nils-herrmann/clean_scirepeval huggingface dataset:
Compiling Language Models from a Linguistically Motivated Unification Grammar

Systems now exist which are able to compile unification grammars into language models that can be included in a speech recognizer, but it is so far unclear whether non-trivial linguistically principled grammars can be used for this purpose. We describe a series of experiments which investigate the question empirically, by incrementally constructing a grammar and discovering what problems emerge when successively larger versions are compiled into finite state graph representations and used as language models for a medium-vocabulary recognition task.
Building a Robust Dialogue System with Limited Data

We describe robustness techniques used in the CommandTalk system at the recognition level, the parsing level, and the dialogue level, and how these were influenced by the lack of domain data. We used interviews with subject matter experts (SME's) to develop a single grammar for recognition, understanding, and generation, thus eliminating the need for a robust parser. We broadened the coverage of the recognition grammar by allowing word insertions and deletions, and we implemented clarification and correction subdialogues to increase robustness at the dialogue level. We discuss the applicability of these techniques to other domains.
Interpreting Language in Context in CommandTalk

is a spoken language interface to the ModSAF  battle eld simulator that allows simulation operators to generate and execute military exercises by creating forces and control measures, assigning missions to forces, and controlling the display. ModSAF is a stochastic distributed entity-based simulation, where one operator controls a collection of forces, while other operators may simultaneously control other allied and/or opposing forces. This paper will focus on how two representations of context are used in CommandTalk to correctly interpret the user's spoken utterances: situational context represents the current state of the simulation, and linguistic context represents the history of the user's linguistic acts.
Edge-based Best-first Chart Parsing Edge-based Best-first Chart Parsing

Best-rst probabilistic chart parsing attempts to parse eeciently by working on edges that are judged \best" by some probabilistic gure of merit (FOM). Recent work has used probabilistic context-free grammars (PCFGs) to assign probabilities to constituents, and to use these probabilities as the starting point for the FOM. This paper extends this approach to using a probabilistic FOM to judge edges (incomplete constituents), thereby giving a much ner-grained control over parsing eeort. We show how this can be accomplished in a particularly simple way using the common idea of binarizing the PCFG. The results obtained are about a factor of twenty improvement over the best prior results | that is, our parser achieves equivalent results using one twentieth the number of edges. Furthermore we show that this improvement is obtained with parsing precision and recall levels superior to those achieved by exhaustive parsing.  Best-rst probabilistic chart parsing attempts to parse eeciently by working on edges that are judged \best" by some probabilistic gure of merit (FOM). Recent work has used probabilistic context-free grammars (PCFGs) to assign probabilities to constituents, and to use these probabilities as the starting point for the FOM. This paper extends this approach to using a probabilistic FOM to judge edges (incomplete constituents), thereby giving a much ner-grained control over parsing eeort. We show how this can be accomplished in a particularly simple way using the common idea of binarizing the PCFG. The results obtained are about a factor of twenty improvement over the best prior results | that is, our parser achieves equivalent results using one twentieth the number of edges. Furthermore we show that this improvement is obtained with parsing precision and recall levels superior to those achieved by exhaustive parsing.
A Hierarchical Bayesian Model for Topic Segmentation

Many streams of real-world data, such as conversations or bo dy movements, consist of relatively coherent segments, each chara terized by particular topics or controllers. Making sense of these data re qui s simultaneously segmenting the sequences and inferring the struc ture of the segments. We present a hierarchical Bayesian model that can be used to break a sequence of utterances or movements into segments with different distributions over topics or controllers. We apply t his model to a database of meetings, showing that its unsupervised segme ntation is competitive with other approaches, and a database of human h and movements, revealing some of the controllers for motions of the h and.
A multidimensional scaling approach to mental multiplication

Adults consistently make errors in solving simple multiplication problems. These errors have been explained with reference to the interference between similar problems. In this paper, we apply multidimensional scaling (MDS) to the domain of multiplication problems, to uncover their underlying similarity structure. A tree-sorting task was used to obtain perceived dissimilarity ratings. The derived representation shows greater similarity between problems containing larger operands and suggests thattie problems (e.g., 7 x 7) hold special status. A version of the generalized context model (Nosofsky, 1986) was used to explore the derived MDS solution. The similarity of multiplication problems made an important contribution to producing a model consistent with human performance, as did the frequency with which such problems arise in textbooks, suggesting that both factors may be involved in the explanation of errors.
Competing strategies in categorization : Expediency and resistance to knowledge restructuring

