nils-herrmann/clean_scirepeval huggingface dataset:
Are arm trajectories planned in kinematic or dynamic coordinates? An adaptation study

There are several invariant features of pointto-point human arm movements: trajectories tend to be straight, smooth, and have bell-shaped velocity profiles. One approach to accounting for these data is via optimization theory; a movement is specified implicitly as the optimum of a cost function, e.g., integrated jerk or torque change. Optimization models of trajectory planning, as well as models not phrased in the optimization framework, generally fall into two main groups-those specified in kinematic coordinates and those specified in dynamic coordinates. To distinguish between these two possibilities we have studied the effects of artificial visual feedback on planar two-joint arm movements. During self-paced point-to-point arm movements the visual feedback of hand position was altered so as to increase the perceived curvature of the movement. The perturbation was zero at both ends of the movement and reached a maximum at the midpoint of the movement. Cost functions specified by hand coordinate kinematics predict adaptation to increased curvature so as to reduce the visual curvature, while dynamically specified cost functions predict no adaptation in the underlying trajectory planner, provided the final goal of the movement can still be achieved. We also studied the effects of reducing the perceived curvature in transverse movements, which are normally slightly curved. Adaptation should be seen in this condition only if the desired trajectory is both specified in kinematic coordinates and actually curved. Increasing the perceived curvature of normally straight sagittal movements led to significant (P<0.001) corrective adaptation in the curvature of the actual hand movement; the hand movement became curved, thereby reducing the visually perceived curvature. Increasing the curvature of the normally curved transverse movements produced a significant (P<0.01) corrective adaptation; the hand movement became straighter, thereby again reducing the visually perceived curvature. When the curvature of naturally curved transverse movements was reduced, there was no significant adaptation (P>0.05). The results of the curvature-increasing study suggest that trajectories are planned in visually based kinematic coordinates. The results of the curvature-reducing study suggest that the desired trajectory is straight in visual space. These results are incompatible with purely dynamicbased models such as the minimum torque change model. We suggest that spatial perception-as mediated by vision-plays a fundamental role in trajectory planning.
Bayesian Learning in Undirected Graphical Models: Approximate MCMC Algorithms

Bayesian learning in undirected graphical models---computing posterior distributions over parameters and predictive quantities---is exceptionally difficult. We conjecture that for general undirected models, there are no tractable MCMC (Markov Chain Monte Carlo) schemes giving the correct equilibrium distribution over parameters. While this intractability, due to the partition function, is familiar to those performing parameter optimisation, Bayesian learning of posterior distributions over undirected model parameters has been unexplored and poses novel challenges. We propose several approximate MCMC schemes and test on fully observed binary models (Boltzmann machines) for a small coronary heart disease data set and larger artificial systems. While approximations must perform well on the model, their interaction with the sampling scheme is also important. Samplers based on variational mean-field approximations generally performed poorly, more advanced methods using loopy propagation, brief sampling and stochastic dynamics lead to acceptable parameter posteriors. Finally, we demonstrate these techniques on a Markov random field with hidden variables.
A Probabilistic Model for Online Document Clustering with Application to Novelty Detection

In this paper we propose a probabilistic model for online document clustering. We use non-parametric Dirichlet process prior to model the growing number of clusters, and use a prior of general English language model as the base distribution to handle the generation of novel clusters. Furthermore, cluster uncertainty is modeled with a Bayesian Dirichlet-multinomial distribution. We use empirical Bayes method to estimate hyperparameters based on a historical dataset. Our probabilistic model is applied to the novelty detection task in Topic Detection and Tracking (TDT) and compared with existing approaches in the literature.
On the Convergence of Bound Optimization Algorithms

Many practitioners who use EM and related algorithms complain that they are sometimes slow. When does this happen, and what can be done about it? In this paper, we study the general class of bound optimization algorithms - including EM, Iterative Scaling, Non-negative Matrix Factorization, CCCP - and their relationship to direct optimization algorithms such as gradientbased methods for parameter learning. We derive a general relationship between the updates performed by bound optimization methods and those of gradient and second-order methods and identify analytic conditions under which bound optimization algorithms exhibit quasi-Newton behavior, and under which they possess poor, first-order convergence. Based on this analysis, we consider several specific algorithms, interpret and analyze their convergence properties and provide some recipes for preprocessing input to these algorithms to yield faster convergence behavior. We report empirical results supporting our analysis and showing that simple data preprocessing can result in dramatically improved performance of bound optimizers in practice.
Unsupervised Learning

