nils-herrmann/clean_scirepeval huggingface dataset:
Transformation-Invariant Clustering Using the EM Algorithm

Clustering is a simple, effective way to derive useful representations of data, such as images and videos. Clustering explains the input as one of several prototypes, plus noise. In situations where each input has been randomly transformed (e.g., by translation, rotation, and shearing in images and videos), clustering techniques tend to extract cluster centers that account for variations in the input due to transformations, instead of more interesting and potentially useful structure. For example, if images from a video sequence of a person walking across a cluttered background are clustered, it would be more useful for the different clusters to represent different poses and expressions, instead of different positions of the person and different configurations of the background clutter. We describe a way to add transformation invariance to mixture models, by approximating the nonlinear transformation manifold by a discrete set of points. We show how the expectation maximization algorithm can be used to jointly learn clusters, while at the same time inferring the transformation associated with each input. We compare this technique with other methods for filtering noisy images obtained from a scanning electron microscope, clustering images from videos of faces into different categories of identification and pose and removing foreground obstructions from video. We also demonstrate that the new technique is quite insensitive to initial conditions and works better than standard techniques, even when the standard techniques are provided with extra data.
Robust variational speech separation using fewer microphones than speakers

A variational inference algorithm for robust speech separation, capable of recovering the underlying speech sources even in the case of more sources than microphone observations, is presented. The algorithm is based upon a generative probabilistic model that fuses time-delay of arrival (TDOA) information with prior information about the speakers and application, to produce an optimal estimate of the underlying speech sources. Simulation results are presented for the case of two, three and four underlying sources and two microphone observations corrupted by noise. The resulting SNR gains (32 dB with two sources, 23 dB with three sources, and 16 dB with four sources) are significantly higher than previous speech separation techniques.
Interactive Decoding of a Broadcast Message

We develop communication strategies for the rate-constrained interactive decoding of a message broadcast to a group of interested users. This situation diers from the relay channel in that all users are interested in the transmitted message, and from the broadcast channel because no user can decode on its own. We focus on two-user scenarios, and describe a baseline strategy that uses ideas of coding with decoder side information. One user acts initially as a relay for the other. That other user then decodes the message and sends back random parity bits, enabling the first user to decode. We show how to improve on this scheme’s performance through a conversation consisting of multiple rounds of discussion. While there are now more messages, each message is shorter, lowering the overall rate of the conversation. Such multi-round conversations can be more ecient because earlier messages serve as side information known at both encoder and decoder. We illustrate these ideas for binary erasure channels. We show that multi-round conversations can decode using less overall rate than is possible with the single-round scheme.
Robust variational speech separation using fewer microphones than speakers

A variational inference algorithm for robust speech separation, capable of recovering the underlying speech sources even in the case of more sources than microphone observations, is presented. The algorithm is based upon an generative probabilistic model that fuses time-delay of arrival (TDOA) information with prior information about the speakers and application, to produce an optimal estimate of the underlying speech sources. Simulation results are presented for the case of two, three and four underlying sources and two microphones observations corrupted by noise. The resulting SNR gains (32 dB with two sources, 23 dB with three sources, and 16 dB with four sources) are significantly higher than previous speech separation techniques.
Denoising and Untangling Graphs Using Degree Priors

This paper addresses the problem of untangling hidden graphs from a set of noisy detections of undirected edges. We present a model of the generation of the observed graph that includes degree-based structure priors on the hidden graphs. Exact inference in the model is intractable; we present an efficient approximate inference algorithm to compute edge appearance posteriors. We evaluate our model and algorithm on a biological graph inference problem.
An iterative dynamic programming approach to 2-D phase unwrapping

We propose a novel Bayesian approach to 2-D phase unwrapping. Modeled as a first-order Gaussian Markov random field, the unwrapped phase is estimated according to a maximum a posteriori (MAP) rule. The estimate is made through a form of 2-D dynamic programming, using a series of row-by- row or column-by-column 1-D dynamic programming optimiza- tions. Increasing the number of states in the dynamic system can improve the unwrapping performance, but also increases the com- putational complexity. Due to this trade-off, a structured iterated conditional mode (SICM) is used to achieve good performance without examining a large number of states in each iteration. A row-by-row followed by column-by-column raster scan takes pre- vious estimates into account through a weighting. Other raster scans are also possible. The approach can be implemented effi- ciently in terms of memory usage due to the recyclable memory of dynamic programming. An example of the approach with seven states is given. Experimental results are compared to other algo- rithms including the least-squares method, the branch-cut method and Flynn's method, using interferometric SAR data. The new SICM algorithm is seen to be superior.
Learning the "Epitome" of an Image

