nils-herrmann/clean_scirepeval huggingface dataset:
Nonlinear Blind Source Separation by Integrating Independent Component Analysis and Slow Feature Analysis

In contrast to the equivalence of linear blind source separation and linear independent component analysis it is not possible to recover the original source signal from some unknown nonlinear transformations of the sources using only the independence assumption. Integrating the objectives of statistical independence and temporal slowness removes this indeterminacy leading to a new method for nonlinear blind source separation. The principle of temporal slowness is adopted from slow feature analysis, an unsupervised method to extract slowly varying features from a given observed vectorial signal. The performance of the algorithm is demonstrated on nonlinearly mixed speech data.
CuBICA: independent component analysis by simultaneous third- and fourth-order cumulant diagonalization

CuBICA, which is an improved method for independent component analysis (ICA) based on the diagonalization of cumulant tensors is proposed. It is based on Comon's algorithm, but it takes third- and fourth-order cumulant tensors into account simultaneously. The underlying contrast function is also mathematically much simpler and has a more intuitive interpretation. It is therefore easier to optimize and approximate. A comparison with Comon's and three other ICA algorithms on different data sets demonstrates its performance.
What is the functional role of adult neurogenesis in the hippocampus

The dentate gyrus is part of the hippocampal memory system and special in that it generates new neurons throughout life. Here we discuss the question of what the functional role of these new neurons might be. Our hypothesis is that they help the dentate gyrus to avoid the problem of catastrophic interference when adapting to new environments. We assume that old neurons are rather stable and preserve an optimal encoding learned for known environments while new neurons are plastic to adapt to those features that are qualitatively new in a new environment. A simple network simulation demonstrates that adding new plastic neurons is indeed a successful strategy for adaptation without catastrophic interference.
Independent Slow Feature Analysis and Nonlinear Blind Source Separation

We present independent slow feature analysis as a new method for nonlinear blind source separation. It circumvents the indeterminacy of nonlinear independent component analysis by combining the objectives of statistical independence and temporal slowness. The principle of temporal slowness is adopted from slow feature analysis, an unsupervised method to extract slowly varying features from a given observed vectorial signal. The performance of the algorithm is demonstrated on nonlinearly mixed speech data.
Estimating Driving Forces of Nonstationary Time Series with Slow Feature Analysis Laurenz Wiskott Institute for Theoretical Biology

Slow feature analysis (SFA) is a new technique for extracting slowly varying features from a quickly varying signal. It is shown here that SFA can be applied to nonstationary time series to estimate a single underlying driving force with high accuracy up to a constant oset and a factor. Examples with a tent map and a logistic map illustrate the performance.
Slow Feature Analysis: A Theoretical Analysis of Optimal Free Responses

Temporal slowness is a learning principle that allows learning of invariant representations by extracting slowly varying features from quickly varying input signals. Slow feature analysis (SFA) is an efficient algorithm based on this principle and has been applied to the learning of translation, scale, and other invariances in a simple model of the visual system. Here, a theoretical analysis of the optimization problem solved by SFA is presented, which provides a deeper understanding of the simulation results obtained in previous studies.
Applying Slow Feature Analysis to Image Sequences Yields a Rich Repertoire of Complex Cell Properties

We apply Slow Feature Analysis (SFA) to image sequences generated from natural images using a range of spatial transformations. An analysis of the resulting receptive fields shows that they have a rich spectrum of invariances and share many properties with complex and hypercomplex cells of the primary visual cortex. Furthermore, the dependence of the solutions on the statistics of the transformations is investigated.
An Improved Cumulant Based Method for Independent Component Analysis

An improved method for independent component analysis based on the diagonalization of cumulant tensors is proposed. It is based on Comon's algorithm  but it takes third- and fourth-order cumulant tensors into account simultaneously. The underlying contrast function is also mathematically much simpler and has a more intuitive interpretation. It is therefore easier to optimize and approximate. A comparison with Comon's algorithm, JADE [2] and FastICA  on different data sets demonstrates its performance.
Objekterkennung in einem selbstorganisierenden neuronalen System