The authors investigated people's ability to restructure their knowledge when additional information about a categorization task is revealed. In 2 experiments, people first learned to rely on a fairly accurate (but imperfect) predictor. At various points in training, a complex relationship between 2 other predictors was revealed in a schematic diagram that could support perfect performance. In Experiment 1, people adopted the complex strategy when it was revealed at the outset but were unable to restructure their knowledge after the expedient predictor had been learned. In Experiment 2, expedient knowledge persisted even with an adaptive display. The persistence of expedient knowledge is explained by associative blocking of potential alternative cues. A 3rd experiment analyzed the strategies people use with and without the diagram. The study confirmed that the diagram, when presented at the outset, significantly alters people's approach to the task.
Competing strategies in categorization: expediency and resistance to knowledge restructuring.

The authors investigated people's ability to restructure their knowledge when additional information about a categorization task is revealed. In 2 experiments, people first learned to rely on a fairly accurate (but imperfect) predictor. At various points in training, a complex relationship between 2 other predictors was revealed in a schematic diagram that could support perfect performance. In Experiment 1, people adopted the complex strategy when it was revealed at the outset but were unable to restructure their knowledge after the expedient predictor had been learned. In Experiment 2, expedient knowledge persisted even with an adaptive display. The persistence of expedient knowledge is explained by associative blocking of potential alternative cues. A 3rd experiment analyzed the strategies people use with and without the diagram. The study confirmed that the diagram, when presented at the outset, significantly alters people's approach to the task.
Variational Message Passing

Bayesian inference is now widely established as one of the principal foundations for machine learning. In practice, exact inference is rarely possible, and so a variety of approximation techniques have been developed, one of the most widely used being a deterministic framework called variational inference. In this paper we introduce Variational Message Passing (VMP), a general purpose algorithm for applying variational inference to Bayesian Networks. Like belief propagation, VMP proceeds by sending messages between nodes in the network and updating posterior beliefs using local operations at each node. Each such update increases a lower bound on the log evidence (unless already at a local maximum). In contrast to belief propagation, VMP can be applied to a very general class of conjugate-exponential models because it uses a factorised variational approximation. Furthermore, by introducing additional variational parameters, VMP can be applied to models containing non-conjugate distributions. The VMP framework also allows the lower bound to be evaluated, and this can be used both for model comparison and for detection of convergence. Variational message passing has been implemented in the form of a general purpose inference engine called VIBES ('Variational Inference for BayEsian networkS') which allows models to be specified graphically and then solved variationally without recourse to coding.
LOCUS: learning object classes with unsupervised segmentation

We address the problem of learning object class models and object segmentations from unannotated images. We introduce LOCUS (learning object classes with unsupervised segmentation) which uses a generative probabilistic model to combine bottom-up cues of color and edge with top-down cues of shape and pose. A key aspect of this model is that the object appearance is allowed to vary from image to image, allowing for significant within-class variation. By iteratively updating the belief in the object's position, size, segmentation and pose, LOCUS avoids making hard decisions about any of these quantities and so allows for each to be refined at any stage. We show that LOCUS successfully learns an object class model from unlabeled images, whilst also giving segmentation accuracies that rival existing supervised methods. Finally, we demonstrate simultaneous recognition and segmentation in novel images using the learned models for a number of object classes, as well as unsupervised object discovery and tracking in video.
Explorer Variational Message Passing

Bayesian inference is now widely established as one of the pr inci al foundations for machine learning. In practice, exact inference is rarely possible, and so a variety of approximation techniques have been developed, one of the most widely used being a deter ministic framework called variational inference. In this paper we introduce Variational Me ssage Passing (VMP), a general purpose algorithm for applying variational inference to Bayesian N etworks. Like belief propagation, VMP proceeds by sending messages between nodes in the network an d updating posterior beliefs using local operations at each node. Each such update increase s a lower bound on the log evidence (unless already at a local maximum). In contrast to belief pr opagation, VMP can be applied to a very general class of conjugate-exponential models becaus it es a factorised variational approximation. Furthermore, by introducing additional variatio nal parameters, VMP can be applied to models containing non-conjugate distributions. The VMP fr amework also allows the lower bound to be evaluated, and this can be used both for model compariso n and for detection of convergence. Variational message passing has been implemented in the for m of a general purpose inference engine called VIBES (‘Variational Inference for BayEsian net workS’) which allows models to be specified graphically and then solved variationally withou t recourse to coding.
Variational Message Passing and its Applications

