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The following Technical Reports are available from the International Computer Science Institute via anonymous FTP from icsi-ftp.Berkeley.EDU. They are located in ~ftp/pub/techreports and are all compressed, postscript files. If you experience difficulties, send mail to ftp-info@icsi.Berkeley.EDU. ------------------------------------------------------------------------------- tr-89-032.ps.Z A Connectionist Model of Unification Andreas Stolcke TR-89-032 May 1989 A general approach to encode and unify recursively nested feature structures in connectionist networks is described. The unification algorithm implemented by the net is based on iterative coarsening of equivalence classes of graph nodes. This method allows the reformulation of unification as a constraint satisfaction problem and enables the connectionist implementation to take full advantage of the potential parallelism inherent in unification, resulting in sublinear time complexity. Moreover, the method is able to process any number of feature structures in parallel, searching for possible unifications and making decisions among mutually exclusive unifications where necessary. Keywords and phrases. Unification, constraint satisfaction, connectionism, feature structures. ------------------------------------------------------------------------------- tr-90-009.ps.Z Miniature Language Acquisition: A touchstone for cognitive science Jerome A. Feldman, George Lakoff, Andreas Stolcke, and Susan Hollbach Weber TR-90-009 March 1990 (revised April 1990) Cognitive Science, whose genesis was interdisciplinary, shows signs of reverting to a disjoint collection of fields. This paper presents a compact, theory-free task that inherently requires an integrated solution. The basic problem is learning a subset of an arbitrary natural language from picture-sentence pairs. We describe a very specific instance of this task and show how it presents fundamental (but not impossible) challenges to several areas of cognitive science including vision, language, inference and learning. ------------------------------------------------------------------------------- tr-90-010.ps.Z L0: A Testbed for Miniature Language Acquisition Susan Hollbach Weber and Andreas Stolcke TR-90-010 July 1990 L0 constitutes a recent effort in Cognitive Science to build a natural language acquisition system for a limited visual domain. As a preparatory step towards addressing the issue of learning in this domain, we have built a set of tools for rapid prototyping and experimentation in the areas of language processing, image processing, and knowledge representation. The special focus of our work was the integration of these different components into a flexible system which would allow us to better understand the domain given by L0 and experiment with alternative approaches to the problems it poses. ------------------------------------------------------------------------------- tr-90-011.ps.Z A Network for Extracting the Locations of Point Clusters Using Selective Attention Subutai Ahmad and Stephen Omohundro ahmad@icsi.berkeley.edu and om@icsi.berkeley.edu TR 90-011 This report explores the problem of dynamically computing visual relations in connectionist systems. It concentrates on the task of learning whether three clumps of points in a 256x256 image form an equilateral triangle. We argue that feed-forward networks for solving this task would not scale well to images of this size. One reason for this is that local information does not contribute to the solution: it is necessary to compute relational information such as the distances between points. Our solution implements a mechanism for dynamically extracting the locations of the point clusters. It consists of an efficient focus of attention mechanism and a cluster detection scheme. The focus of attention mechanism allows the system to select any circular portion of the image in constant time. The cluster detector directs the focus of attention to clusters in the image. These two mechanisms are used to sequentially extract the relevant coordinates. With this new representation (locations of the points) very few training examples are required to learn the correct function. The resulting network is also very compact: the number of required weights is proportional to the number of input pixels. ------------------------------------------------------------------------------- tr-90-015.ps.Z Learning Feature-based Semantics with Simple Recurrent Networks Andreas Stolcke TR-90-015 April 1990 The paper investigates the possibilities for using simple recurrent networks as transducers which map sequential natural language input into non-sequential feature-based semantics. The networks perform well on sentences containing a single main predicate (encoded by transitive verbs or prepositions) applied to multiple-feature objects (encoded as noun-phrases with adjectival modifiers), and shows robustness against ungrammatical inputs. A second set of experiments deals with