Estimating and visualizing high-order statistics of multivariate data is important for analysis, synthesis and visualization in science and engineering. Often, data consists of measurements on an underlying domain, such as space or time. Examples include images, audio signals and text, where the domains are 2-D space, 1-D time and 1-D symbol index. We introduce a model called the “epitome” that can simultaneously represent multi-scale high-order statistics as a set of parameters on the same domain as the input data. A cost function measures how well multi-scale patches drawn from the input data match the epitome and this cost function can be optimized efficiently using the EM algorithm. Our technique reduces a large number of high-order statistics to an intuitive, compact representation that is suitable for a variety of data processing applications. We demonstrate our method using problems of object detection, texture segmentation and image retrieval.
Analysis of an iterative dynamic programming approach to 2-D phase unwrapping

We consider a novel Bayesian approach to 2-D phase unwrapping. The phase is unwrapped according to a maximum a posteriori (MAP) rule, where the estimate is made through a form of 2-D dynamic programming. The approach uses structured iterated conditional modes to achieve good performance without examining a large number of states in the dynamic system. We analyze the performance of the approach by transforming the problem to one of decoding a convolutional code. An example with seven states in the dynamic program is given. We derive an approximate upper bound for the probability of pixel error based on a Gaussian Markov random field model. Monte Carlo simulation results show that the bound offers a good approximation to the probability of error. A comparison with other phase unwrapping techniques on a real data set suggests that the new approach is superior.
Accounting for uncertainity in observations: A new paradigm for Robust Automatic Speech Recognition

We introduce a new paradigm for Robust Automatic Speech Recognition that directly incorporates information about the uncertainty introduced by environmental noise. In contrast to the feature cleaning and model adaptation paradigms, where the noise compensation mechanism is separate from the recognizer, the new paradigm unifies the noise compensation mechanism and the recognizer. The Algonquin framework serves to demonstrate the importance of retaining soft information, i.e. information about the degree of uncertainty in the observations. The Algonquin framework employs Gaussian mixture models to model both noise and speech. Uncertainty introduced by the noise process is captured by the variance of the noise model. The Algonquin framework also allows us to isolate the effect of retaining or discarding soft information. Our initial results indicate that substantial improvements in recognition rates can be achieved through the use of soft information.
A Robust and Efficient Method to Unwrap MR Phase Images

L. Ying, J. X. Ji, D. C. Munson, Jr. , Z. P. Liang, R. Koetter, B. J. Frey University of Illinois at Urbana Champaign, Urbana, IL, United States, University of Toronto, Toronto, Ontario, Canada SYNOPSIS Phase unwrapping finds many important applications in MRI, ranging from field mapping to flow imaging. In this paper, we propose a novel approach to 2-D phase unwrapping, which models the true phase as a Gaussian Markov random field. The phase is unwrapped using the maximum a posteriori (MAP) rule. The underlying optimization problem is solved using a two-dimensional dynamic programming algorithm, which is efficient and robust to noise. The proposed algorithm has been tested using various MRI data (acquired using spin-echo and gradient-echo sequences), yielding excellent phase maps.
Learning Montages of Transformed Latent Images as Representations of Objects That Change in Appearance

This paper introduces a novel probabilistic model for representing objects that change in appearance as a result of changes in pose, due to small deformations of their sub-parts and the relative spatial transformation of sub-parts of the object. We call the model a probabilistic montage. The model is based upon the idea that an image can be represented as a montage using many, small transformed and cropped patches from a collection of latent images. The approach is similar to that which might be employed by a police artist who might represent an image of a criminal suspect's face using a montage of face parts cut out of a "library" of face parts. In contrast, for our model, we learn the library of small latent images from a set of examples of objects that are changing in shape. In our approach, first the image is divided into a grid of sub-images. Each sub-image in the grid acts as window that crops a piece out of one of a collection of slightly larger images possible for that location in the image. We illustrate various probability models that can be used to encode the appropriate relationships for latent images and cropping transformations among the different patches. In this paper we present the complete algorithm for a tree-structured model. We show how the approach and model are able to find representations of the appearance of full body images of people in motion. We show how our approach can be used to learn representations of objects in an "unsupervised" manner and present results using our model for recognition and tracking purposes in a "supervised" manner.
Noise robust speech recognition using Gaussian basis functions for non-linear likelihood function approximation