This thesis is concerned with the development of Variational Message Passing (VMP), an algorithm for automatically performing variational inference in a probabilistic graphical model. VMP allows learning and reasoning about a system to proceed directly from a given probabilistic model of that system. The utility of VMP has been demonstrated by solving problems in the domains of machine vision and bioinformatics. VMP dramatically simplifies the construction and testing of new variational models and readily allows a range of alternative models to be tested on a given problem. In chapter 1, a probabilistic approach to automatic learning and reasoning is introduced. Belief propagation, an existing exact inference algorithm that uses message passing in a graphical model, is outlined, along with its limitations. These limitations lead to the need for approximate inference methods, including sampling methods and variational inference. The latter method of variational inference, which provides an analytical approximation to the posterior distribution, is described in detail. Chapter 2 presents a novel framework for performing automatic variational inference in a wide range of probabilistic models. The core of the framework is the Variational Message Passing algorithm which is an analog of belief propagation that uses message passing within a graphical model to optimise an approximate variational distribution. A software package, called VIBES (Variational Inference in BayESian networks), is presented as an implementation of the VMP framework. A tutorial is included which demonstrates applying VIBES to a small data set. Chapter 3 sees the framework being applied to the problem of modelling non-linear image manifolds such as those of face images and digits images. In chapter 4, the problems of DNA microarray image analysis and gene expression modelling are addressed, again using the VMP framework. Chapter 5 extends Variational Message Passing by allowing variational distributions which retain part of the dependency structure of the original model. The resulting Structured VMP algorithm is shown to improve the quality of the approximate inference and hence widen the applicability of the framework. Conclusions and suggestions for future research directions are presented in Chapter 6.
Generative Affine Localisation and Tracking

We present an extension to the Jojic and Frey (2001) layered sprite model which allows for layers to undergo affine transformations. This extension allows for affine object pose to be inferred whilst simultaneously learning the object shape and appearance. Learning is carried out by applying an augmented variational inference algorithm which includes a global search over a discretised transform space followed by a local optimisation. To aid correct convergence, we use bottom-up cues to restrict the space of possible affine transformations. We present results on a number of video sequences and show how the model can be extended to track an object whose appearance changes throughout the sequence.
Structured Variational Distributions in VIBES

Variational methods are becoming increasingly popular for the approximate solution of complex probabilistic models in machine learning, computer vision, information retrieval and many other fields. Unfortunately, for every new application it is necessary first to derive the specific forms of the variational update equations for the particular probabilistic model being used, and then to implement these equations in applicationspecific software. Each of these steps is both time consuming and error prone. We have therefore recently developed a general purpose inference engine called VIBES  (‘Variational Inference for Bayesian Networks’) which allows a wide variety of probabilistic models to be implemented and solved variationally without recourse to coding. New models are specified as a directed acyclic graph using an interface analogous to a drawing package, and VIBES then automatically generates and solves the variational equations. The original version of VIBES assumed a fully factorized variational posterior distribution. In this paper we present an extension of VIBES in which the variational posterior distribution corresponds to a sub-graph of the full probabilistic model. Such structured distributions can produce much closer approximations to the true posterior distribution. We illustrate this approach using an example based on Bayesian hidden Markov models.
VIBES: A Variational Inference Engine for Bayesian Networks

In recent years variational methods have become a popular tool for approximate inference and learning in a wide variety of probabilistic models. For each new application, however, it is currently necessary first to derive the variational update equations, and then to implement them in application-specific code. Each of these steps is both time consuming and error prone. In this paper we describe a general purpose inference engine called VIBES ('Variational Inference for Bayesian Networks') which allows a wide variety of probabilistic models to be implemented and solved variationally without recourse to coding. New models are specified either through a simple script or via a graphical interface analogous to a drawing package. VIBES then automatically generates and solves the variational equations. We illustrate the power and flexibility of VIBES using examples from Bayesian mixture modelling.
Non-linear Bayesian Image Modelling

In recent years several techniques have been proposed for modelling the low-dimensional manifolds, or 'subspaces', of natural images. Examples include principal component analysis (as used for instance in 'eigen-faces'), independent component analysis, and auto-encoder neural networks. Such methods suffer from a number of restrictions such as the limitation to linear manifolds or the absence of a probablistic representation. In this paper we exploit recent developments in the fields of variational inference and latent variable models to develop a novel and tractable probabilistic approach to modelling manifolds which can handle complex non-linearities. Our framework comprises a mixture of sub-space components in which both the number of components and the effective dimensionality of the subspaces are determined automatically as part of the Bayesian inference procedure. We illustrate our approach using two classical problems: modelling the manifold of face images and modelling the manifolds of hand-written digits.
Change Point Problems in Linear Dynamical Systems