sentences containing embedded structures. Here the network is able to process multiple levels of sentence-final embeddings but only one level of center-embedding. This turns out to be a consequence of the network's inability to retain information that is not reflected in the outputs over intermediate phases of processing. Two extensions to Elman's \shortcite{Elman:88} original recurrent network architecture are introduced. ------------------------------------------------------------------------------- tr-91-030.ps.Z Probability estimation by feed-forward networks in continuous speech recognition Steve Renals, Nelson Morgan and Herve Bourlard TR-91-030 August 1991 We review the use of feed-forward networks as estimators of probability densities in hidden Markov modelling. In this paper we are mostly concerned with radial basis functions (RBF) networks. We note the isomorphism of RBF networks to tied mixture density estimators; additionally we note that RBF networks are trained to estimate posteriors rather than the likelihoods estimated by tied mixture density estimators. We show how the neural network training should be modified to resolve this mismatch. We also discuss problems with discriminative training, particularly the problem of dealing with unlabelled training data and the mismatch between model and data priors. ------------------------------------------------------------------------------- tr-91-031.ps.Z pSather monitors: Design, Tutorial, Rationale and Implementation Jerome A. Feldman, Chu-Cheow Lim and Franco Mazzanti TR-91-031 September 1989 Sather is a new object-oriented programming language under development at the International Computer Science Institute. The initial beta test release of the language was in June, 1991. From the outset, one goal of the Sather project has been the incorporation of constructs to support parallel programming. pSather is a parallel extension of Sather aimed at shared memory parallel architectures. A prototype of the language is currently being implemented on a Sequent Symmetry and on SUN Sparc-Stations. pSather monitors are one of the basic new features introduced in the language to deal with parallelism. The current design is presented and discussed in detail. ------------------------------------------------------------------------------- tr-91-032.ps.Z GAL: Networks that grow when they learn and shrink when they forget Ethem Alpaydin TR 91-032 Learning when limited to modification of some parameters has a limited scope; the capability to modify the system structure is also needed to get a wider range of the learnable. In the case of artificial neural networks, learning by iterative adjustment of synaptic weights can only succeed if the network designer predefines an appropriate network structure, i.e., number of hidden layers, units, and the size and shape of their receptive and projective fields. This paper advocates the view that the network structure should not, as usually done, be determined by trial-and-error but should be computed by the learning algorithm. Incremental learning algorithms can modify the network structure by addition and/or removal of units and/or links. A survey of current connectionist literature is given on this line of thought. ``Grow and Learn'' (GAL) is a new algorithm that learns an association at one-shot due to being incremental and using a local representation. During the so-called ``sleep'' phase, units that were previously stored but which are no longer necessary due to recent modifications are removed to minimize network complexity. The incrementally constructed network can later be finetuned off-line to improve performance. Another method proposed that greatly increases recognition accuracy is to train a number of networks and vote over their responses. The algorithm and its variants are tested on recognition of handwritten numerals and seem promising especially in terms of learning speed. This makes the algorithm attractive for on-line learning tasks, e.g., in robotics. The biological plausibility of incremental learning is also discussed briefly. Keywords Incremental learning, supervised learning, classificomion, pruning, destructive methods, growth, constructive methods, nearest neighbor. ------------------------------------------------------------------------------- tr-91-034.ps.Z Sather Language Design and Performance Evaluation Chu-Cheow Lim and Andreas Stolcke% TR-91-034 Sather is an object-oriented language recently designed and implemented at the International Computer Science Institute in Berkeley. It compiles into C and is intended to allow development of object-oriented, reusable software while retaining C's efficiency and portability. We investigate to what extent these goals were met through a comparative performance study and analysis of Sather and C programs on a RISC machine. Several language design decisions in Sather are motivated by the goal of efficient compilation to standard architectures. We evaluate the reasoning behind these decisions, using instruction set usage