One approach to achieving noise and distortion robust speech recognition is to remove noise and distortion with algorithms of low complexity prior to the use of much higher complexity speech recognizers. This approach has been referred to as cleaning. In this paper we present an approach for speech cleaning using a time-varying, non-linear probabilistic model of a signals log Mel-filter-bank representation. We then present a new non-linear probabilistic inference technique and show results using this technique within the probabilistic cleaning model. In this approach we represent distributions for underlying noise, speech and channel characteristics as Gaussian mixtures and use Gaussian basis functions to model the non-linear likelihood function. This allows us to efficiently compute complex multi-modal probability distributions over speech and noise components of the underlying signal. We show how this method can be used to clean speech features and present results using the Aurora 2 speech recognizer trained on clean speech data. We present competitive initial results from a minimum mean square error version of this approach for a subset of the Aurora 2 noisy digits recognition tasks.
An iterative dynamic programming approach to 2-D phase unwrapping

We consider a novel Bayesian approach to 2-D phase unwrapping. The phase is unwrapped according to a maximum a posteriori (MAP) rule, where the estimate is made through a form of 2-D dynamic programming. The approach uses structured iterated conditional modes to achieve good performance without examining a large number of states in the dynamic system. We analyze the performance of the approach by transforming the problem to one of decoding a convolutional code. An example with seven states in the dynamic program is given. We derive an approximate upper bound for the probability of pixel error based on a Gaussian Markov random field model. Monte Carlo simulation results show that the bound offers a good approximation to the probability of error. A comparison with other phase unwrapping techniques on a real data set suggests that the new approach is superior.
Phase unwrapping by minimizing Kikuchi free energy

Phase unwrapping in 2-dimensional topologies is an important problem that has several applications in radar and satellite imaging. The s um product algorithm (belief propagation) gives excellent results for the phase unwrapping problem. In this work, we present a gradient smoothing technique that uses higher order surface models to produce very smooth surfaces and report an improvement in the solution obtained. In a recent important work, Yedidia et. al have showed the theoretical connections between belief propagation algorithms and free energy in statistical physics. Based on this, we present a model that uses the Kikuchi technique to compute better posterior marginals than those produced by sum product algorithm.
Fast Transformation-Invariant Factor Analysis

Dimensionality reduction techniques such as principal component analysis and factor analysis are used to discover a linear mapping between high dimensional data samples and points in a lower dimensional subspace. In , Jojic and Frey introduced mixture of transformation-invariant component analyzers (MTCA) that can account for global transformations such as translations and rotations, perform clustering and learn local appearance deformations by dimensionality reduction. However, due to enormous computational requirements of the EM algorithm for learning the model, O(N2) where N is the dimensionality of a data sample, MTCA was not practical for most applications. In this paper, we demonstrate how fast Fourier transforms can reduce the computation to the order of N log N. With this speedup, we show the effectiveness of MTCA in various applications - tracking, video textures, clustering video sequences, object recognition, and object detection in images.
Combination of statistical and rule-based approaches for spoken language understanding

A Natural User Interface (NUI), where a user can type or speak a request, is a good complement to the well-known Graphical User Interface (GUI). Accurately extracting user intent from such typed or spoken queries is a very difficult challenge. In this paper we evaluate several techniques to extract user intent from typed sentences in the context of the well-known Airline Travel Information (ATIS) domain, where we want to extract which of the possible tasks the user wants to do and the value of the slots associated to that task. In previous work we showed that a Semantic Context Free Grammar (CFG) semi-automatically derived from labeled data can offer very good results. In this paper we evaluate several statistical pattern recognition techniques including Support Vector Machines (SVM), Naive Bayes classifiers and task-dependent n-gram language models. These methods can yield a very low task classification error rate. If used in combination with our CFG system, they can also lead to very low slot error rates.
Speech recognition in adverse environments: a probabilistic approach

In this thesis I advocate a probabilistic view of robust speech recognition. I discuss the classification of distorted features using an optimal classifier, and I show how the generation of noisy speech can be represented as a generative graphical probability model. By doing so, my aim is to build a conceptual framework that provides a unified understanding of robust speech recognition, and to some extent bridges the gap between a purely signal processing viewpoint and the pattern classification or decoding viewpoint. The most tangible contribution of this thesis is the introduction of the Algonquin method for robust speech recognition. It exemplifies the probabilistic method and encompasses a number of novel ideas. For example, it uses a probability distribution to describe the relationship between clean speech, noise, channel and the resultant noisy speech. It employs a variational approach to find an approximation to the joint posterior distribution which can be used for the purpose of restoring the distorted observations. It also allows us to estimate the parameters of the environment using a Generalized EM method. Another important contribution of this thesis is a new paradigm for robust speech recognition, which we call uncertainty decoding. This new paradigm follows naturally from the standard way of performing inference in the graphical probability model that describes noisy speech generation.
Introduction to the special issue on codes on graphs and iterative algorithms