We study the problem of learning two regimes (we have a normal and a prefault regime in mind) based on a train set of non-Markovian observation sequences. Key to the model is that we assume that once the system switches from the normal to the prefault regime it cannot restore and will eventually result in a fault. We refer to the particular setting as semi-supervised since we assume the only information given to the learner is whether a particular sequence ended with a stop (implying that the sequence was generated by the normal regime) or with a fault (implying that there was a switch from the normal to the fault regime). In the latter case the particular time point at which a switch occurred is not known.The underlying model used is a switching linear dynamical system (SLDS). The constraints in the regime transition probabilities result in an exact inference procedure that scales quadratically with the length of a sequence. Maximum aposteriori (MAP) parameter estimates can be found using an expectation maximization (EM) algorithm with this inference algorithm in the E-step. For long sequences this will not be practically feasible and an approximate inference and an approximate EM procedure is called for. We describe a flexible class of approximations corresponding to different choices of clusters in a Kikuchi free energy with weak consistency constraints.
Gaussian Quadrature Based Expectation Propagation

We present a general approximation method for Bayesian inference problems. The method is based on Expectation Propagation (EP). Projection steps in the EP iteration that can­ not be done analytically are done using Gaus­ sian quadrature. By identifying a general form in the projections, the only quadrature rules that are required are for exponential family weight functions. The corresponding cumulant and moment generating functions can then be used to automatically derive the necessary quadrature rules. In this article the approach is restricted to approximating families that factorize to a product of one­ dimensional families. The final algorithm has interesting similar­ ities with particle filtering algorithms. We discuss these, and also discuss the rela­ tionship with variational Bayes and Laplace propagation. Experimental results are given for an interesting model from mathematical finance.
IMPROVED UNSCENTED K ALM AN SMOOTHING FOR STOCK VOLATILITY ES TI MAT IO N

We introduce a novel approximate inference algorithm for non-linear dynamical systems. The algorithm is based upon ex- pectation propagation and Gaussian quadrature. The first forward pass is strongly related to the unscented Kalman filter. It im- proves upon unscented Kalman filtering by only making Gaussian approximations in the latent and not in the observation space. Smoothed estimates can be found without inverting latent space dynamics and can be improved by iteration. Multiple forward and backward passes make it possible to improve local approximations and make them as consistent as possible. We demonstrate the validity of the approach with an interesting inference problem in stochastic stock volatility models. The tradi- tional unscented Kalman filter is ill suited for this problem: it can be proven that t he traditional filter effectively never updates prior beliefs. The novel algorithm gives good results and improves with iteration.
Improved unscented kalman smoothing for stock volatility estimation

We introduce a novel approximate inference algorithm for nonlinear dynamical systems. The algorithm is based upon expectation propagation and Gaussian quadrature. The first forward pass is strongly related to the unscented Kalman filter. It improves upon unscented Kalman filtering by only making Gaussian approximations in the latent and not in the observation space. Smoothed estimates can be found without inverting latent space dynamics and can be improved by iteration. Multiple forward and backward passes make it possible to improve local approximations and make them as consistent as possible. We demonstrate the validity of the approach with an interesting inference problem in stochastic stock volatility models. The traditional unscented Kalman filter is ill suited for this problem: it can be proven that the traditional filter effectively never updates prior beliefs. The novel algorithm gives good results and improves with iteration
Multi-scale Switching Linear Dynamical Systems

Switching linear dynamic systems can monitor systems that operate in different regimes. In this article we introduce a class of multi-scale switching linear dynamical systems that are particularly suited if such regimes form a hierarchy. The setup consists of a specific switching linear dynamical system for every level of coarseness. Jeffrey's rule of conditioning is used to coordinate the models at the different levels. 
 
When the models are appropriately constrained, inference at finer levels can be performed independently for every subtree. This makes it possible to determine the required degree of detail on-line. The refinements of very improbable regimes need not be explored. 
 
The computational complexity of exact inference in both the standard and the multi-class switching linear dynamical system is exponential in the number of observations. We describe an appropriate approximate inference algorithm based on expectation propagation and relate it to a variant of the Bethe free energy.