nils-herrmann/clean_scirepeval huggingface dataset:
Think Globally, Fit Locally: Unsupervised Learning of Low Dimensional Manifold

The problem of dimensionality reduction arises in many fields of information processing, including machine learning, data compression, scientific visualization, pattern recognition, and neural computation. Here we describe locally linear embedding (LLE), an unsupervised learning algorithm that computes low dimensional, neighborhood preserving embeddings of high dimensional data. The data, assumed to be sampled from an underlying manifold, are mapped into a single global coordinate system of lower dimensionality. The mapping is derived from the symmetries of locally linear reconstructions, and the actual computation of the embedding reduces to a sparse eigenvalue problem. Notably, the optimizations in LLE---though capable of generating highly nonlinear embeddings---are simple to implement, and they do not involve local minima. In this paper, we describe the implementation of the algorithm in detail and discuss several extensions that enhance its performance. We present results of the algorithm applied to data sampled from known manifolds, as well as to collections of images of faces, lips, and handwritten digits. These examples are used to provide extensive illustrations of the algorithm's performance---both successes and failures---and to relate the algorithm to previous and ongoing work in nonlinear dimensionality reduction.
Learning High Dimensional Correspondences from Low Dimensional Manifolds

Many different high dimensional data sets are characterized by the same underlying modes of variability. When these modes of variability are continuous and few in number, they can be viewed as parameterizing a low dimensional manifold. The manifold provides a compact shared representation of the data, suggesting correspondences between the high dimensional examples from different data sets. These correspondences, though naturally induced by the underlying manifold, are difficult to learn using traditional methods in supervised learning. In this paper, we generalize three methods in unsupervised learning—principal components analysis, factor analysis, and locally linear embedding— to discover subspaces and manifolds that provide common low dimensional representations of different high dimensional data sets. We use the shared representations discovered by these algorithms to put high dimensional examples from different data sets into correspondence. Finally, we show that a notion of “self-correspondence” between examples in the same data set can be used to improve the performance of these algorithms on small data sets. The algorithms are demonstrated on images and text.
Think globally, fit locally: unsupervised l earning of non-linear manifolds

A construction member having a tubular body with side walls surrounding a passage through the body. Each side wall has an opening and a plurality of notches for accommodating a connecting element. The side walls have a longitudinally projected flange of a size that fits into a passage of another construction member so that a plurality of construction members can be connected together. The body can have a square, triangular, and other multiside shapes.
Real Time Voice Processing with Audiovisual Feedback: Toward Autonomous Agents with Perfect Pitch

We have implemented a real time front end for detecting voiced speech and estimating its fundamental frequency. The front end performs the signal processing for voice-driven agents that attend to the pitch contours of human speech and provide continuous audiovisual feedback. The algorithm we use for pitch tracking has several distinguishing features: it makes no use of FFTs or autocorrelation at the pitch period; it updates the pitch incrementally on a sample-by-sample basis; it avoids peak picking and does not require interpolation in time or frequency to obtain high resolution estimates; and it works reliably over a four octave range, in real time, without the need for postprocessing to produce smooth contours. The algorithm is based on two simple ideas in neural computation: the introduction of a purposeful nonlinearity, and the error signal of a least squares fit. The pitch tracker is used in two real time multimedia applications: a voice-to-MIDI player that synthesizes electronic music from vocalized melodies, and an audiovisual Karaoke machine with multimodal feedback. Both applications run on a laptop and display the user's pitch scrolling across the screen as he or she sings into the computer.
Multiplicative Updates for Classification by Mixture Models

We investigate a learning algorithm for the classification of nonnegative data by mixture models. Multiplicative update rules are derived that directly optimize the performance of these models as classifiers. The update rules have a simple closed form and an intuitive appeal. Our algorithm retains the main virtues of the Expectation-Maximization (EM) algorithm—its guarantee of monotonic improvement, and its absence of tuning parameters—with the added advantage of optimizing a discriminative objective function. The algorithm reduces as a special case to the method of generalized iterative scaling for log-linear models. The learning rate of the algorithm is controlled by the sparseness of the training data. We use the method of nonnegative matrix factorization (NMF) to discover sparse distributed representations of the data. This form of feature selection greatly accelerates learning and makes the algorithm practical on large problems. Experiments show that discriminatively trained mixture models lead to much better classification than comparably sized models trained by EM.
A statistical model for robust integration of narrowband cues in speech

