mtybilly/Instruct2DS huggingface dataset:
Mixture Modeling by Affinity Propagation
Authors: Brendan J. Frey, Delbert Dueck
Published in: Advances in Neural Information Processing Systems 18  (NIPS 2005) (Main Conference Track), 2005

Abstract: Clustering is a fundamental problem in machine learning and has been approached in many ways. Two general and quite different approaches include iteratively ﬁtting a mixture model (e.g., using EM) and linking to- gether pairs of training cases that have high afﬁnity (e.g., using spectral methods). Pair-wise clustering algorithms need not compute sufﬁcient statistics and avoid poor solutions by directly placing similar examples in the same cluster. However, many applications require that each cluster of data be accurately described by a prototype or model, so afﬁnity-based clustering – and its beneﬁts – cannot be directly realized. We describe a technique called “afﬁnity propagation”, which combines the advantages of both approaches. The method learns a mixture model of the data by recursively propagating afﬁnity messages. We demonstrate afﬁnity prop- agation on the problems of clustering image patches for image segmen- tation and learning mixtures of gene expression models from microar- ray data. We ﬁnd that afﬁnity propagation obtains better solutions than mixtures of Gaussians, the K-medoids algorithm, spectral clustering and hierarchical clustering, and is both able to ﬁnd a pre-speciﬁed number of clusters and is able to automatically determine the number of clusters. Interestingly, afﬁnity propagation can be viewed as belief propagation in a graphical model that accounts for pairwise training case likelihood functions and the identiﬁcation of cluster centers.
Tensor Subspace Analysis
Authors: Xiaofei He, Deng Cai, Partha Niyogi
Published in: Advances in Neural Information Processing Systems 18  (NIPS 2005) (Main Conference Track), 2005

Abstract: Previous work has demonstrated that the image variations of many ob- jects (human faces in particular) under variable lighting can be effec- tively modeled by low dimensional linear spaces. The typical linear sub- space learning algorithms include Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Locality Preserving Projec- tion (LPP). All of these methods consider an n1 × n2 image as a high dimensional vector in Rn1×n2, while an image represented in the plane is intrinsically a matrix. In this paper, we propose a new algorithm called Tensor Subspace Analysis (TSA). TSA considers an image as the sec- ond order tensor in Rn1 ⊗ Rn2, where Rn1 and Rn2 are two vector spaces. The relationship between the column vectors of the image ma- trix and that between the row vectors can be naturally characterized by TSA. TSA detects the intrinsic local geometrical structure of the tensor space by learning a lower dimensional tensor subspace. We compare our proposed approach with PCA, LDA and LPP methods on two standard databases. Experimental results demonstrate that TSA achieves better recognition rate, while being much more efﬁcient.
Query by Committee Made Real
Authors: Ran Gilad-bachrach, Amir Navot, Naftali Tishby
Published in: Advances in Neural Information Processing Systems 18  (NIPS 2005) (Main Conference Track), 2005

Abstract: Training a learning algorithm is a costly task. A major goal of active learning is to reduce this cost. In this paper we introduce a new algorithm, KQBC, which is capable of actively learning large scale problems by using selective sampling. The algorithm overcomes the costly sampling step of the well known Query By Committee (QBC) algorithm by projecting onto a low dimensional space. KQBC also enables the use of kernels, providing a simple way of extending QBC to the non-linear scenario. Sampling the low dimension space is done using the hit and run random walk. We demonstrate the success of this novel algorithm by applying it to both artificial and a real world problems.
An Application of Markov Random Fields to Range Sensing
Authors: James Diebel, Sebastian Thrun
Published in: Advances in Neural Information Processing Systems 18  (NIPS 2005) (Main Conference Track), 2005