statistics, cache simulations, and other data collected by instrumented Sather-generated code. We conclude that while Sather users still pay a moderate overhead for programming convenience (in both run time and memory usage) the overall CPU and memory usage profiles of Sather programs are virtually identical to those of comparable C programs. Our analysis also shows that each of the choices made in Sather design and implementation is well justified by a distinctive performance advantage. It seems, then, that Sather proves the feasibility of its own design goal of making object-oriented programming efficient on standard architectures using a combination of judicious language design and efficient implementation. ------------------------------------------------------------------------------- tr-91-035.ps.Z HiPNeT-1 : A Highly Pipelined Architecture for Neural Network Training Krste Asanovic, Brian E. D. Kingsbury, Nelson Morgan, and John Wawrzynek TR-91-035 June 1991 Current artificial neural network (ANN) algorithms require extensive computational resources. However, they exhibit massive fine-grained parallelism and require only moderate arithmetic precision. These properties make possible custom VLSI implementations for high performance, low cost systems. This paper describes one such system, a special purpose digital VLSI architecture to implement neural network training in a speech recognition application. The network algorithm has a number of atypical features. These include: shared weights, sparse activation, binary inputs, and a serial training input stream. The architecture illustrates a number of design techniques to exploit these algorithm-specific features. The result is a highly pipelined system which sustains a learning rate of one pattern per clock cycle. At a clock rate of 20MHz each "neuron" site performs 200 million connection updates per second. Multiple such neurons can be integrated onto a modestly sized VLSI die. ------------------------------------------------------------------------------- tr-91-036.ps.Z Experimental Determination of Precision Requirements for Back-Propagation Training of Artificial Neural Networks Krste Asanovic and Nelson Morgan TR-91-036 June 1991 The impact of reduced weight and output precision on the back-propagation training algorithm is experimentally determined for a feed-forward multi-layer perceptron. In contrast with previous such studies, the network is large with over 20,000 weights, and is trained with a large, real-world data set of over 130,000 patterns to perform a difficult task, that of phoneme classification for a continuous speech recognition system. The results indicate that 16b weight values are sufficient to achieve training and classification results comparable to 32b floating point, provided that weight and bias values are scaled separately, and that rounding rather than truncation is employed to reduce the precision of intermediary values. Output precision can be reduced to 8 bits without significant effects on performance. ------------------------------------------------------------------------------- tr-91-048.ps.Z ICSIM: An Object-Oriented Connectionist Simulator Hienz W. Schmidt, Benedict Gomes TR-91-048 November 1991 ICSIM is a connectionist net simulator under development at ICSI and written in Sather. It is object-oriented to meet the requirements for flexibility and reuse of homogeneous and structured connectionist nets and to allow the user to encapsulate efficient customized implementations perhaps running on dedicated hardware. Nets are composed by combining off-the-shelf library classes and, if necessary, by specializing some of their behaviour. General user interface classes allow a uniform or customized graphic presentation of the nets being modeled. The report gives an overview of the simulator. Its main concepts, the class structure of its library and some of the design decisions are sketched and a number of example nets are used to illustrate how net structure, interconnection and behavior are defined. ------------------------------------------------------------------------------- tr-91-050.ps.Z Learning Spatial Concepts Using a Partially-Structured Connectionist Architecture Terry Regier TR-91-050 October 1991 This paper reports on the learning of spatial concepts in the L0 project. The challenge of designing an architecture capable of learning spatial concepts from any of the world's languages is first highlighted by reviewing the spatial systems of a number of languages which differ strikingly from English in this regard. A partially structured connectionist architecture is presented which has successfully learned concepts from the languages outlined. In this architecture, highly structured subnetworks, specialized for the spatial concept learning task, feed into an unstructured, fully-connected upper subnetwork. The system's success at the learning task is attributed on the one hand to the constrained search space which results from structuring, and on the other hand to the flexibility