In the 50 years since Shannon determined the capacity of ergodic channels, the construction of capacity-approaching coding schemes has been the supreme goal of coding research. Finally today, we know of practical codes and decoding algorithms that can closely approach the channel capacity of some classical memoryless channels. It is a remarkable fact motivating this special issue that all known practical, capacity-approaching coding schemes are now understood to be codes defined on graphs, together with the associated iterative decoding algorithms.
Factor graphs and the sum-product algorithm

Algorithms that must deal with complicated global functions of many variables often exploit the manner in which the given functions factor as a product of "local" functions, each of which depends on a subset of the variables. Such a factorization can be visualized with a bipartite graph that we call a factor graph, In this tutorial paper, we present a generic message-passing algorithm, the sum-product algorithm, that operates in a factor graph. Following a single, simple computational rule, the sum-product algorithm computes-either exactly or approximately-various marginal functions derived from the global function. A wide variety of algorithms developed in artificial intelligence, signal processing, and digital communications can be derived as specific instances of the sum-product algorithm, including the forward/backward algorithm, the Viterbi algorithm, the iterative "turbo" decoding algorithm, Pearl's (1988) belief propagation algorithm for Bayesian networks, the Kalman filter, and certain fast Fourier transform (FFT) algorithms.
Fast, Large-Scale Transformation-Invariant Clustering

In previous work on "transformed mixtures of Gaussians" and "transformed hidden Markov models", we showed how the EM algorithm in a discrete latent variable model can be used to jointly normalize data (e.g., center images, pitch-normalize spectrograms) and learn a mixture model of the normalized data. The only input to the algorithm is the data, a list of possible transformations, and the number of clusters to find. The main criticism of this work was that the exhaustive computation of the posterior probabilities over transformations would make scaling up to large feature vectors and large sets of transformations intractable. Here, we describe how a tremendous speed-up is acheived through the use of a variational technique for decoupling transformations, and a fast Fourier transform method for computing posterior probabilities. For N × N images, learning C clusters under N rotations, N scales, N x-translations and N y-translations takes only (C + 2 log N)N2 scalar operations per iteration. In contrast, the original algorithm takes CN6 operations to account for these transformations. We give results on learning a 4-component mixture model from a video sequence with frames of size 320×240. The model accounts for 360 rotations and 76,800 translations. Each iteration of EM takes only 10 seconds per frame in MATLAB, which is over 5 million times faster than the original algorithm.
ALGONQUIN: iterating laplace's method to remove multiple types of acoustic distortion for robust speech recognition

One approach to robust speech recognition is to use a simple speech model to remove the distortion, before applying the speech recognizer. Previous attempts at this approach have relied on unimodal or point estimates of the noise for each utterance. In challenging acoustic environments, e.g., an airport, the spectrum of the noise changes rapidly during an utterance, making a point estimate a poor representation. We show how an iterative form of Laplace’s method can be used to estimate the clean speech, using a time-varying probability model of the log-spectra of the clean speech, noise and channel distortion. We use this method, called ALGONQUIN, to denoise speech features and then feed these features into a large vocabulary speech recognizer whose WER on the clean Wall Street Journal data is 4.9%. When 10 dB of noise consisting of an airplane engine shutting down is added to the data, the recognizer obtains a WER of 28.8%. ALGONQUIN reduces the WER to 12.6%, well below the WER of 25.0% obtained by our spectral subtraction algorithm, and close to the WER of 9.7% obtained by the slow procedure of retraining the recognizer on training data corrupted by the exact same noise. In fact, if ALGONQUIN is used to denoise the noisy training data before the recognizer is retrained, the WER is improved to 8.5%. For 10 dB of additive uniform white noise, our spectral subtraction algorithm reduces the WER from 55.1% to 33.8%. ALGONQUIN reduces the WER to 14.2%. The recognizer trained on noisy data obtains a WER of 14%, whereas the recognizer trained on noisy data denoised by ALGONQUIN obtains a WER of 9.9%.