Abstract: This paper describes a highly successful application of MRFs to the problem of generating high-resolution range images. A new generation of range sensors combines the capture of low-resolution range images with the acquisition of registered high-resolution camera images. The MRF in this paper exploits the fact that discontinuities in range and coloring tend to co-align. This enables it to generate high-resolution, low-noise range images by integrating regular camera images into the range data. We show that by using such an MRF, we can substantially improve over existing range imaging technology.
Analysis of Spectral Kernel Design based Semi-supervised Learning
Authors: Tong Zhang, Rie Kubota Ando
Published in: Advances in Neural Information Processing Systems 18  (NIPS 2005) (Main Conference Track), 2005

Abstract: We consider a framework for semi-supervised learning using spectral decomposition based un-supervised kernel design. This approach sub- sumes a class of previously proposed semi-supervised learning methods on data graphs. We examine various theoretical properties of such meth- ods. In particular, we derive a generalization performance bound, and obtain the optimal kernel design by minimizing the bound. Based on the theoretical analysis, we are able to demonstrate why spectral kernel design based methods can often improve the predictive performance. Ex- periments are used to illustrate the main consequences of our analysis.
Representing Part-Whole Relationships in Recurrent Neural Networks
Authors: Viren Jain, Valentin Zhigulin, H. S. Seung
Published in: Advances in Neural Information Processing Systems 18  (NIPS 2005) (Main Conference Track), 2005

Abstract: There is little consensus about the computational function of top-down synaptic connections in the visual system. Here we explore the hypothesis that top-down connections, like bottom-up connections, reflect partwhole relationships. We analyze a recurrent network with bidirectional synaptic interactions between a layer of neurons representing parts and a layer of neurons representing wholes. Within each layer, there is lateral inhibition. When the network detects a whole, it can rigorously enforce part-whole relationships by ignoring parts that do not belong. The network can complete the whole by filling in missing parts. The network can refuse to recognize a whole, if the activated parts do not conform to a stored part-whole relationship. Parameter regimes in which these behaviors happen are identified using the theory of permitted and forbidden sets [3, 4]. The network behaviors are illustrated by recreating Rumelhart and McClelland's "interactive activation" model [7]. In neural network models of visual object recognition [2, 6, 8], patterns of synaptic connectivity often reflect part-whole relationships between the features that are represented by neurons. For example, the connections of Figure 1 reflect the fact that feature B both contains simpler features A1, A2, and A3, and is contained in more complex features C1, C2, and C3. Such connectivity allows neurons to follow the rule that existence of the part is evidence for existence of the whole. By combining synaptic input from multiple sources of evidence for a feature, a neuron can "decide" whether that feature is present. 1 The synapses shown in Figure 1 are purely bottom-up, directed from simple to complex features. However, there are also top-down connections in the visual system, and there is little consensus about their function. One possibility is that top-down connections also reflect part-whole relationships. They allow feature detectors to make decisions using the rule that existence of the whole is evidence for existence of its parts. In this paper, we analyze the dynamics of a recurrent network in which part-whole relationships are stored as bidirectional synaptic interactions, rather than the unidirectional interactions of Figure 1. The network has a number of interesting computational capabilities. When the network detects a whole, it can rigorously enforce part-whole relationships Synaptic connectivity may reflect other relationships besides part-whole. For example, invariances can be implemented by connecting detectors of several instances of the same feature to the same target, which is consequently an invariant detector of the feature. 1
Size Regularized Cut for Data Clustering
Authors: Yixin Chen, Ya Zhang, Xiang Ji
Published in: Advances in Neural Information Processing Systems 18  (NIPS 2005) (Main Conference Track), 2005

Abstract: We present a novel spectral clustering method that enables users to incorporate prior knowledge of the size of clusters into the clustering process. The cost function, which is named size regularized cut (SRcut), is defined as the sum of the inter-cluster similarity and a regularization term measuring the relative size of two clusters. Finding a partition of the data set to minimize SRcut is proved to be NP-complete. An approximation algorithm is proposed to solve a relaxed version of the optimization problem as an eigenvalue problem. Evaluations over different data sets demonstrate that the method is not sensitive to outliers and performs better than normalized cut.
How fast to work: Response vigor, motivation and tonic dopamine
Authors: Yael Niv, Nathaniel D. Daw, Peter Dayan
Published in: Advances in Neural Information Processing Systems 18  (NIPS 2005) (Main Conference Track), 2005