afforded by the unstructured upper subnetwork. ------------------------------------------------------------------------------- tr-91-058.ps.Z Detecting Skewed Symmetries Stefan Posch TR-91-058 October 1991 Many surfaces of objects in our world are bounded by planar bilaterally symmetric figures. When these figures are imaged under orthographic projection a skewed symmetric contour results. In this paper a new fast, local method to recover skewed symmetries from curve segments is proposed. It can be applied to complete as well as to occluded contours. Furthermore, the skewed symmetry property is employed to overcome fragmentation of a contour during segmentation. ------------------------------------------------------------------------------- tr-91-059.ps.Z Line Labeling Using Markov Random Fields Terry Regier TR-91-059 November 1991 The task of obtaining a line labeling from a greyscale image of trihedral objects presents difficulties not found in the classical line labeling problem. As originally formulated, the line labeling problem assumed that each junction was correctly pre-classified as being of a particular junction type (e.g. T, Y, arrow); the success of the algorithms proposed have depended critically upon getting this initial junction classification correct. In real images, however, junctions of different types may actually look quite similar, and this pre-classification is often difficult to achieve. This issue is addressed by recasting the line labeling problem in terms of a coupled probabilistic system which labels both lines and junctions. This results in a robust system, in which prior knowledge of acceptable configurations can serve to overcome the problem of misleading or ambiguous evidence. ------------------------------------------------------------------------------- tr-91-061.ps.Z Combinatory Differential Fields: An Algebraic Approach to Approximate Computation and Constructive Analysis Karl Aberer TR-91-061 October 1991 The algebraic structure of combinatory differential fields is constructed to provide a semantics for computations in analysis. In this setting programs, approximations, limits and operations of analysis are represented as algebraic terms. Analytic algorithms can be derived by algebraic methods. The main tool in this construction are combinatory models which are inner algebras of Engeler graph models. As an universal domain of denotational semantics the lattice structure of the graph models allows to give a striking simple semantics for computations with approximations. As models of combinatory algebra they provide all essential computational constructs, including recursion. Combinatory models are constructed as extensions of first order theories. The classical first order theory to describe analysis is the theory of differential fields. It turns out that two types of computational constructs, namely composition and piecewise definition of functions, are preferably introduced as extensions of the differential fields theory. Combinatory differential fields are then the combinatory models of these enriched differential fields. We show for basic algorithms of computational analysis how their combinatory counterparts are derived in the algebraic setting. We illustrate how these algorithms are suitable to be implemented in a computer algebra environment like mathematica. ------------------------------------------------------------------------------- tr-91-062.ps.Z Self-Testing/Correcting with Applications to Numerical Problems (Revised Version)} Manuel Blum, Michael Luby, Ronitt Rubinfeld TR-91-062 Suppose someone gives us an extremely fast program $P$ that we can call as a black box to compute a function $f$. Should we trust that $P$ works correctly? A {\em self-testing/correcting pair} for $f$ allows us to: (1) estimate the probability that $P(x) \not= f(x)$ when $x$ is randomly chosen; (2) on {\em any} input $x$, compute $f(x)$ correctly as long as $P$ is not too faulty on average. Furthermore, both (1) and (2) take time only slightly more than the original running time of $P$. We present general techniques for constructing simple to program self-testing/\-correcting pairs for a variety of numerical functions, including integer multiplication, modular multiplication, matrix multiplication, inverting matrices, computing the determinant of a matrix, computing the rank of a matrix, integer division, modular exponentiation and polynomial multiplication. ------------------------------------------------------------------------------- tr-91-064.ps.Z Distortion Accumulation in Image Transform Coding/Decoding Cascades Michael Gilge TR-91-064 December 1991 With an increasing number of applications that employ transform coding algorithms for data reduction, the effect of distortion accumulation caused by multiple coding needs to be investigated. Multiple coding occurs when more than one coding system is connected in a cascade. From the second stage on, the coding algorithm operates on data that has been previously coded/decoded. First a generic