Wir stellen hier ein System fur invariante und robuste Erkennung von Objekten aus Kamerabildern vor. Das System beansprucht, sowohl ein Modell fur biologisches Objektsehen (zumindest fur eine ontogenetisch fruhe Form davon) zu sein, als auch auf der Hohe des technischen Fortschritts zu stehen.  Modell basiert auf den Prinzipien zeitlicher Merkmalsbindung und schneller reversibler synaptischer Plastizitat. Objekte werden in Form zweidimensionaler Ansichten gespeichert. Diese werden kompetitiv an Testbilder angepast. Wahrend des Anpassungsprozesses werden vollstandige Matrizen dynamischer Bindungen zwischen dem Bild und allen Modellen durch einen Prozes rascher Selbstorganisation verfeinert, wobei im Endzustand nur noch einander entsprechende Punkte im Bild und in den Objektmodellen (besonders dem erkannten) verbunden sind. Als Datenformat fur die Reprasentation von Bildern benutzen wir lokale Mengen („jets“) von Gabor-basierten Wavelets. Wir demonstrieren das Leistungsvermogen unseres Systems, indem wir es menschliche Gesichter wiedererkennen lassen, u.zw. aus Datenbasen von mehr als hundert Bildern. Das System ist invariant hinsichtlich retinaler Position und es ist robust hinsichtlich Kopfdrehung, Skalierung, Gesichtsdeformation und Beleuchtung.
Learning Invariance Manifolds

A new algorithm for learning invariance manifolds is introduced that allows a neuron to learn a non-linear transfer function to extract invariant or rather slowly varying features from a vectorial input sequence. This is generalized to a group of neurons, referred to as a Gibson-clique, to learn slowly varying features that are uncorrelated. Since the transfer functions are non-linear, this technique can be applied iteratively to learn more and more complex and invariant features in a hierarchical architecture. Two simple examples demonstrate the general properties of the learning algorithm.
Phantom Faces for Face Analysis Phantom Faces for Face Analysis

The system presented is part of a general object recognition system. Images of faces are represented as graphs, labeled with topographical information and local features. New graphs of faces are generated by an elastic graph matching procedure comparing the new face with a composition of stored graphs: the face bunch graph. The result of this matching process can be used to generate composite images of faces and to determine facial attributes represented in the bunch graph, such as sex or the presence of glasses or a beard.
Phantom Faces for Face Analysis

The system presented here is a specialized version of a general object recognition system. Images of faces are represented by graphs, labeled with topographical information and local features. New graphs of faces are generated by an elastic graph matching procedure comparing the new face with a set of stored model graphs: the face bunch graph. The result of this matching process can be used to generate composite images of faces and to determine facial attributes represented in the face bunch graph, such as sex or the presence of glasses or a beard. The performance of the system is comparable to that of other systems.
Face recognition by elastic bunch graph matching

We present a system for recognizing human faces from single images out of a large database containing one image per person. Faces are represented by labeled graphs, based on a Gabor wavelet transform. Image graphs of new faces are extracted by an elastic graph matching process and can be compared by a simple similarity function. The system differs from Lades et al. (1993) in three respects. Phase information is used for accurate node positioning. Object-adapted graphs are used to handle large rotations in depth. Image graph extraction is based on a novel data structure, the bunch graph, which is constructed from a small set of sample image graphs.
Face Recognition by Elastic Bunch Graph Matching

We present a system for recognizing human faces from single images out of a large database containing one image per person. Faces are represented by labeled graphs, based on a Gabor wavelet transform. Image graphs of new faces are extracted by an elastic graph matching process and can be compared by a simple similarity function. The system differs from the preceding one (Lades et al., 1993) in three respects. Phase information is used for accurate node positioning. Object-adapted graphs are used to handle large rotations in depth. Image graph extraction is based on a novel data structure, the bunch graph, which is constructed from a small get of sample image graphs.
Phantom faces for face analysis

The system presented is a specialized version of a general object recognition system. Images of faces are represented as graphs, labeled with topographical information and local features. New graphs of faces are generated by an elastic graph matching procedure comparing the new face with a set of stored model graphs: the face bunch graph. The result of this matching process can be used to generate composite images of faces and to determine facial attributes represented in the face bunch graph, such as sex or the presence of glasses or a beard.
Segmentation from Motion: Combining Gabor- and Mallat-Wavelets to Overcome Aperture and Correspondence Problem

A segmentation-from-motion algorithm is presented, which is designed to be part of a general object recognition system. The key idea is to integrate information from Gabor- and Mallat-wavelet transform to overcome the aperture and the correspondence problem. The assumption is made that objects move fronto-parallel. Gabor-wavelet responses allow precise estimation of image flow vectors with low spatial resolution. A histogram over this image flow field is evaluated, its local maxima providing motion hypotheses. These serve to reduce the correspondence problem on the Mallat-wavelet transform, which provides the required high resolution. The segmentation reliability is improved by integration over time. The system can segment even small, disconnected, and openworked objects of arbitrary number, such as dot patterns. Several examples demonstrate the performance of the system and show, that the algorithm behaves reasonably even if the assumption of fronto-parallel motion is strongly violated.
Face Recognition by Elastic Bunch Graph Matching