Abstract: Reinforcement learning models have long promised to unify computa- tional, psychological and neural accounts of appetitively conditioned be- havior. However, the bulk of data on animal conditioning comes from free-operant experiments measuring how fast animals will work for rein- forcement. Existing reinforcement learning (RL) models are silent about these tasks, because they lack any notion of vigor. They thus fail to ad- dress the simple observation that hungrier animals will work harder for food, as well as stranger facts such as their sometimes greater produc- tivity even when working for irrelevant outcomes such as water. Here, we develop an RL framework for free-operant behavior, suggesting that subjects choose how vigorously to perform selected actions by optimally balancing the costs and beneﬁts of quick responding. Motivational states such as hunger shift these factors, skewing the tradeoff. This accounts normatively for the effects of motivation on response rates, as well as many other classic ﬁndings. Finally, we suggest that tonic levels of dopamine may be involved in the computation linking motivational state to optimal responding, thereby explaining the complex vigor-related ef- fects of pharmacological manipulation of dopamine.
Robust design of biological experiments
Authors: Patrick Flaherty, Adam Arkin, Michael I. Jordan
Published in: Advances in Neural Information Processing Systems 18  (NIPS 2005) (Main Conference Track), 2005

Abstract: We address the problem of robust, computationally-efficient design of biological experiments. Classical optimal experiment design methods have not been widely adopted in biological practice, in part because the resulting designs can be very brittle if the nominal parameter estimates for the model are poor, and in part because of computational constraints. We present a method for robust experiment design based on a semidefinite programming relaxation. We present an application of this method to the design of experiments for a complex calcium signal transduction pathway, where we have found that the parameter estimates obtained from the robust design are better than those obtained from an "optimal" design.
On the Accuracy of Bounded Rationality: How Far from Optimal Is Fast and Frugal?
Authors: Michael Schmitt, Laura Martignon
Published in: Advances in Neural Information Processing Systems 18  (NIPS 2005) (Main Conference Track), 2005

Abstract: Fast and frugal heuristics are well studied models of bounded rationality. Psychological research has proposed the take-the-best heuristic as a successful strategy in decision making with limited resources. Take-thebest searches for a sufficiently good ordering of cues (features) in a task where objects are to be compared lexicographically. We investigate the complexity of the problem of approximating optimal cue permutations for lexicographic strategies. We show that no efficient algorithm can approximate the optimum to within any constant factor, if P = NP. We further consider a greedy approach for building lexicographic strategies and derive tight bounds for the performance ratio of a new and simple algorithm. This algorithm is proven to perform better than take-the-best.
Fast Online Policy Gradient Learning with SMD Gain Vector Adaptation
Authors: Jin Yu, Douglas Aberdeen, Nicol N. Schraudolph
Published in: Advances in Neural Information Processing Systems 18  (NIPS 2005) (Main Conference Track), 2005

Abstract: Reinforcement learning by direct policy gradient estimation is attractive in theory but in practice leads to notoriously ill-behaved optimization problems. We improve its robustness and speed of convergence with stochastic meta-descent, a gain vector adaptation method that employs fast Hessian-vector products. In our experiments the resulting algorithms outperform previously employed online stochastic, ofﬂine conjugate, and natural policy gradient methods.
Combining Graph Laplacians for Semi--Supervised Learning
Authors: Andreas Argyriou, Mark Herbster, Massimiliano Pontil
Published in: Advances in Neural Information Processing Systems 18  (NIPS 2005) (Main Conference Track), 2005