image communication system is being modelled and situations that can lead to distortion accumulation are analyzed. These results show two main reasons for distortion accumulation, which are separately and jointly investigated using a JPEG-type compression algorithm. The first situation involves geometric operations between the decoding and next coding step. Measurements show however that these spatial manipulations are the main contributors to distortion accumulation. The second reason for distortion accumulation is a misalignment of the block segmentation reference point in subsequent transform operations. A block raster detection algorithm is derived that can find the position of the block raster that was introduced in a previous coding step. If this information is used in the block segmentation of the following coding step, distortion accumulation can be avoided. Simulation results are given for an extended algorithm that registers regions of homogeneous block raster in images consisting of several subimages. ------------------------------------------------------------------------------- tr-91-065.ps.Z Motion Video Coding for Packet-Switching Networks -- An Integrated Approach Michael Gilge and Riccardo Gusella TR-91-065 December 1991 The advantages of packet video, constant image quality, service integration and statistical multiplexing, are overshadowed by packet loss, delay and jitter. By integrating network-control into the image data compression algorithm, the strong interactions between the coder and the network can be exploited and the available network bandwidth can be used best. In order to enable video transmission over today's networks without reservation or priorities and in the presence of high packet loss rates, congestion avoidance techniques need to be employed. This is achieved through rate and flow control, where feedback from the network is used to adapt coding parameters and vary the output rate. From the coding point of view the network is seen as data buffer. Analogously to constant bit rate applications, where a controller measures buffer fullness, we attempt to avoid network congestion (eq. buffer overflow) by monitoring the network and adapting the coding parameters in real-time. ------------------------------------------------------------------------------- tr-91-068.ps.Z Construction of a pseudo-random generator from any one-way function Johan Hastad, Russell Impagliazzo, Leonid A. Levin, Michael Luby TR-91-068 We show how to construct a pseudo-random generator from any one-way function. In contrast, previous works have constructed pseudo-random generators only from one-way functions with special structural properties. Our overall approach is different in spirit from previous work; we concentrate on extracting and smoothing entropy from a single iteration of the one-way function using universal hash functions. ------------------------------------------------------------------------------- tr-91-069.ps.Z RASTA-PLP Speech Analysis Hynek Hermansky, Nelson Morgan, Aruna Bayya, and Phil Kohn TR-91-069 December 1991 Most speech parameter estimation techniques are easily influenced by the frequency response of the communication channel. We have developed a technique that is more robust to such steady-state spectral factors in speech. The approach is conceptually simple and computationally efficient. The new method is described, and experimental results are reported, showing a significant advantage for the proposed method. ------------------------------------------------------------------------------- tr-91-070.ps.Z Connectionist Speech Recognition: Status and Prospects Steve Renals, Nelson Morgan, Herve Bourlard, Michael Cohen, Horacio Franco, Chuck Wooters and Phil Kohn. TR-91-070 December 1991 We report on recent advances in the ICSI connectionist speech recognition project. Highlights include: 1. Experimental results showing that connectionist methods can improve the performance of a context independent maximum likelihood trained HMM system, resulting in a performance close to that achieved using state of the art context dependent HMM systems of much higher complexity; 2. Mixing (context independent) connectionist probability estimates with maximum likelihood trained context dependent models to improve the performance of a state of the art system; 3. The development of a network decomposition method that allows connectionist modelling of context dependent phones efficiently and parsimoniously, with no statistical independence assumptions. ------------------------------------------------------------------------------- tr-91-071.ps.Z GDNN: A Gender-Dependent Neural Network for Continuous Speech Recognition Yochai Konig, Nelson Morgan, and Claudia Chandra TR-91-071 December 1991 Conventional speaker-independent speech recognition systems do not consider speaker-dependent parameters in the probability estimation of phonemes. These recognition systems are instead tuned to the ensemble statistics over many speakers. Most parametric representations of