nils-herrmann/clean_scirepeval huggingface dataset:
A Robot That Improves Its Ability To

The use of machine learning is attractive in the design of autonomous robots, since it enables robots to adapt to the unforeseen. However, one of the key bottlenecks of current machine learning algorithms is the enormous sample complexity, which appears to prohibit their usage in all but the simplest of robotic domains. To make machine learning more practical in such domains, more powerful algorithms are needed that can generalize more accurately from less training data. This paper investigates the feasibility of learning algorithms that gradually improve over the lifetime of the robot. When faced with a novel thing to learn, knowledge acquired in previous learning tasks improves the ability of the robot to generalize, hence reduces the sample complexity. In this paper, we report results of applying a particular algorithm to mobile robot perception problems, which was originally proposed by Sud-darth 15]. The learning tasks considered involve the recognition of persons, objects and locations. We illustrate that having previously learned related tasks allows a robot to learn a novel task from signiicantly fewer training examples than if it was learning from scratch. Based on these results, we argue that self-adjusting learning strategies are superior to conventional learning algorithms in many robotic domains.
Lifelong Learning: A Case Study.

Machine learning has not yet succeeded in the design of robust learning algorithms that generalize well from very small datasets. In contrast, humans often generalize correctly from only a single training example, even if the number of potentially relevant features is large. To do so, they successfully exploit knowledge acquired in previous learning tasks, to bias subsequent learning. This paper investigates learning in a lifelong context. Lifelong learning addresses situations where a learner faces a stream of learning tasks. Such scenarios provide the opportunity for synergetic effects that arise if knowledge is transferred across multiple learning tasks. To study the utility of transfer, several approaches to lifelong learning are proposed and evaluated in an object recognition domain. It is shown that all these algorithms generalize consistently more accurately from scarce training data than comparable "single-task" approaches.
Is Learning The n-th Thing Any Easier Than Learning The First?

This paper investigates learning in a lifelong context. Lifelong learning addresses situations in which a learner faces a whole stream of learning tasks. Such scenarios provide the opportunity to transfer knowledge across multiple learning tasks, in order to generalize more accurately from less training data. In this paper, several different approaches to lifelong learning are described, and applied in an object recognition domain. It is shown that across the board, lifelong learning approaches generalize consistently more accurately from less training data, by their ability to transfer knowledge across learning tasks.
Explanation-based neural network learning a lifelong learning approach

From the Publisher: 
Lifelong learning addresses situations in which a learner faces a series of different learning tasks providing the opportunity for synergy among them. Explanation-based neural network learning (EBNN) is a machine learning algorithm that transfers knowledge across multiple learning tasks. When faced with a new learning task, EBNN exploits domain knowledge accumulated in previous learning tasks to guide generalization in the new one. As a result, EBNN generalizes more accurately from less data than comparable methods. Based Neural Network Learning: A Lifelong Learning Approach describes the basic EBNN paradigm and investigates it in the context of supervised learning, reinforcement learning, robotics, and chess.
Fast maximum a-posteriori inference on Monte Carlo state spaces

Many important algorithms for statistical inference can be expressed as a weighted maxkernel search problem. This is the case with the Viterbi algorithm for HMMs, message construction in maximum a posteriori BP (max-BP), as well as certain particlesmoothing algorithms. Previous work has focused on reducing the cost of this procedure in discrete regular grids . MonteCarlo state spaces, which are vital for highdimensional inference, cannot be handled by these techniques. We present a novel dualtree based algorithm that is appliable to a wide range of kernels and shows substantial performance gains over naive computation.
Empirically Evaluating Multiagent Reinforcement Learning Algorithms

This article makes two contributions. First, we present a platform for running and analyzing multiagent reinforcement learning experiments. Second, to demonstrate this platform we undertook and evaluated an empirical test of multiagent reinforcement learning algorithms from the literature, which to our knowledge is the largest such test ever conducted. We summarize some conclusions from our experiments, comparing algorithms on a variety of metrics including reward, regret, convergence to a Nash equilibrium and behavior in self play 1 .
Empirical Testing of Fast Kernel Density Estimation Algorithms

We present results of experiments testing the Fast Gauss Transform, Improved Fast Gauss Transform, and Dual-Tree methods (using kd-tree and Anchors Hierarchy data structures) for fast Kernel Density Estimation (KDE). We examine the performance of these methods with respect to data set size, dimension, allowable error, and data set structure (“clumpiness”), measured in terms of CPU time and memory usage. This is the first multi-method comparison in the literature. The results are striking, challenging several claims that are commonly made about these methods. The results are useful for researchers considering fast methods for KDE problems. Along the way, we provide a corrected error bound and a parameter-selection regime for the IFGT algorithm.
Toward Practical N2 Monte Carlo: the Marginal Particle Filter

