sequence structural risk minimization. As a preliminary baseline for this proposal, MFCC+d+dd 13-mixture 3-state HMMs were trained to distinguish stops, fricatives, nasals, vowels, glides, and silence. Depending on insertion penalty, six-class recognition accuracy varied from 52% to 74% (82% correct, 8% insertion). Binary recognition accuracy for the four manner-class distinctive features was 80% for [silence], 78% for [syllabic], 78% for [sonorant], and 77% for [continuant]. For this result, the HMM required 6310 trainable parameters. Stevens proposed improving the accuracy and noise robustness of speech recognition by eliminating frame-based observation PDFs, and instead focusing the attention of the classifier on instantaneous phonetic landmarks. Phonetic landmarks are articulatory events with acoustic signatures easily detected at low SNR, including syllabic nuclei, intervocalic glides, and onsets and offsets of the distinctive features [sonorant] and [continuant]. Stevens proposed that acoustic observations should be optimized separately for each landmark type, e.g., measures of signal periodicity sampled once per 10ms for the detection of syllable nuclei, but energies sampled once per 1ms for the detection of stops. Following Stevens’ proposal, Niyogi used kernel-based SVMs to detect stop consonants in TIMIT. EspyWilson and her students further developed SVM-based landmark detectors for onsets and offsets of the distinctive features [silence] (94% recognition accuracy), [syllabic] (79% accuracy), [sonorant] (93%), and [continuant] (94%). Six-manner-class recognition accuracy on TIMIT was 80%, using a total of 160 trainable parameters. Juneja and Espy-Wilson are currently testing a Viterbi-like algorithm for continuous speech recognition using SVM landmark detectors, together with a training algorithm comparable to segmental K-means re-estimation. These results form the foundation for the proposed workshop.
Locality Preserving Projections

Many problems in information processing involve some form of dimensionality reduction. In this paper, we introduce Locality Preserving Projections (LPP). These are linear projective maps that arise by solving a variational problem that optimally preserves the neighborhood structure of the data set. LPP should be seen as an alternative to Principal Component Analysis (PCA) – a classical linear technique that projects the data along the directions of maximal variance. When the high dimensional data lies on a low dimensional manifold embedded in the ambient space, the Locality Preserving Projections are obtained by finding the optimal linear approximations to the eigenfunctions of the Laplace Beltrami operator on the manifold. As a result, LPP shares many of the data representation properties of nonlinear techniques such as Laplacian Eigenmaps or Locally Linear Embedding. Yet LPP is linear and more crucially is defined everywhere in ambient space rather than just on the training data points. This is borne out by illustrative examples on some high dimensional data sets.
Measuring the Functional Load of Phonological Contrasts

Frequency counts are a measure of how much use a language makes of a linguistic unit, such as a phoneme or word. However, what is often important is not the units themselves, but the contrasts between them. A measure is therefore needed for how much use a language makes of a contrast, i.e. the functional load (FL) of the contrast. We generalize previous work in linguistics and speech recognition and propose a family of measures for the FL of several phonological contrasts, including phonemic oppositions, distinctive features, suprasegmentals, and phonological rules. We then test it for robustness to changes of corpora. Finally, we provide examples in Cantonese, Dutch, English, German and Mandarin, in the context of historical linguistics, language acquisition and speech recognition. More information can be found at this http URL
Locality Preserving Projections (LPP)

Many problems in information processing involve some form of dimensionality reduction. In this paper, we introduce Locality Preserving Projections (LPP). These are linear projective maps that arise by solving a variational problem that optimally preserves the neighborhood structure of the data set. LPP should be seen as an alternative to principal component analysis (PCA) – a classical linear technique that projects the data along the directions of maximal variance. When the high dimensional data lies on a low dimensional manifold embedded in the ambient space, the Locality Preserving Projections are obtained by finding the optimal linear approximations to the eigenfunctions of the Laplace Beltrami operator on the manifold. As a result, LPP shares many of the data representation properties of non linear techniques such as Laplacian Eigenmap  or Locally Linear Embedding . This is borne out by illustrative examples on a couple of high dimensional image data sets.
Almost-everywhere Algorithmic Stability and Generalization Error