We give a tutorial and overview of the field of unsupervised learning from the perspective of statistical modeling. Unsupervised learning can be motivated from information theoretic and Bayesian principles. We briefly review basic models in unsupervised learning, including factor analysis, PCA, mixtures of Gaussians, ICA, hidden Markov models, state-space models, and many variants and extensions. We derive the EM algorithm and give an overview of fundamental concepts in graphical models, and inference algorithms on graphs. This is followed by a quick tour of approximate Bayesian inference, including Markov chain Monte Carlo (MCMC), Laplace approximation, BIC, variational approximations, and expectation propagation (EP). The aim of this chapter is to provide a high-level view of the field. Along the way, many state-ofthe-art ideas and future directions are also reviewed.
Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions

An approach to semi-supervised learning is proposed that is based on a Gaussian random field model. Labeled and unlabeled data are represented as vertices in a weighted graph, with edge weights encoding the similarity between instances. The learning problem is then formulated in terms of a Gaussian random field on this graph, where the mean of the field is characterized in terms of harmonic functions, and is efficiently obtained using matrix methods or belief propagation. The resulting learning algorithms have intimate connections with random walks, electric networks, and spectral graph theory. We discuss methods to incorporate class priors and the predictions of classifiers obtained by supervised learning. We also propose a method of parameter learning by entropy minimization, and show the algorithm's ability to perform feature selection. Promising experimental results are presented for synthetic data, digit classification, and text classification tasks.
Assignment 5 : Variational and Sampling Methods Unsupervised Learning

where y us a D-dimensional vector and I is the D×D identity matrix. Assume you have a data set of N i.i.d. observations of y, i.e. Y = {y, . . . ,y}. More details are provided in Appendix A. General Matlab hint: wherever possible, avoid looping over the data points. Many (but not all) of these functions can be written using matrix operations. In Matlab it’s much faster.  Each question depends on earlier questions. Start as soon as possible.  Derivations, code and plots.
Unsupervised learning of sensory-motor primitives

The search for motor primitives has captured the attention of researches in both biological and computational motor control. Yet a theory of how to construct such primitives from first principles is lacking. Here we propose to do that by building a compact forward model of the sensory-motor periphery via unsupervised learning. We also propose a method for probabilistic inversion of the forward model, which yields low-level feedback loops that can simplify control. The idea is applied to simulated biomechanical systems of varying levels of detail.
Expectation propagation for infinite mixtures ( Extended abstract )

This note describes a method for approximate inference in infinite models that uses deterministic Expectation Propagation instead of Monte Carlo. For infinite Gaussian mixtures, the algorithm provides cluster parameter estimates, cluster memberships, and model evidence. Model parameters, such as the expected size of the mixture, can be efficiently tuned via EM with EP as the E-step. The same approach can apply other infinite models such as infinite HMMs.
Warped Gaussian Processes

We generalise the Gaussian process (GP) framework for regression by learning a nonlinear transformation of the GP outputs. This allows for non-Gaussian processes and non-Gaussian noise. The learning algorithm chooses a nonlinear transformation such that transformed data is well-modelled by a GP. This can be seen as including a preprocessing transformation as an integral part of the probabilistic modelling problem, rather than as an ad-hoc step. We demonstrate on several real regression problems that learning the transformation can lead to significantly better performance than using a regular GP, or a GP with a fixed transformation.
The EM-EP algorithm for Gaussian process classification

Gaussian process classifiers (GPCs) are fully statistical kernel classification models derived from Gaussian processes for regression. In GPCs, the probability of belonging to a certain class at an input location is monotonically related to the value of some latent function at that location. Starting from a prior over this latent function, the data are used to infer both the posterior over the latent function and the values of hyperparameters determining various aspects of the function. GPCs can also be viewed as graphical models with latent variables. Based on the work of [1, 2], we present an approximate EM algorithm, the EM-EP algorithm for learning both the latent function and the hyperparameters of a GPC. The algorithm alternates the following steps until convergence. In the E-step, given the hyperparameters, a density for the latent variables defining the latent function is computed via the Expectation-Propagation (EP) algorithm [1, 2]. In the M-step, given the density for the latent values, the hyperparameters are selected to maximize a variational lower bound on the marginal likelihood (i.e. the model evidence). This algorithm is found to converge in practice and provides an efficient Bayesian framework for learning hyperparameters of the kernel. We examine the role of various different hyperparameters which model labeling errors, the lengthscales (i.e. relevances) of different features, and sharpness of the decision boundary. The added flexibility these provide results in significantly improved performance. Experimental results on synthetic and real data sets show that the EM-EP algorithm works well, with GPCs giving equal or better performance than support vector machines (SVMs) on all data sets tested.
Combining active learning and semi-supervised learning using Gaussian fields and harmonic functions

