nils-herrmann/clean_scirepeval huggingface dataset:
Recognizing Hand-written Digits Using Hierarchical Products of Experts

The product of experts learning procedure  can discover a set of stochastic binary features that constitute a non-linear generative model of handwritten images of digits. The quality of generative models learned in this way can be assessed by learning a separate model for each class of digit and then comparing the unnormalized probabilities of test images under the 10 different class-specific models. To improve discriminative performance, it is helpful to learn a hierarchy of separate models for each digit class. Each model in the hierarchy has one layer of hidden units and the nth level model is trained on data that consists of the activities of the hidden units in the already trained (n - 1)th level model. After training, each level produces a separate, unnormalized log probabilty score. With a three-level hierarchy for each of the 10 digit classes, a test image produces 30 scores which can be used as inputs to a supervised, logistic classification network that is trained on separate data. On the MNIST database, our system is comparable with current state-of-the-art discriminative methods, demonstrating that the product of experts learning procedure can produce effective generative models of high-dimensional data.
Using Free Energies to Represent Q-values in a Multiagent Reinforcement Learning Task

The problem of reinforcement learning in large factored Markov decision processes is explored. The Q-value of a state-action pair is approximated by the free energy of a product of experts network. Network parameters are learned on-line using a modified SARSA algorithm which minimizes the inconsistency of the Q-values of consecutive state-action pairs. Actions are chosen based on the current value estimates by fixing the current state and sampling actions from the network using Gibbs sampling. The algorithm is tested on a co-operative multi-agent task. The product of experts model is found to perform comparably to table-based Q-learning for small instances of the task, and continues to perform well when the problem becomes too large for a table-based representation.
Learning Distributed Representations by Mapping Concepts and Relations into a Linear Space

Linear Relational Embedding is a method of learning a distributed representation of concepts from data consisting of binary relations between concepts. Concepts are represented as vectors, binary relations as matrices, and the operation of applying a relation to a concept as a matrix-vector multiplication that produces an approximation to the related concept. A representation for concepts and relations is learned by maximizing an appropriate discriminative goodness function using gradient ascent. On a task involving family relationships, learning is fast and leads to good generalization.
Using EM for Reinforcement Learning

We discsus Hinton’s (1989) relative payoff procedure (RPP), a static reinforcement learning algorithm whose foundation is not stochastic gradient ascent. We show circumstances under which applying the RPP is guaranteed to increase the mean return, even though it can make large changes in the values of the parameters. The proof is based on a mapping between the RPP and a form of the expectation-maximisation procedure of Dempster, Laird & Rubin (1976).
Rate-coded Restricted Boltzmann Machines for Face Recognition

We describe a neurally-inspired, unsupervised learning algorithm that builds a non-linear generative model for pairs of face images from the same individual. Individuals are then recognized by finding the highest relative probability pair among all pairs that consist of a test image and an image whose identity is known. Our method compares favorably with other methods in the literature. The generative model consists of a single layer of rate-coded, non-linear feature detectors and it has the property that, given a data vector, the true posterior probability distribution over the feature detector activities can be inferred rapidly without iteration or approximation. The weights of the feature detectors are learned by comparing the correlations of pixel intensities and feature activations in two phases: When the network is observing real data and when it is observing reconstructions of real data generated from the feature activations.
Extracting distributed representations of concepts and relations from positive and negative propositions

Linear relational embedding (LRE) was introduced previously by the authors (1999) as a means of extracting a distributed representation of concepts from relational data. The original formulation cannot use negative information and cannot properly handle data in which there are multiple correct answers. In this paper we propose an extended formulation of LRE that solves both these problems. We present results in two simple domains, which show that learning leads to good generalization.
Modeling High-Dimensional Data by Combining Simple Experts

It is possible to combine multiple non-linear probabilistic models of the same data by multiplying the probability distributions together and then renormalizing. A “productof experts”is a very efficient way to model data that simultaneously satisfies many different constraints. It is difficult to fit a product of experts to data using maximum likelihood because the gradient of the log likelihood is intractable, but there is an efficient way of optimizing a different objective function and this produces good models of high-dimensional data.
Products of experts