We explore in some detail the notion of algorithmic stability as a viable framework for analyzing the generalization error of learning algorithms. We introduce the new notion of training stability of a learning algorithm and show that, in a general setting, it is sufficient for good bounds on generalization error. In the PAC setting, training stability is both necessary and sufficient for learnability.\ The approach based on training stability makes no reference to VC dimension or VC entropy. There is no need to prove uniform convergence, and generalization error is bounded directly via an extended McDiarmid inequality. As a result it potentially allows us to deal with a broader class of learning algorithms than Empirical Risk Minimization. \ We also explore the relationships among VC dimension, generalization error, and various notions of stability. Several examples of learning algorithms are considered.
Algorithmic Stability and Learning on Manifolds

The talk consists of two parts: in the first part, we review the notion of algorithmic stability to obtain bounds on generalization error using training error estimates. We introduce the new notion of training stability that is sufficient for tight concentration bounds in general and is both necessary and sufficient for PAC learning. In the second part, we consider several algorithms for which the notion of algorithmic stability seems useful. In particular, we consider problems of clustering, classification, and regression in a setting where the data lies on a low-dimensional Riemannian manifold embedded in a high dimensional ambient (Euclidean) space.
Statistical Learning: Stability is Sufficient for Generalization and Necessary and Sufficient for Consistency of Empirical Risk Minimization

Solutions of learning problems by Empirical Risk Minimization (ERM) -- and almost-ERM when the minimizer does not exist -- need to be consistent, so that they may be predictive. They also need to be well-posed in the sense of being stable, so that they might be used robustly. We propose a statistical form of leave-one-out stability, called CVEEE(loo) stability. Our main new results are two. We prove that for bounded loss classes CVEEE(loo) stability is (a) sufficient for generalization, that is convergence in probability of the empirical error to the expected error, for any algorithm satisfying it and, (b) necessary and sufficient for generalization and consistency of ERM. Thus CVEEE(loo) stability is a weak form of stability that represents a sufficient condition for generalization for general learning algorithms while subsuming the classical conditions for consistency of ERM. We discuss alternative forms of stability. In particular, we conclude that for ERM a certain form of well-posedness is equivalent to consistency.
Computational and evolutionary aspects of language

Language is our legacy. It is the main evolutionary contribution of humans, and perhaps the most interesting trait that has emerged in the past 500 million years. Understanding how darwinian evolution gives rise to human language requires the integration of formal language theory, learning theory and evolutionary dynamics. Formal language theory provides a mathematical description of language and grammar. Learning theory formalizes the task of language acquisition—it can be shown that no procedure can learn an unrestricted set of languages. Universal grammar specifies the restricted set of languages learnable by the human brain. Evolutionary dynamics can be formulated to describe the cultural evolution of language and the biological evolution of universal grammar.
Algorithmic stability and ensemble-based learning

We explore two themes in formal learning theory. We begin with a detailed, general study of the relationship between the generalization error and stability of learning algorithms. We then examine ensemble-based learning from the points of view of stability, decorrelation, and threshold complexity. 
A central problem of learning theory is bounding generalization error. Most such bounds have been obtained through uniform convergence, via some variant of VC dimension. These analyses focus on the space of classifiers available to a learning algorithm, rather than on the algorithm itself. 
Bousquet and Elisseeff (2002) have shown, using McDiarmid's method of independent bounded differences (1989), that algorithmic stability implies good bounds on generalization error. However, their definition of stability is too restrictive to be widely applicable. 
We introduce the more general notion of training stability. We show that training stability implies good bounds on generalization error even when the learner has infinite VC dimension. Our proof requires a result of independent interest: we generalize McDiarmid's theorem to the case when differences are bounded with high probability. 
In the Probably-Approximately-Correct setting of Valiant (1984), training stability is necessary and sufficient for good error performance and serves as a distribution-dependent analog of VC dimension. This enables us to emphasize the learning algorithm instead of the classifier space. 
We next consider algorithms (e.g., boosting) which construct ensembles of weak classifiers. We show that AdaBoost (Freund and Schapire, 1997), a widely-used boosting algorithm, is stability-preserving. 
We demonstrate that some boosting algorithms implicitly construct classifiers which are decorrelated (i.e., rarely jointly wrong). We present two new algorithms which build ensembles of explicitly decorrelated classifier pairs. Experiments indicate that these new algorithms are sometimes competitive with AdaBoost and with bagging (Breiman, 1996). The experiments employ a fast new algorithm for finding decorrelated pairs of decision stumps. 
Lastly, we use threshold complexity to show that, under certain conditions, AdaBoost takes a long time to converge. In some situations, our pair-based algorithms are exponentially faster than AdaBoost. We analyze boosting as a dynamical system over the space of sequences of weights.