Sequential Monte Carlo techniques are useful for state estimation in non-linear, non-Gaussian dynamic models. These methods allow us to approximate the joint posterior distribution using sequential importance sampling. In this framework, the dimension of the target distribution grows with each time step, thus it is necessary to introduce some resampling steps to ensure that the estimates provided by the algorithm have a reasonable variance. In many applications, we are only interested in the marginal filtering distribution which is defined on a space of fixed dimension. We present a Sequential Monte Carlo algorithm called the Marginal Particle Filter which operates directly on the marginal distribution, hence avoiding having to perform importance sampling on a space of growing dimension. Using this idea, we also derive an improved version of the auxiliary particle filter. We show theoretic and empirical results which demonstrate a reduction in variance over conventional particle filtering, and present techniques for reducing the cost of the marginal particle filter with N particles from O(N2) to O(N logN).
Learning about Individuals from Group Statistics

We propose a new problem formulation which is similar to, but more informative than, the binary multiple-instance learning problem. In this setting, we are given groups of instances (described by feature vectors) along with estimates of the fraction of positively-labeled instances per group. The task is to learn an instance level classifier from this information. That is, we are trying to estimate the unknown binary labels of individuals from knowledge of group statistics. We propose a principled probabilistic model to solve this problem that accounts for uncertainty in the parameters and in the unknown individual labels. This model is trained with an efficient MCMC algorithm. Its performance is demonstrated on both synthetic and real-world data arising in general object recognition.
Fast Krylov Methods for N-Body Learning

This paper addresses the issue of numerical computation in machine learning domains based on similarity metrics, such as kernel methods, spectral techniques and Gaussian processes. It presents a general solution strategy based on Krylov subspace iteration and fast N-body learning methods. The experiments show significant gains in computation and storage on datasets arising in image segmentation, object detection and dimensionality reduction. The paper also presents theoretical bounds on the stability of these methods.
A Boosted Particle Filter: Multitarget Detection and Tracking

The problem of tracking a varying number of non-rigid objects has two major difficulties. First, the observation models and target distributions can be highly non-linear and non-Gaussian. Second, the presence of a large, varying number of objects creates complex interactions with overlap and ambiguities. To surmount these difficulties, we introduce a vision system that is capable of learning, detecting and tracking the objects of interest. The system is demonstrated in the context of tracking hockey players using video sequences. Our approach combines the strengths of two successful algorithms: mixture particle filters and Adaboost. The mixture particle filter  is ideally suited to multi-target tracking as it assigns a mixture component to each player. The crucial design issues in mixture particle filters are the choice of the proposal distribution and the treatment of objects leaving and entering the scene. Here, we construct the proposal distribution using a mixture model that incorporates information from the dynamic models of each player and the detection hypotheses generated by Adaboost. The learned Adaboost proposal distribution allows us to quickly detect players entering the scene, while the filtering process enables us to keep track of the individual players. The result of interleaving Adaboost with mixture particle filters is a simple, yet powerful and fully automatic multiple object tracking system.
Insights on Fast Kernel Density Estimation Algorithms

We present results of experiments testing the Fast Gauss Transform, Improved Fast Gauss Transform, and Dual-Tree methods (using kd-tree and Anchors Hierarchy data structures) for fast Kernel Density Estimation (KDE). We examine the performance of these methods with respect to data set size, dimension, allowable error, and data set structure (“clumpiness”), measured in terms of CPU time and memory usage. This is the first multi-method comparison in the literature. The results are striking, challenging several claims that are commonly made about these methods. The results will be useful for researchers considering fast methods for KDE problems. We also provide theoretical results that improve the bounds on dual tree methods.
Owed to a Martingale: A Fast Bayesian On-Line EM Algorithm for Multinomial Models

This paper introduces a fast Bayesian online expectation maximization (BOEM) algorithm for multinomial mixtures. Using some properties of the Dirichlet distribution, we derive expressions for adaptive learning rates that depend solely on the data and the prior’s hyperparameters. As a result, we avoid the problem of having to tune the learning rates using heuristics. In the application to multinomial clustering, choosing the prior’s hyperparameters is an easy task. Our experiments on large real data sets demonstrate that our Bayesian online learning algorithms are fast and provide accurate regularized solutions. We prove asymptotic convergence of our algorithms using stochastic approximation theory.
1 Matching Words and Pictures