It is possible to combine multiple probabilistic models of the same data by multiplying the probabilities together and then renormalizing. This is a very efficient way to model high-dimensional data which simultaneously satisfies many different low dimensional constraints. Each individual expert model can focus on giving high probability to data vectors that satisfy just one of the constraints. Data vectors that satisfy this one constraint but violate other constraints will be ruled out by their low probability under the other expert models. Training a product of models appears difficult because, in addition to maximizing the probabilities that the individual models assign to the observed data, it is necessary to make the models disagree on unobserved regions of the data space. However, if the individual models are tractable there is a fairly efficient way to train a product of models. This training algorithm suggests a biologically plausible way of learning neural population codes.
Fast Neural Network Emulation and Control of Dynamical Systems

Computer animation through the numerical simulation of physics-based graphics models offers unsurpassed realism, but it can be computationally demanding. This paper demonstrates the possibility of replacing the numerical simulation of nontrivial dynamic models with a dramatically more efficient "NeuroAnimator" that exploits neural networks. NeuroAnimators are automatically trained off-line to emulate physical dynamics through the observation of physics-based models in action. Depending on the model, its neural network emulator can yield physically realistic animation one or two orders of magnitude faster than conventional numerical simulation. We demonstrate NeuroAnimators for a variety of physics-based models. By exploiting the network structure of the NeuroAnimator, we also introduce a remarkably fast algorithm for learning controllers that enables either complex physics-based models or their neural network emulators to synthesize motions satisfying prescribed animation goals.
Cascaded redundancy reduction

We describe a method for incrementally constructing a hierarchical generative model of an ensemble of binary data vectors. The model is composed of stochastic, binary, logistic units. Hidden units are added to the model one at a time with the goal of minimizing the information required to describe the data vectors using the model. In addition to the top-down generative weights that define the model, there are bottom-up recognition weights that determine the binary states of the hidden units given a data vector. Even though the stochastic generative model can produce each data vector in many ways, the recognition model is forced to pick just one of these ways. The recognition model therefore underestimates the ability of the generative model to predict the data, but this underestimation greatly simplifies the process of searching for the generative and recognition weights of a new hidden unit.
Coaching variables for regression and classification

In a regression or classification setting where we wish to predict Y from x1,x2,..., xp, we suppose that an additional set of ‘coaching’ variables z1,z2,..., zm are available in our training sample. These might be variables that are difficult to measure, and they will not be available when we predict Y from x1,x2,..., xp in the future. We consider two methods of making use of the coaching variables in order to improve the prediction of Y from x1,x2,..., xp. The relative merits of these approaches are discussed and compared in a number of examples.
Glove-TalkII-a neural-network interface which maps gestures to parallel formant speech synthesizer controls

Glove-TalkII is a system which translates hand gestures to speech through an adaptive interface. Hand gestures are mapped continuously to ten control parameters of a parallel formant speech synthesizer. The mapping allows the hand to act as an artificial vocal tract that produces speech in real time. This gives an unlimited vocabulary in addition to direct control of fundamental frequency and volume. Currently, the best version of Glove-TalkII uses several input devices (including a Cyberglove, a ContactGlove, a three-space tracker, and a foot pedal), a parallel formant speech synthesizer, and three neural networks. The gesture-to-speech task is divided into vowel and consonant production by using a gating network to weight the outputs of a vowel and a consonant neural network. The gating network and the consonant network are trained with examples from the user. The vowel network implements a fixed user-defined relationship between hand position and vowel sound and does not require any training examples from the user. Volume, fundamental frequency, and stop consonants are produced with a fixed mapping from the input devices. One subject has trained to speak intelligibly with Glove-TalkII. He speaks slowly but with far more natural sounding pitch variations than a text-to-speech synthesizer.
learning can then be used to fine-tune the CD solution . 1 ON CONTRASTIVE DIVERGENCE LEARNING