We present a system for recognizing human faces from single images out of a large database with one image per person. The task is difficult because of image variation in terms of position, size, expression, and pose. The system collapses most of this variance by extracting concise face descriptions in the form of image graphs. In these, fiducial points on the face (eyes, mouth etc.) are described by sets of wavelet components (jets). Image graph extraction is based on a novel approach, the bunch graph, which is constructed from a small set of sample image graphs. Recognition is based on a straight-forward comparison of image graphs. We report recognition experiments on the FERET database and the Bochum database, including recognition across pose.
Reconstruction from Graphs Labeled with Responses of Gabor Filters

The work presented is part of a larger effort to build a general object recognition system. Objects as well as human faces are represented by graphs labeled with Gabor filter responses. We describe an optimal method to reconstruct images from such graphs. Two examples of how this can be used to analyze the object representation or to compensate for its deficiencies are presented. Since the reconstruction method is formulated generally for an arbitray set of linear filters, it can also be applied to data produced by other systems, artificial or biological.
Recognizing Faces by Dynamic Link Matching

Dynamic Link Matching is a neural dynamics for translation invariant object recognition that is robust against distortion. We here demonstrate human face recognition against a gallery of 112 neutral frontal view faces. Probe images are distorted due to rotation in depth and changing facial expression. Probe images and gallery models are represented by layers of neurons interpreted as labeled graphs. Nodes are labeled by local features based on the Gabor wavelet transform. Probe images are matched to the gallery of face models by Dynamic Link Matching. Concurrently with the matching process a simple winner-take-all mechanism identiies the correct model. A dynamic window of attention restricts the match to the part of the image occupied by the face.
Internal Report 96{05 Face Recognition by Dynamic Link Matching Face Recognition by Dynamic Link Matching

We present here a system for invariant and robust recognition of objects from camera images. The system aspires both to be a model for biological object vision (at least an ontogenetically early form of it) and to be at the cutting edge of technological achievement. Our model is based on the principles of temporal feature binding and dynamic link matching. Objects are stored in the form of two-dimensional aspects. These are competitively matched against current images. During the matching process, complete matrices of dynamic links between the image and all models are reened by a process of rapid self-organization, the nal state connecting only corresponding points in image and object models. As data format for representing images we use local sets (\jets") of Gabor-based wavelets. We have tested the performance of our system by having it recognize human faces against data bases of more than one hundred images. The system is invariant with respect to retinal position, and it is robust with respect to head rotation, scale, facial deformation and illumination. The source code for this model is available by anonymous ftp 1 and respective simulation instructions are given in this report.
Face recognition by dynamic link matching

We present a neural system for the recognition of objects from realistic images, together with results of tests of face recognition from a large gallery. The system is inherently invariant with respect to shift, and is robust against many other variations, most notably rotation in depth and deformation. The system is based on Dynamic Link Matching. It consists of an image domain and a model domain, which we tentatively identify with primary visual cortex and infero-temporal cortex. Both domains have the form of neural sheets of hypercolumns, which are composed of simple feature detectors (modeled as Gabor-based wavelets). Each object is represented in memory by a separate model sheet, that is, a two-dimensional array of features. The match of the image to the models is performed by network self-organization, in which rapid reversible synaptic plasticity of the connections (\dynamic links") between the two domains is controlled by signal correlations, which are shaped by xed inter-columnar connections and by the dynamic links themselves. The system requires very little genetic or learned structure, relying essentially on the rules of rapid synaptic plasticity and the a priori constraint of preservation of topography to nd matches. This constraint is encoded within the neural sheets with the help of lateral connections, which are excitatory over short range and inhibitory over long range.
Face Recognition and Gender determination

The system presented here is a specialized version of a general object recognition system. Images of faces are represented as graphs, labeled with topographical information and local templates. Different poses are represented by different graphs. New graphs of faces are generated by an elastic graph matching procedure comparing the new face with a set of precomputed graphs: the "general face knowledge". The final phase of the matching process can be used to generate composite images of faces and to determine certain features represented in the general face knowledge, such as gender or the presence of glasses or a beard. The graphs can be compared by a similarity function which makes the system efficient in recognizing faces.
Statement of Research Intent

Advances in digital imaging have made it possible to put cameras almost everywh ere, from cellular phones to satellites. These cameras enable us to generate large numbers of pictures of almost anything in the world. Harnessing this imaging capability in applications, such as computer g raphics, monitoring, or computer vision, will require flexible models of images and the scenes that gen erat them. My goal is to develop machine learning techniques for building tractable models of images fo r applications in graphics, image processing, data mining, and image understanding. Machine learning is an exciting tool for solving computer vision problems beca use it provides a means for building more accurate models of images and the scenes pictured in those ima ges. My work in recovering the intrinsic characteristics of a scene, described below, is a good e xample of how applying learning techniques can lead to significantly improved results on a classic vision proble m.
Recovering shading and reflectance from a single image