Active and semi-supervised learning are important techniques when labeled data are scarce. We combine the two under a Gaussian random field model. Labeled and unlabeled data are represented as vertices in a weighted graph, with edge weights encoding the similarity between instances. The semi-supervised learning problem is then formulated in terms of a Gaussian random field on this graph, the mean of which is characterized in terms of harmonic functions. Active learning is performed on top of the semisupervised learning scheme by greedily selecting queries from the unlabeled data to minimize the estimated expected classification error (risk); in the case of Gaussian fields the risk is efficiently computed using matrix methods. We present experimental results on synthetic data, handwritten digit recognition, and text classification tasks. The active learning scheme requires a much smaller number of queries to achieve high accuracy compared with random query selection.
Optimization with EM and Expectation-Conjugate-Gradient

We show a close relationship between the Expectation - Maximization (EM) algorithm and direct optimization algorithms such as gradient-based methods for parameter learning. We identify analytic conditions under which EM exhibits Newton-like behavior, and conditions under which it possesses poor, first-order convergence. Based on this analysis, we propose two novel algorithms for maximum likelihood estimation of latent variable models, and report empirical results showing that, as predicted by theory, the proposed new algorithms can substantially outperform standard EM in terms of speed of convergence in certain cases.
The variational Bayesian EM algorithm for incomplete data: with application to scoring graphical model structures

We present an efficient procedure for estimating the marginal likelihood of probabilistic models with latent variables or incomplete data. This method constructs and optimises a lower bound on the marginal likelihood using variational calculus, resulting in an iterative algorithm which generalises the EM algorithm by maintaining posterior distributions over both latent variables and parameters. We define the family of conjugate-exponential models—which includes finite mixtures of exponential family models, factor analysis, hidden Markov models, linear state-space models, and other models of interest—for which this bound on the marginal likelihood can be computed very simply through a modification of the standard EM algorithm. In particular, we focus on applying these bounds to the problem of scoring discrete directed graphical model structures (Bayesian networks). Extensive simulations comparing the variational bounds to the usual approach based on the Bayesian Information Criterion (BIC) and to a sampling-based gold standard method known as Annealed Importance Sampling (AIS) show that variational bounds substantially outperform BIC in finding the correct model structure at relatively little computational cost, while approaching the performance of the much more costly AIS procedure. Using AIS allows us to provide the first serious case study of the tightness of variational bounds. We also analyse the perfomance of AIS through a variety of criteria, discuss the use of other variational approaches to estimating marginal likelihoods based on Bethe and Kikuchi approximations, and outline directions in which this work can be extended.
A Bayesian network model for protein fold and remote homologue recognition

The Bayesian network approach is a framework which combines graphical representation and probability theory, which includes, as a special case, hidden Markov models. Hidden Markov models trained on amino acid sequence or secondary structure data alone have been shown to have potential for addressing the problem of protein fold and superfamily classification.



This paper describes a novel implementation of a Bayesian network which simultaneously learns amino acid sequence, secondary structure and residue accessibility for proteins of known three-dimensional structure. An awareness of the errors inherent in predicted secondary structure may be incorporated into the model by means of a confusion matrix. Training and validation data have been derived for a number of protein superfamilies from the Structural Classification of Proteins (SCOP) database. Cross validation results using posterior probability classification demonstrate that the Bayesian network performs better in classifying proteins of known structural superfamily than a hidden Markov model trained on amino acid sequences alone.
Graphical models: parameter learning

“Graphical models” combine graph theory and probability theory to provide a general framework for representing models in which a number of variables interact. Graphical models trace their origins to many different fields and have been applied in wide variety of settings: for example, to develop probabilistic expert systems, to understand neural network models, to infer trait inheritance in geneologies, to model images, to correct errors in digital communication, or to solve complex decision problems. Remarkably, the same formalisms and algorithms can be applied to this wide range of problems. Each node in the graph represent a random variable (or more generally a set of random variables). The pattern of edges in the graph represents the qualitative dependencies between the variables; the absence of an edge between two nodes means that any statistical dependency between these two variables is mediated via some other variable or set of variables. The quantitative dependencies between variables which are connected via edges are specified via parameterized conditional distributions, or more generally non-negative “potential functions”. The pattern of edges and the potential functions together specify a joint probability distribution over all the variables in the graph. We refer to the pattern of edges as the structure of the graph, while the parameters of the potential functions simply as the parameters of the graph. In this chapter, we assume that the structure of the graph is given, and that our goal is to learn the parameters of the graph from data. Solutions to the problem of learning the graph structure from data are given in GRAPHICAL MODELS, STRUCTURE LEARNING. We briefly review some of the notation from PROBABILISTIC INFERENCE IN GRAPHICAL MODELS which we will need to cover parameter learning in graphical models; we assume that the reader is familiar with the contents
A Bayesian approach to modelling uncertainty in gene expression clusters