Abstract: A foundational problem in semi-supervised learning is the construction of a graph underlying the data. We propose to use a method which optimally combines a number of differently constructed graphs. For each of these graphs we associate a basic graph kernel. We then compute an optimal combined kernel. This kernel solves an extended regularization problem which requires a joint minimization over both the data and the set of graph kernels. We present encouraging results on different OCR tasks where the optimal combined kernel is computed from graphs constructed with a variety of distances functions and the `k ' in nearest neighbors.
Integrate-and-Fire models with adaptation are good enough
Authors: Renaud Jolivet, Alexander Rauch, Hans-rudolf Lüscher, Wulfram Gerstner
Published in: Advances in Neural Information Processing Systems 18  (NIPS 2005) (Main Conference Track), 2005

Abstract: Integrate-and-Fire-type models are usually criticized because of their simplicity. On the other hand, the Integrate-and-Fire model is the basis of most of the theoretical studies on spiking neuron models. Here, we develop a sequential procedure to quantitatively evaluate an equivalent Integrate-and-Fire-type model based on intracellular recordings of cortical pyramidal neurons. We find that the resulting effective model is sufficient to predict the spike train of the real pyramidal neuron with high accuracy. In in vivo-like regimes, predicted and recorded traces are almost indistinguishable and a significant part of the spikes can be predicted at the correct timing. Slow processes like spike-frequency adaptation are shown to be a key feature in this context since they are necessary for the model to connect between different driving regimes.
Large-Scale Multiclass Transduction
Authors: Thomas Gärtner, Quoc V. Le, Simon Burton, Alex J. Smola, Vishy Vishwanathan
Published in: Advances in Neural Information Processing Systems 18  (NIPS 2005) (Main Conference Track), 2005

Abstract: We present a method for performing transductive inference on very large datasets. Our algorithm is based on multiclass Gaussian processes and is effective whenever the multiplication of the kernel matrix or its inverse with a vector can be computed sufﬁciently fast. This holds, for instance, for certain graph and string kernels. Transduction is achieved by varia- tional inference over the unlabeled data subject to a balancing constraint.
Sparse Gaussian Processes using Pseudo-inputs
Authors: Edward Snelson, Zoubin Ghahramani
Published in: Advances in Neural Information Processing Systems 18  (NIPS 2005) (Main Conference Track), 2005

Abstract: We present a new Gaussian process (GP) regression model whose covariance is parameterized by the the locations of M pseudo-input points, which we learn by a gradient based optimization. We take M N, where N is the number of real data points, and hence obtain a sparse regression method which has O(M 2 N ) training cost and O(M 2 ) prediction cost per test case. We also find hyperparameters of the covariance function in the same joint optimization. The method can be viewed as a Bayesian regression model with particular input dependent noise. The method turns out to be closely related to several other sparse GP approaches, and we discuss the relation in detail. We finally demonstrate its performance on some large data sets, and make a direct comparison to other sparse GP methods. We show that our method can match full GP performance with small M , i.e. very sparse solutions, and it significantly outperforms other approaches in this regime.
Fast biped walking with a reflexive controller and real-time policy searching
Authors: Tao Geng, Bernd Porr, Florentin Wörgötter
Published in: Advances in Neural Information Processing Systems 18  (NIPS 2005) (Main Conference Track), 2005

Abstract: In this paper, we present our design and experiments of a planar biped robot ("RunBot") under pure reflexive neuronal control. The goal of this study is to combine neuronal mechanisms with biomechanics to obtain very fast speed and the on-line learning of circuit parameters. Our controller is built with biologically inspired sensor- and motor-neuron models, including local reflexes and not employing any kind of position or trajectory-tracking control algorithm. Instead, this reflexive controller allows RunBot to exploit its own natural dynamics during critical stages of its walking gait cycle. To our knowledge, this is the first time that dynamic biped walking is achieved using only a pure reflexive controller. In addition, this structure allows using a policy gradient reinforcement learning algorithm to tune the parameters of the reflexive controller in real-time during walking. This way RunBot can reach a relative speed of 3.5 leg-lengths per second after a few minutes of online learning, which is faster than that of any other biped robot, and is also comparable to the fastest relative speed of human walking. In addition, the stability domain of stable walking is quite large supporting this design strategy.
Learning from Data of Variable Quality
Authors: Koby Crammer, Michael Kearns, Jennifer Wortman
Published in: Advances in Neural Information Processing Systems 18  (NIPS 2005) (Main Conference Track), 2005