Given an input image, decompose it into an image representing the shading of the scene and a second image representing the reflectance of every point in the scene. Finding the shading image can be thought of as re-rendering the image as if every surface shown in the image was made of the same material, such as plaster.  1 shows an example image that has been decomposed into shading and reflectance images.
Embedded trees: estimation of Gaussian Processes on graphs with cycles

Graphical models provide a powerful general framework for encoding the structure of large-scale estimation problems. However, the graphs describing typical real-world phenomena contain many cycles, making direct estimation procedures prohibitively costly. In this paper, we develop an iterative inference algorithm for general Gaussian graphical models. It operates by exactly solving a series of modified estimation problems on spanning trees embedded within the original cyclic graph. When these subproblems are suitably chosen, the algorithm converges to the correct conditional means. Moreover, and in contrast to many other iterative methods, the tree-based procedures we propose can also be used to calculate exact error variances. Although the conditional mean iteration is effective for quite densely connected graphical models, the error variance computation is most efficient for sparser graphs. In this context, we present a modeling example suggesting that very sparsely connected graphs with cycles may provide significant advantages relative to their tree-structured counterparts, thanks both to the expressive power of these models and to the efficient inference algorithms developed herein. The convergence properties of the proposed tree-based iterations are characterized both analytically and experimentally. In addition, by using the basic tree-based iteration to precondition the conjugate gradient method, we develop an alternative, accelerated iteration that is finitely convergent. Simulation results are presented that demonstrate this algorithm's effectiveness on several inference problems, including a prototype distributed sensing application.
Nonlinear Manifold Learning Part II 6 . 454 Summary

Manifold learning addresses the problem of finding low–dimensional structure within collections of high–dimensional data. Recent interest in this problem was motivated by the development of a pair of algorithms, locally linear embedding (LLE)  and isometric feature mapping (IsoMap) . Both methods use local, linear relationships to derive global, nonlinear structure, although their specific assumptions and optimization criteria differ. For an introduction to these algorithms, as well as further motivation of the manifold learning problem, see . In this survey, we discuss three manifold learning algorithms which adopt the basic structure of LLE, but attempt to address some of its shortcomings. The first, Laplacian eigenmaps , is primarily interesting because it provides a new theoretical framework for understanding LLE. This framework points the way to the Hessian eigenmaps  algorithm, which explicitly attempts to estimate, and minimize, the local curvature of the embedding function. Interestingly, this Hessian extension to LLE is asymptotically correct for a strictly larger class of embeddings than any previously known algorithm. Finally, the charting algorithm  casts manifold learning as a density estimation problem, thereby adding robustness to noisy or sparsely sampled data.
Statistical and Information-Theoretic Methods for Self-Organization and Fusion of Multimodal, Networked Sensors

The appeal of distributed sensing and computation is matched by the formidable challenges it presents in terms of estimation and communication. Applications range from military surveillance to collaborative office environments. Despite the attractiveness of exploiting networks of low-power and low-cost sensors, how to do so is a difficult problem. In this paper, we adopt a statistical viewpoint of such networks, and identify three key challenges. The first is to develop principled methods for low-level fusion of sensors measuring different modalities. We discuss an information-theoretic approach to sensor fusion, and present experimental results using audio and video data. The core component of this method is the learning of a nonparametric joint statistical model for the sensing modes. Secondly, we discuss how one might apply such a sensor fusion algorithm to acquire the relative geometry of a network of sensors using passively-sensed data. Specifically, we show how the fusion method previously developed can be used to find correspondences between pairs of long-baseline sensors. Finding such correspondences is, in general, the starting point for recovering the geometry. Finally, we discuss two iterative algorithms for performing inference on graphical models with cycles. Such models provide a flexible framework for constructing globally consistent statistical models from a set of local interactions. Importantly, the algorithms that we present allow information to be transmitted and processed in a distributed manner.
Tree-Based Modeling and Estimation of Gaussian Processes on Graphs with Cycles

We present the embedded trees algorithm, an iterative technique for estimation of Gaussian processes defined on arbitrary graphs. By exactly solving a series of modified problems on embedded spanning trees, it computes the conditional means with an efficiency comparable to or better than other techniques. Unlike other methods, the embedded trees algorithm also computes exact error covariances. The error covariance computation is most efficient for graphs in which removing a small number of edges reveals an embedded tree. In this context, we demonstrate that sparse loopy graphs can provide a significant increase in modeling power relative to trees, with only a minor increase in estimation complexity.