We present a new and very rich approach for modeling multi-modal data sets, focusing on the specific case of segmented images with associated text. Learning the joint distribution of image regions and words has many applications. We consider in detail predicting words associated with whole images (auto-annotation) and corresponding to particular image regions (region naming). Auto-annotation might help organize and access large collections of images. Region naming is a model of object recognition as a process of translating image regions to words, much as one might translate from one language to another. Learning the relationships between image regions and semantic correlates (words) is an interesting example of multi-modal data mining, particularly because it is typically hard to apply data mining techniques to collections of images. We develop a number of models for the joint distribution of image regions and words, including several which explicitly learn the correspondence between regions and words. We study multi-modal and correspondence extensions to Hofmann’s hierarchical clustering/aspect model, a translation model adapted from statistical machine translation (Brown et al.), and a multi-modal extension to latent dirichlet allocation (LDA). All models are assessed using a large collection of annotated images of real scenes. We study in depth the difficult problem of measuring performance. For the annotation task, we look at prediction performance on held out data. We present three alternative measures, oriented toward different types of task. Measuring the performance of correspondence methods is harder, because one must determine whether a word has been placed on the right region of an image. We can use annotation performance as a proxy measure, but accurate measurement requires hand labeled data, and thus must occur on a smaller scale. We show results using both an annotation proxy, and manually labeled data.
Matching Words and Pictures

We present a new approach for modeling multi-modal data sets, focusing on the specific case of segmented images with associated text. Learning the joint distribution of image regions and words has many applications. We consider in detail predicting words associated with whole images (auto-annotation) and corresponding to particular image regions (region naming). Auto-annotation might help organize and access large collections of images. Region naming is a model of object recognition as a process of translating image regions to words, much as one might translate from one language to another. Learning the relationships between image regions and semantic correlates (words) is an interesting example of multi-modal data mining, particularly because it is typically hard to apply data mining techniques to collections of images. We develop a number of models for the joint distribution of image regions and words, including several which explicitly learn the correspondence between regions and words. We study multi-modal and correspondence extensions to Hofmann's hierarchical clustering/aspect model, a translation model adapted from statistical machine translation (Brown et al.), and a multi-modal extension to mixture of latent Dirichlet allocation (MoM-LDA). All models are assessed using a large collection of annotated images of real scenes. We study in depth the difficult problem of measuring performance. For the annotation task, we look at prediction performance on held out data. We present three alternative measures, oriented toward different types of task. Measuring the performance of correspondence methods is harder, because one must determine whether a word has been placed on the right region of an image. We can use annotation performance as a proxy measure, but accurate measurement requires hand labeled data, and thus must occur on a smaller scale. We show results using both an annotation proxy, and manually labeled data.
The Sound of an Album Cover: A Probabilistic Approach to Multimedia

We present a novel, flexible, statistical approach to modeling music, images and text jointly. The technique is based on multi-modal mixture models and efficient computation using online EM. The learned models can be used to browse multimedia databases, to query on a multimedia database using any combination of music, images and text (lyrics and other contextual information), to annotate documents with music and images, and to find documents in a database similar to input text, music and/or graphics files.
Rao-Blackwellised particle filtering for fault diagnosis

We tackle the fault diagnosis problem using conditionally Gaussian state space models and an efficient Monte Carlo method known as Rao-Blackwellised particle filtering. In this setting, there is one different linear-Gaussian state space model for each possible discrete state of operation. The task of diagnosis is to identify the discrete state of operation using the continuous measurements corrupted by Gaussian noise. The method is applied to the diagnosis of faults in planetary rovers.
"Name That Song!" A Probabilistic Approach to Querying on Music and Text

We present a novel, flexible statistical approach for modelling music and text jointly. The approach is based on multi-modal mixture models and maximum a posteriori estimation using EM. The learned models can be used to browse databases with documents containing music and text, to search for music using queries consisting of music and text (lyrics and other contextual information), to annotate text documents with music, and to automatically recommend or identify similar songs.
Real-Time Monitoring of Complex Industrial Processes with Particle Filters

This paper discusses the application of particle filtering algorithms to fault diagnosis in complex industrial processes. We consider two ubiquitous processes: an industrial dryer and a level tank. For these applications, we compared three particle filtering variants: standard particle filtering, Rao-Blackwellised particle filtering and a version of Rao-Blackwellised particle filtering that does one-step look-ahead to select good sampling regions. We show that the overhead of the extra processing per particle of the more sophisticated methods is more than compensated by the decrease in error and variance.
The Sound of an Album Cover: Probabilistic Multimedia and IR