Abstract: We initiate the study of learning from multiple sources of limited data, each of which may be corrupted at a different rate. We develop a com- plete theory of which data sources should be used for two fundamental problems: estimating the bias of a coin, and learning a classiﬁer in the presence of label noise. In both cases, efﬁcient algorithms are provided for computing the optimal subset of data.
Two view learning: SVM-2K, Theory and Practice
Authors: Jason Farquhar, David Hardoon, Hongying Meng, John S. Shawe-taylor, Sándor Szedmák
Published in: Advances in Neural Information Processing Systems 18  (NIPS 2005) (Main Conference Track), 2005

Abstract: Kernel methods make it relatively easy to define complex highdimensional feature spaces. This raises the question of how we can identify the relevant subspaces for a particular learning task. When two views of the same phenomenon are available kernel Canonical Correlation Analysis (KCCA) has been shown to be an effective preprocessing step that can improve the performance of classification algorithms such as the Support Vector Machine (SVM). This paper takes this observation to its logical conclusion and proposes a method that combines this two stage learning (KCCA followed by SVM) into a single optimisation termed SVM-2K. We present both experimental and theoretical analysis of the approach showing encouraging results and insights.
Extracting Dynamical Structure Embedded in Neural Activity
Authors: Byron M. Yu, Afsheen Afshar, Gopal Santhanam, Stephen I. Ryu, Krishna V. Shenoy, Maneesh Sahani
Published in: Advances in Neural Information Processing Systems 18  (NIPS 2005) (Main Conference Track), 2005

Abstract: Spiking activity from neurophysiological experiments often exhibits dy- namics beyond that driven by external stimulation, presumably reﬂect- ing the extensive recurrence of neural circuitry. Characterizing these dynamics may reveal important features of neural computation, par- ticularly during internally-driven cognitive operations. For example, the activity of premotor cortex (PMd) neurons during an instructed de- lay period separating movement-target speciﬁcation and a movement- initiation cue is believed to be involved in motor planning. We show that the dynamics underlying this activity can be captured by a low- dimensional non-linear dynamical systems model, with underlying re- current structure and stochastic point-process output. We present and validate latent variable methods that simultaneously estimate the system parameters and the trial-by-trial dynamical trajectories. These meth- ods are applied to characterize the dynamics in PMd data recorded from a chronically-implanted 96-electrode array while monkeys perform delayed-reach tasks.
Large-scale biophysical parameter estimation in single neurons via constrained linear regression
Authors: Misha Ahrens, Liam Paninski, Quentin J. Huys
Published in: Advances in Neural Information Processing Systems 18  (NIPS 2005) (Main Conference Track), 2005

Abstract: Our understanding of the input-output function of single cells has been substantially advanced by biophysically accurate multi-compartmental models. The large number of parameters needing hand tuning in these models has, however, somewhat hampered their applicability and interpretability. Here we propose a simple and well-founded method for automatic estimation of many of these key parameters: 1) the spatial distribution of channel densities on the cell's membrane; 2) the spatiotemporal pattern of synaptic input; 3) the channels' reversal potentials; 4) the intercompartmental conductances; and 5) the noise level in each compartment. We assume experimental access to: a) the spatiotemporal voltage signal in the dendrite (or some contiguous subpart thereof, e.g. via voltage sensitive imaging techniques), b) an approximate kinetic description of the channels and synapses present in each compartment, and c) the morphology of the part of the neuron under investigation. The key observation is that, given data a)-c), all of the parameters 1)-4) may be simultaneously inferred by a version of constrained linear regression; this regression, in turn, is efficiently solved using standard algorithms, without any "local minima" problems despite the large number of parameters and complex dynamics. The noise level 5) may also be estimated by standard techniques. We demonstrate the method's accuracy on several model datasets, and describe techniques for quantifying the uncertainty in our estimates.
Data-Driven Online to Batch Conversions
Authors: Ofer Dekel, Yoram Singer
Published in: Advances in Neural Information Processing Systems 18  (NIPS 2005) (Main Conference Track), 2005