We investigate a statistical model for integrating narrowband cues in speech. The model is inspired by two ideas in human speech perception: (i) Fletcher?s hypothesis (1953) that independent detectors, working in narrow frequency bands, account for the robustness of auditory strategies, and (ii) Miller and Nicely?s analysis (1955) that perceptual confusions in noisy bandlimited speech are correlated with phonetic features. We apply the model to detecting the phonetic feature +/?sonorant] that distinguishes vowels, approximants, and nasals (sonorants) from stops, fricatives, and affricates (obstruents). The model is represented by a multilayer probabilistic network whose binary hidden variables indicate sonorant cues from different parts of the frequency spectrum. We derive the Expectation-Maximization algorithm for estimating the model?s parameters and evaluate its performance on clean and corrupted speech.
An Introduction to Locally Linear Embedding

Many problems in information processing involve some form of dimensionality reduction. Here we describe locally linear embedding (LLE), an unsupervised learning algorithm that computes low dimensional, neighborhood preserving embeddings of high dimensional data. LLE attempts to discover nonlinear structure in high dimensional data by exploiting the local symmetries of linear reconstructions. Notably, LLE maps its inputs into a single global coordinate system of lower dimensionality, and its optimizations— though capable of generating highly nonlinear embeddings—do not involve local minima. We illustrate the method on images of lips used in audiovisual speech synthesis.
Attractor Dynamics in Feedforward Neural Networks

We study the probabilistic generative models parameterized by feedfor-ward neural networks. An attractor dynamics for probabilistic inference in these models is derived from a mean field approximation for large, layered sigmoidal networks. Fixed points of the dynamics correspond to solutions of the mean field equations, which relate the statistics of each unittothoseofits Markovblanket. We establish global convergence of the dynamics by providing a Lyapunov function and show that the dynamics generate the signals required for unsupervised learning. Our results for feedforward networks provide a counterpart to those of Cohen-Grossberg and Hopfield for symmetric networks.
Periodic Component Analysis: An Eigenvalue Method for Representing Periodic Structure in Speech

An eigenvalue method is developed for analyzing periodic structure in speech. Signals are analyzed by a matrix diagonalization reminiscent of methods for principal component analysis (PCA) and independent component analysis (ICA). Our method--called periodic component analysis (πCA)--uses constructive interference to enhance periodic components of the frequency spectrum and destructive interference to cancel noise. The front end emulates important aspects of auditory processing, such as cochlear filtering, nonlinear compression, and insensitivity to phase, with the aim of approaching the robustness of human listeners. The method avoids the inefficiencies of autocorrelation at the pitch period: it does not require long delay lines, and it correlates signals at a clock rate on the order of the actual pitch, as opposed to the original sampling rate. We derive its cost function and present some experimental results.
Feature extraction in human speech recognition

In 1955 George Miller and Patricia Nicely explored the human speech code and discovered that a five‐channel feature code is used to extract phonemes from speech. Their mutual information analysis of the confusion matrices of filtered noisy speech showed that the five channels were virtually independent of each other, accounting for the robustness of speech communication quantified by Miller, Heise, and Lichten in 1951. While the 1955 analysis was a major step forward, it did not lead to practical results, since there are no known methods for extracting these features from the speech. This weakness is the basis of the present research program, to define the signal processing required to robustly extract features from narrow bands of speech. Our emphasis is to work in narrow bands, starting from critical bands. In this paper we discuss the first step of this process, which is to measure (a) the jnd for the detection of speech in noise and (b) the jnd for residue pitch given narrow bands of speech in noise.
Maximum likelihood and minimum classification error factor analysis for automatic speech recognition

Hidden Markov models (HMMs) for automatic speech recognition rely on high dimensional feature vectors to summarize the short-time properties of speech. Correlations between features can arise when the speech signal is nonstationary or corrupted by noise. We investigate how to model these correlations using factor analysis, a statistical method for dimensionality reduction. Factor analysis uses a small number of parameters to model the covariance structure of high dimensional data. These parameters can be chosen in two ways: (1) to maximize the likelihood of observed speech signals, or (2) to minimize the number of classification errors. We derive an expectation-maximization (EM) algorithm for maximum likelihood estimation and a gradient descent algorithm for improved class discrimination. Speech recognizers are evaluated on two tasks, one small-sized vocabulary (connected alpha-digits) and one medium-sized vocabulary (New Jersey town names). We find that modeling feature correlations by factor analysis leads to significantly increased likelihoods and word accuracies. Moreover, the rate of improvement with model size often exceeds that observed in conventional HMM's.
Modeling the rate of speech by Markov processes on curves