During the course of this investigation, many observations were made about the estimation of the in vivo power spectrum as it applies to estimating the characteristic size of the tissue microstructure. In this final chapter, the conclusions derived from all of the observations are summarized. Then, future goals for extending the work and applying the conclusions are discussed. 9.1 Conclusions from Current Investigation For the estimation of the characteristic size of the tissue microstructure, the in vivo power spectrum can be determined to sufficient accuracy by estimating the total attenuation along the propagation path over the frequency range used to estimate the scatterer size. Hence, most of the work in this investigation attempted to solve for the scatterer size and total attenuation simultaneously. However, before solving for these two parameters, the effects of focusing on the estimation of the scatterer size were considered while assuming that the attenuation was known so that the results of the investigation could later be applied to clinically relevant fields. By assuming that the velocity potential fields near the focus could be modeled as a three-dimensional Gaussian distribution, focusing could be corrected by using a new generalized attenuation-compensation function that accounted for focusing, windowing, and attenuation. The new generalized attenuation-compensation function was then compared to the traditional attenuation-compensation functions that neglected focusing along the beam axis using computer simulations and phantom experiments. The generalized attenuation-compensation function yielded improvements in size estimation accuracy as high as 100% over the traditional attenuation-compensation functions. However, errors from the 155
Cascaded redundancy reduction.

We describe a method for incrementally constructing a hierarchical generative model of an ensemble of binary data vectors. The model is composed of stochastic, binary, logistic units. Hidden units are added to the model one at a time with the goal of minimizing the information required to describe the data vectors using the model. In addition to the top-down generative weights that define the model, there are bottom-up recognition weights that determine the binary states of the hidden units given a data vector. Even though the stochastic generative model can produce each data vector in many ways, the recognition model is forced to pick just one of these ways. The recognition model therefore underestimates the ability of the generative model to predict the data, but this underestimation greatly simplifies the process of searching for the generative and recognition weights of a new hidden unit.
Fast Neural Network Emulation of Dynamical Systems for Computer Animation

Computer animation through the numerical simulation of physics-based graphics models offers unsurpassed realism, but it can be computationally demanding. This paper demonstrates the possibility of replacing the numerical simulation of nontrivial dynamic models with a dramatically more efficient "NeuroAnimator" that exploits neural networks. NeuroAnimators are automatically trained off-line to emulate physical dynamics through the observation of physics-based models in action. Depending on the model, its neural network emulator can yield physically realistic animation one or two orders of magnitude faster than conventional numerical simulation. We demonstrate NeuroAnimators for a variety of physics-based models.
NeuroAnimator: fast neural network emulation and control of physics-based models

Animation through the numerical simulation of physicsbased graphics models offers unsurpassed realism, but it can be computationally demanding. Likewise, the search for controllers that enable physics-based models to produce desired animations usually entails formidable computational cost. This paper demonstrates the possibility of replacing the numerical simulation and control of dynamic models with a dramatically more efficient alternative. In particular, we propose the NeuroAnimator, a novel approach to creating physically realistic animation that exploits neural networks. NeuroAnimators are automatically trained off-line to emulate physical dynamics through the observation of physicsbased models in action. Depending on the model, its neural network emulator can yield physically realistic animation one or two orders of magnitude faster than conventional numerical simulation. Furthermore, by exploiting the network structure of the NeuroAnimator, we introduce a fast algorithm for learning controllers that enables either physics-based models or their neural network emulators to synthesize motions satisfying prescribed animation goals. We demonstrate NeuroAnimators for a variety of physics-based models. CR Categories: I.3.7 [Computer Graphics]: Three-Dimensional Graphics and Realism—Animation; I.6.8 [Simulation and Modeling]: Types of Simulation—
A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants

The EM algorithm performs maximum likelihood estimation for data in which some variables are unobserved. We present a function that resembles negative free energy and show that the M step maximizes this function with respect to the model parameters and the E step maximizes it with respect to the distribution over the unobserved variables. From this perspective, it is easy to justify an incremental variant of the EM algorithm in which the distribution for only one of the unobserved variables is recalculated in each E step. This variant is shown empirically to give faster convergence in a mixture estimation problem. A variant of the algorithm that exploits sparse conditional distributions is also described, and a wide range of other variant algorithms are also seen to be possible.
Glove-talk II - a neural-network interface which maps gestures to parallel formant speech synthesizer controls