speech, however, are highly speaker dependent, and probability distributions suitable for a certain speaker may not perform as well for other speakers. It would be desirable to incorporate constraints on analysis that rely on the same speaker producing all the frames in an utterance. Our experiments take a first step towards this speaker consistency modeling by using a classification network to help generate gender-dependent phonetic probabilities for a statistical recognition system. Our results show a good classification rate for the gender classification net. Simple use of such a model to augment an existing larger network that estimates phonetic probabilities does not help speech recognition performance. However, when the new net is properly integrated in an HMM recognizer, it provides significant improvement in word accuracy. ------------------------------------------------------------------------------- tr-91-072.ps.Z SPERT: A VLIW/SIMD Microprocessor for Artificial Neural Network Computations Krste Asanovic, James Beck, Brian E. D. Kingsbury, Phil Kohn, Nelson Morgan, John Wawrzynek TR-91-072 December 1991 SPERT (Synthetic PERceptron Testbed) is a fully programmable single chip microprocessor designed for efficient execution of artificial neural network algorithms. The first implementation will be in a 1.2 micron CMOS technology with a 50MHz clock rate, and a prototype system is being designed to occupy a double SBus slot within a Sun Sparcstation. SPERT will sustain over 300 million connections per second during pattern classification, and around 100 million connection updates per second while running the popular error backpropagation training algorithm. This represents a speedup of around two orders of magnitude over a Sparcstation-2 for algorithms of interest. An earlier system produced by our group, the Ring Array Processor (RAP), used commercial DSP chips. Compared with a RAP multiprocessor of similar performance, SPERT represents over an order of magnitude reduction in cost for problems where fixed-point arithmetic is satisfactory. This report describes the current architecture, and gives the results of detailed simulations. The report also makes a short comparison to other high-performance digital neurocomputing chips. ------------------------------------------------------------------------------- tr-91-074.ps.Z Recent Work in VLSI Elements for Digital Implementations of Artificial Neural Networks Brian E. D. Kingsbury, Bertrand Irissou, Krste Asanovic, John Wawrzynek, Nelson Morgan TR-91-074 December 1991 A family of high-performance, area-efficient VLSI elements is being developed to simplify the design of artificial neural network processors. The libraries are designed around the MOSIS Scalable CMOS design rules, giving users the option of fabricating designs in 2.0um or 1.2um n-well processes, and greatly simplifying migration of the libraries to new MOSIS technologies. To date, libraries and generators have been created for saturating and nonsaturating adders, a two's-complement multiplier, and a triple-ported register file. The SPERT processor currently being designed at ICSI will be based upon these libraries, and is expected to run at 50 MHz when realized in a 1.2um CMOS technology. ------------------------------------------------------------------------------- tr-92-005.ps.Z New algorithmic results for lines-in-3-space problems Leonidas J. Guibas and Marco Pellegrini TR-92-005 January 1992 In the first part of the report we consider some incidence and ordering problems for lines in 3-space. We solve the problem of detecting efficiently if a query simplex is collision-free among polyhedral obstacles. In order to solve this problem we develop new on-line data structures to detect intersections of query halfplanes with sets of lines and segments. Then, we consider the nearest-neighbor problems for lines. Given a set of$n$ lines in 3-space, the shortest vertical segment between any pair of lines is found in randomized expected time $O(n^{8/5+\epsilon})$ for every $\eps>0$. The longest connecting vertical segment is found in time $O(n^{4/3+\eps})$. The shortest connecting segment is found in time $O(n^{5/3 + \epsilon})$. Problems involving lines, points and spheres in 3-space have important applications in graphics, CAD and optimization. In the second part of the report we consider several problems of this kind. We give subquaratic algorithms to count the number of incidences between a set of lines and a set of spheres, and to find the minimum distance between a set of lines and a set of points. We show that the sphere of minimum radius intersecting every line in a set of $n$ lines can be found in optimal expected time $O(n)$. Given $m$ possibly intersecting spheres we solve ray-shooting queries in $O(\log^2 m)$ time using a data structure of size $O(m^{5+\eps})$. This technical report collects part of the second author's work at I.C.S.I. form September 1991 to January 1992. -------------------------------------------------------------------------------