We propose a statistical model for automatic speech recognition that relates variations in speaking rate to nonlinear warpings of time. The model describes a discrete random variable, s(t), that evolves as a function of the arc length traversed along a curve, parameterized by x(t). Since arc length does not depend on the rate at which a curve is traversed, this evolution gives rise to a family of Markov processes whose predictions, Pr[sjx], are invariant to nonlinear warpings of time. We describe the use of such models, known as Markov processes on curves (MPCs), for automatic speech recognition, where x are acoustic feature trajectories and s are phonetic transcriptions. On two tasks|recognizing New Jersey town names and connected alpha-digits|we nd that MPCs yield lower word error rates than comparably trained hid-
A Mean Field Learning Algorithm for Unsupervised Neural Networks

We introduce a learning algorithm for unsupervised neural networks based on ideas from statistical mechanics. The algorithm is derived from a mean field approximation for large,layered sigmoid belief networks. We show how to (approximately) infer the statistics of these networks without resort to sampling. This is done by solving the mean field equations, which relate the statistics of each unit to those of its Markov blanket. Using these statistics as target values, the weights in the network are adapted by a local delta rule. We evaluate the strengths and weaknesses of these networks for problems in statistical pattern recognition.
Markov Processes on Curves for Automatic Speech Recognition

We investigate a probabilistic framework for automatic speech recognition based on the intrinsic geometric properties of curves. In particular, we analyze the setting in which two variables-one continuous (x), one discrete (s)-evolve jointly in time. We suppose that the vector x traces out a smooth multidimensional curve and that the variable s evolves stochastically as a function of the arc length traversed along this curve. Since arc length does not depend on the rate at which a curve is traversed, this gives rise to a family of Markov processes whose predictions, Pr[s|x], are invariant to nonlinear warpings of time. We describe the use of such models, known as Markov processes on curves (MPCs), for automatic speech recognition, where x are acoustic feature trajectories and s are phonetic transcriptions. On two tasks--recognizing New Jersey town names and connected alpha-digits--we find that MPCs yield lower word error rates than comparably trained hidden Markov models.
Inference in Multilayer Networks Large Deviation Bounds

VIa We study probabilistic inference in large, layered Bayesian networks represented as directed acyclic graphs. We show that the intractability of exact inference in such networks does not preclude their effective use. We give algorithms for approximate probabilistic inference that exploit averaging phenomena occurring at nodes with large numbers of parents . We show that these algorithms compute rigorous lower and upper bounds on marginal probabilities of interest, prove that these bounds become exact in the limit of large networks, and provide rates of convergence.
Automatic Segmentation of Continuous Trajectories with Invariance to Nonlinear Warpings of Time

We study the classiication problem that arises when two variables|one continuous (x), one discrete (s)|evolve jointly in time. We suppose that the vector x traces out a smooth multidimensionalcurve, to each point of which the variable s attaches a discrete label. The trace of s thus partitions the curve into diierent segments whose boundaries occur where s changes value. We consider how to learn the mapping between x and s from examples of segmented curves. Our approach is to model the conditional random process that generates segments of constant s along the curve of x. We suppose that the variable s evolves stochastically as a function of the arc length traversed by x. Since arc length does not depend on the rate at which a curve is traversed, this gives rise to a family of Markov processes whose predictions , Prrs j x], are invariant to nonlinear warpings (or reparameterizations) of time. We show how to learn the parameters of these Markov processes from labeled and/or unla-beled examples of segmented curves. The resulting models are motivated for automatic speech recognition, where x are acoustic features and s are phonetic transcriptions.
Large Deviation Methods for Approximate Probabilistic Inference

We study two-layer belief networks of binary random variables in which the conditional probabilities Pr [child|parents] depend monotonically on weighted sums of the parents. In large networks where exact probabilistic inference is intractable, we show how to compute upper and lower bounds on many probabilities of interest. In particular, using methods from large deviation theory, we derive rigorous bounds on marginal probabilities such as Pr[children] and prove rates of convergence for the accuracy of our bounds as a function of network size. Our results apply to networks with generic transfer function parameterizations of the conditional probability tables, such as sigmoid and noisy-OR. They also explicitly illustrate the types of averaging behavior that can simplify the problem of inference in large networks.
Inference in Multilayer Networks via Large Deviation Bounds

We study probabilistic inference in large, layered Bayesian networks represented as directed acyclic graphs. We show that the intractability of exact inference in such networks does not preclude their effective use. We give algorithms for approximate probabilistic inference that exploit averaging phenomena occurring at nodes with large numbers of parents. We show that these algorithms compute rigorous lower and upper bounds on marginal probabilities of interest, prove that these bounds become exact in the limit of large networks, and provide rates of convergence.
Modeling Acoustic Correlations by Factor Analysis