We present a novel, flexible, statistical approach to modeling music, images and text jointly. The technique is based on multi-modal mixture models and efficient computation using online EM. The learned models can be used to browse multimedia databases, to query on a multimedia database using any combination of music, images and text (lyrics and other contextual information), to annotate documents with music and images, and to find documents in a database similar to input text, music and/or graphics files.
Variational MCMC

We propose a new class of learning algorithms that combines variational approximation and Markov chain Monte Carlo (MCMC) simulation. Naive algorithms that use the variational approximation as proposal distribution can perform poorly because this approximation tends to underestimate the true variance and other features of the data. We solve this problem by introducing more sophisticated MCMC algorithms. One of these algorithms is a mixture of two MCMC kernels: a random walk Metropolis kernel and a block Metropolis-Hastings (MH) kernel with a variational approximation as proposal distribution. The MH kernel allows one to locate regions of high probability efficiently. The Metropolis kernel allows us to explore the vicinity of these regions. This algorithm outperforms variational approximations because it yields slightly better estimates of the mean and considerably better estimates of higher moments, such as covariances. It also outperforms standard MCMC algorithms because it locates the regions of high probability quickly, thus speeding up convergence. We also present an adaptive MCMC algorithm that iterates between improving the variational approximation and improving the MCMC approximation. We demonstrate the algorithms on the problem of Bayesian parameter estimation for logistic (sigmoid) belief networks.
An Introduction to Sequential Monte Carlo Methods

Many real-world data analysis tasks involve estimating unknown quantities from some given observations. In most of these applications, prior knowledge about the phenomenon being modelled is available. This knowledge allows us to formulate Bayesian models, that is prior distributions for the unknown quantities and likelihood functions relating these quantities to the observations. Within this setting, all inference on the unknown quantities is based on the posterior distribution obtained from Bayes’ theorem. Often, the observations arrive sequentially in time and one is interested in performing inference on-line. It is therefore necessary to update the posterior distribution as data become available. Examples include tracking an aircraft using radar measurements, estimating a digital communications signal using noisy measurements, or estimating the volatility of financial instruments using stock market data. Computational simplicity in the form of not having to store all the data might also be an additional motivating factor for sequential methods.
Sequential Monte Carlo Methods in Practice

Monte Carlo methods are revolutionizing the on-line analysis of data in fields as diverse as financial modeling, target tracking and computer vision. These methods, appearing under the names of bootstrap filters, condensation, optimal Monte Carlo filters, particle filters and survival of the fittest, have made it possible to solve numerically many complex, non-standard problems that were previously intractable. This book presents the first comprehensive treatment of these techniques, including convergence results and applications to tracking, guidance, automated target recognition, aircraft navigation, robot navigation, econometrics, financial modeling, neural networks, optimal control, optimal filtering, communications, reinforcement learning, signal enhancement, model averaging and selection, computer vision, semiconductor design, population biology, dynamic Bayesian networks, and time series analysis. This will be of great value to students, researchers and practitioners, who have some basic knowledge of probability. Arnaud Doucet received the Ph. D. degree from the University of Paris-XI Orsay in 1997. From 1998 to 2000, he conducted research at the Signal Processing Group of Cambridge University, UK. He is currently an assistant professor at the Department of Electrical Engineering of Melbourne University, Australia. His research interests include Bayesian statistics, dynamic models and Monte Carlo methods. Nando de Freitas obtained a Ph.D. degree in information engineering from Cambridge University in 1999. He is presently a research associate with the artificial intelligence group of the University of California at Berkeley. His main research interests are in Bayesian statistics and the application of on-line and batch Monte Carlo methods to machine learning. Neil Gordon obtained a Ph.D. in Statistics from Imperial College, University of London in 1993. He is with the Pattern and Information Processing group at the Defence Evaluation and Research Agency in the United Kingdom. His research interests are in time series, statistical data analysis, and pattern recognition with a particular emphasis on target tracking and missile guidance.
Bayesian Latent Semantic Analysis of Multimedia Databases

We present a Bayesian mixture model for probabilistic latent semantic analysis of documents with images and text. The Bayesian perspective allows us to perform automatic regularisation to obtain sparser and more coherent clustering models. It also enables us to encode a priori knowledge, such as word and image preferences. The learnt model can be used for browsing digital databases, information retrieval with image and/or text queries, image annotation (adding words to an image) and text illustration (adding images to a text).