Glove-Talk II is a system which translates hand gestures to speech through an adaptive interface. Hand gestures are mapped continuously to ten control parameters of a parallel formant speech synthesizer. The mapping allows the hand to act as an artificial vocal tract that produces speech in real time. This gives an unlimited vocabulary in addition to direct control of fundamental frequency and volume. Currently, the best version of Glove-Talk II uses several input devices, a parallel formant speech synthesizer, and three neural networks. The gesture-to-speech task is divided into vowel and consonant production by using a gating network to weight the outputs of a vowel and a consonant neural network. The gating network and the consonant network are trained with examples from the user. The vowel network implements a fixed user-defined relationship between hand position and vowel sound and does not require any training examples from the user. Volume, fundamental frequency, and stop consonants are produced with a fixed mapping from the input devices. With Glove-Talk II, the subject can speak slowly but with far more natural sounding pitch variations than a text-to-speech synthesizer.
Using Expectation-Maximization for Reinforcement Learning

We discuss Hinton's (1989) relative payoff procedure (RPP), a static reinforcement learning algorithm whose foundation is not stochastic gradient ascent. We show circumstances under which applying the RPP is guaranteed to increase the mean return, even though it can make large changes in the values of the parameters. The proof is based on a mapping between the RPP and a form of the expectation-maximization procedure of Dempster, Laird, and Rubin (1977).
Modeling the manifolds of images of handwritten digits

This paper describes two new methods for modeling the manifolds of digitized images of handwritten digits. The models allow a priori information about the structure of the manifolds to be combined with empirical data. Accurate modeling of the manifolds allows digits to be discriminated using the relative probability densities under the alternative models. One of the methods is grounded in principal components analysis, the other in factor analysis. Both methods are based on locally linear low-dimensional approximations to the underlying data manifold. Links with other methods that model the manifold are discussed.
A Mobile Robot That Learns Its Place

We show how a neural network can be used to allow a mobile robot to derive an accurate estimate of its location from noisy sonar sensors and noisy motion information. The robot's model of its location is in the form of a probability distribution across a grid of possible locations. This distribution is updated using both the motion information and the predictions of a neural network that maps locations into likelihood distributions across possible sonar readings. By predicting sonar readings from locations, rather than vice versa, the robot can handle the very nongaussian noise in the sonar sensors. By using the constraint provided by the noisy motion information, the robot can use previous readings to improve its estimate of its current location. By treating the resulting estimates as if they were correct, the robot can learn the relationship between location and sonar readings without requiring an external supervision signal that specifies the actual location of the robot. It can learn to locate itself in a new environment with almost no supervision, and it can maintain its location ability even when the environment is nonstationary.
Automated motif discovery in protein structure prediction

The protein structure prediction problem (PSP) is one of the central problems in molecular and structural biology. A computational method that could produce a correct detailed three-dimensional structural model for a protein, given its linear sequence of amino acids, would greatly accelerate progress in the biomedical sciences and industries. This thesis presents PSP as a combinatorial optimization problem, the most straightforward formulations of which require search of an exponentially-large conformation space and are known to be NP-Hard. This otherwise intractable search can in practice be reduced or eliminated through the discovery and use of motifs. Motifs are abstractions of observed patterns that encode structurally important relationships among constituent parts of a complex object like a protein tertiary structure. Motif discovery is accomplished by particular combinatorial search and statistical estimation methods. 
This thesis explores in detail two particular motif discovery subproblems, and discusses how their solutions can be applied to the overall structure prediction problem: (1) For a complex multi-stage prediction task, what makes a good intermediate representation language? We address this question by presenting and analyzing methods for the discovery of protein secondary structure classes that are more predictable from amino acid sequence than the standard classes of $\alpha$-helix, $\beta$-sheet, and "random coil". (2) Given a database of M objects, each characterized by values $a\sb{ij}\in {\cal A}\sb{j}$ for each of N discrete variables $\{c\sb{j}\}\sbsp{j=1}{N},$ return the list of "most interesting" higher-order features $\gamma\sb{l},$ i.e., sets of $k\sb{l}$ variables with highest estimated correlation, for any $2 \le k\sb{l} \le N$. In the PSP context, the problem is the detection of correlations between amino acid residues in an aligned set of evolutionarily-related protein sequences. We present and analyze a fast procedure, based on multinomial sampling and a novel coding scheme, that avoids the exhaustive search, prior limits on the order k, and exponentially large parameter space of other methods. 
The focus of this thesis is PSP, but the techniques and analysis are also aimed at wider application to other hard, multi-stage prediction problems.