Hidden Markov models (HMMs) for automatic speech recognition rely on high dimensional feature vectors to summarize the short-time properties of speech. Correlations between features can arise when the speech signal is non-stationary or corrupted by noise. We investigate how to model these correlations using factor analysis, a statistical method for dimensionality reduction. Factor analysis uses a small number of parameters to model the covariance structure of high dimensional data. These parameters are estimated by an Expectation-Maximization (EM) algorithm that can be embedded in the training procedures for HMMs. We evaluate the combined use of mixture densities and factor analysis in HMMs that recognize alphanumeric strings. Holding the total number of parameters fixed, we find that these methods, properly combined, yield better models than either method on its own.
Aggregate and mixed-order Markov models for statistical language processing

We consider the use of language models whose size and accuracy are intermediate between different order n-gram models. Two types of models are studied in particular. Aggregate Markov models are classbased bigram models in which the mapping from words to classes is probabilistic. Mixed-order Markov models combine bigram models whose predictions are conditioned on different words. Both types of models are trained by ExpectationMaximization (EM) algorithms for maximum likelihood estimation. We examine smoothing procedures in which these models are interposed between different order n-grams. This is found to significantly reduce the perplexity of unseen word combinations.
Minimum classification error factor analysis (MCE-FA) for automatic speech recognition

Modeling acoustic correlation in automatic speech recognition systems is essential when the speech signal is non stationary or corrupted by noise. We present a statistical method for improved acoustic modeling in continuous density hidden Markov models (HMMs). Factor analysis uses a small number of parameters to model the covariance structure of the speech signal. These parameters are estimated by an Expectation-Maximization algorithm, then further adjusted using discriminative minimum classification error training. Experimental results on 1219 New Jersey town names demonstrate that the proposed method produces faster, smaller and more accurate recognition models.
A Variational Principle for Model-based Morphing

Given a multidimensional data set and a model of its density, we consider how to define the optimal interpolation between two points. This is done by assigning a cost to each path through space, based on two competing goals-one to interpolate through regions of high density, the other to minimize arc length. From this path functional, we derive the Euler-Lagrange equations for extremal motion; given two points, the desired interpolation is found by solving a boundary value problem. We show that this interpolation can be done efficiently, in high dimensions, for Gaussian, Dirichlet, and mixture models.
Exploiting Tractable Substructures in Intractable Networks

We develop a refined mean field approximation for inference and learning in probabilistic neural networks. Our mean field theory, unlike most, does not assume that the units behave as independent degrees of freedom; instead, it exploits in a principled way the existence of large substructures that are computationally tractable. To illustrate the advantages of this framework, we show how to incorporate weak higher order interactions into a first-order hidden Markov model, treating the corrections (but not the first order structure) within mean field theory.
Detecting, localizing and recovering kinematics of textured animals

We develop and demonstrate an object recognition system capable of accurately detecting, localizing, and recovering the kinematic configuration of textured animals in real images. We build a deformation model of shape automatically from videos of animals and an appearance model of texture from a labeled collection of animal images, and combine the two models automatically. We develop a simple texture descriptor that outperforms the state of the art. We test our animal models on two datasets; images taken by professional photographers from the Corel collection, and assorted images from the Web returned by Google. We demonstrate quite good performance on both datasets. Comparing our results with simple baselines, we show that for the Google set, we can recognize objects from a collection demonstrably hard for object recognition.
Evaluation strategies for image understanding and retrieval

We address evaluation of image understanding and retrieval large scale image data in the context of three evaluation projects. The first project is a comprehensive strategy for evaluating image retrieval algorithms and provides an open reference data set for doing so. The second project develops word prediction as a semantically relevant evaluation strategy, and applies it to the evaluation of of image processing methods for semantic image analysis. The third project evaluates words for suitability of their visual properties for use in an image annotation framework.
Image region entropy: a measure of "visualness" of web images associated with one concept

We propose a new method to measure "visualness" of concepts, that is, what extent concepts have visual characteristics. To know which concept has visually discriminative power is important for image annotation, especially automatic image annotation by image recognition system, since not all concepts are related to visual contents. Our method performs probabilistic region selection for images which are labeled as concept "X" or "non-X", and computes an entropy measure which represents "visualness" of concepts. In the experiments, we collected about forty thousand images from the World-Wide Web using the Google Image Search for 150 concepts. We examined which concepts are suitable for annotation of image contents.