The use of clustering methods has rapidly become one of the standard computational approaches to understanding microarray gene expression data [3, 1, 7]. In clustering, the patterns of expression of different genes across time, treatments, and tissues are grouped into distinct clusters (perhaps organized hierarchically) in which genes in the same cluster are assumed to be potentially functionally related or to be influenced by a common upstream factor. Such cluster structure can be used to aid in the elucidation of regulatory networks. For example, a compendium of gene expression profiles corresponding to mutants and chemical treatments can be used as a systematic tool to identify gene functions because mutants or drug targets that display similar profiles are likely to share cellular functions .
A bayesian view of motor adaptation

L'adaptabilite du systeme moteur a des perturbations visuelles ou mecaniques a ete clairement demontree par des experiences d'adaptation a des decalages prismatiques de la scene visuelle, ou par l'adaptation a des champs de force produits par un manipulandum robotise. Bien que ces experiences aient demontre la remarquable adaptabilite du systeme moteur, dans le monde reel, le systeme adaptatif doit tenir compte de l'incertitude inherente a l'environnement avec lequel il interagit. Deux sources d'incertitude doivent etre considerees : l'incertitude associee aux retro-actions sensorielles et l'incertitude provenant du fait que l'environnement peut changer. Ces differentes sortes d'incertitude produisent des contraintes sur la facon dont les informations sensorielles et contextuelles doivent etre integrees dans le temps afin de mettre a jour de facon optimale un systeme de controle adaptatif. Nous considerons l'adaptation motrice comme un probleme d'identification de systeme inferant un modele interne de l'environnement a partir d'observations bruitees, en nous basant sur l'exemple d'un systeme lineaire simple, et en etendant cette idee afin d'inclure l'information contextuelle. La theorie Bayesienne fournit un cadre dans lequel de telles sources incertaines d'information peuvent etre integrees de facon optimale. Nous discutons brievement les resultats experimentaux en termes de role de l'information contextuelle sur l'adaptation a des charges dynamiques et nous passons en revue un certain nombre d'experiences recentes qui abordent explicitement le theme de l'adaptation a des environnements bruites et variant en fonction du temps.
Learning from labeled and unlabeled data with label propagation

We investigate the use of unlabeled data to help labeled data in cl ssification. We propose a simple iterative algorithm, label pro pagation, to propagate labels through the dataset along high density are as d fined by unlabeled data. We analyze the algorithm, show its solution , and its connection to several other algorithms. We also show how to lear n p ameters by minimum spanning tree heuristic and entropy minimiz ation, and the algorithm’s ability to perform feature selection. Expe riment results are promising.
Learning with Multiple Labels

In this paper, we study a special kind of learning problem in which each training instance is given a set of (or distribution over) candidate class labels and only one of the candidate labels is the correct one. Such a problem can occur, e.g., in an information retrieval setting where a set of words is associated with an image, or if classes labels are organized hierarchically. We propose a novel discriminative approach for handling the ambiguity of class labels in the training examples. The experiments with the proposed approach over five different UCI datasets show that our approach is able to find the correct label among the set of candidate labels and actually achieve performance close to the case when each training instance is given a single correct label. In contrast, naive methods degrade rapidly as more ambiguity is introduced into the labels.
Bayesian Monte Carlo

We investigate Bayesian alternatives to classical Monte Carlo methods for evaluating integrals. Bayesian Monte Carlo (BMC) allows the incorporation of prior knowledge, such as smoothness of the integrand, into the estimation. In a simple problem we show that this outperforms any classical importance sampling method. We also attempt more challenging multidimensional integrals involved in computing marginal likelihoods of statistical models (a.k.a. partition functions and model evidences). We find that Bayesian Monte Carlo outperformed Annealed Importance Sampling, although for very high dimensional problems or problems with massive multimodality BMC may be less adequate. One advantage of the Bayesian approach to Monte Carlo is that samples can be drawn from any distribution. This allows for the possibility of active design of sample points so as to maximise information gain.