Abstract: Online learning algorithms are typically fast, memory efficient, and simple to implement. However, many common learning problems fit more naturally in the batch learning setting. The power of online learning algorithms can be exploited in batch settings by using online-to-batch conversions techniques which build a new batch algorithm from an existing online algorithm. We first give a unified overview of three existing online-to-batch conversion techniques which do not use training data in the conversion process. We then build upon these data-independent conversions to derive and analyze data-driven conversions. Our conversions find hypotheses with a small risk by explicitly minimizing datadependent generalization bounds. We experimentally demonstrate the usefulness of our approach and in particular show that the data-driven conversions consistently outperform the data-independent conversions.
Learning Rankings via Convex Hull Separation
Authors: Glenn Fung, Rómer Rosales, Balaji Krishnapuram
Published in: Advances in Neural Information Processing Systems 18  (NIPS 2005) (Main Conference Track), 2005

Abstract: We propose efﬁcient algorithms for learning ranking functions from or- der constraints between sets—i.e. classes—of training samples. Our al- gorithms may be used for maximizing the generalized Wilcoxon Mann Whitney statistic that accounts for the partial ordering of the classes: spe- cial cases include maximizing the area under the ROC curve for binary classiﬁcation and its generalization for ordinal regression. Experiments on public benchmarks indicate that: (a) the proposed algorithm is at least as accurate as the current state-of-the-art; (b) computationally, it is sev- eral orders of magnitude faster and—unlike current methods—it is easily able to handle even large datasets with over 20,000 samples.
Interpolating between types and tokens by estimating power-law generators
Authors: Sharon Goldwater, Mark Johnson, Thomas L. Griffiths
Published in: Advances in Neural Information Processing Systems 18  (NIPS 2005) (Main Conference Track), 2005

Abstract: Standard statistical models of language fail to capture one of the most striking properties of natural languages: the power-law distribution in the frequencies of word tokens. We present a framework for developing statistical models that generically produce power-laws, augmenting stan- dard generative models with an adaptor that produces the appropriate pattern of token frequencies. We show that taking a particular stochastic process – the Pitman-Yor process – as an adaptor justiﬁes the appearance of type frequencies in formal analyses of natural language, and improves the performance of a model for unsupervised learning of morphology.
Response Analysis of Neuronal Population with Synaptic Depression
Authors: Wentao Huang, Licheng Jiao, Shan Tan, Maoguo Gong
Published in: Advances in Neural Information Processing Systems 18  (NIPS 2005) (Main Conference Track), 2005

Abstract: In this paper, we aim at analyzing the characteristic of neuronal population responses to instantaneous or time-dependent inputs and the role of synapses in neural information processing. We have derived an evolution equation of the membrane potential density function with synaptic depression, and obtain the formulas for analytic computing the response of instantaneous re rate. Through a technical analysis, we arrive at several signi cant conclusions: The background inputs play an important role in information processing and act as a switch betwee temporal integration and coincidence detection. the role of synapses can be regarded as a spatio-temporal lter; it is important in neural information processing for the spatial distribution of synapses and the spatial and temporal relation of inputs. The instantaneous input frequency can affect the response amplitude and phase delay.
Sequence and Tree Kernels with Statistical Feature Mining
Authors: Jun Suzuki, Hideki Isozaki
Published in: Advances in Neural Information Processing Systems 18  (NIPS 2005) (Main Conference Track), 2005

Abstract: This paper proposes a new approach to feature selection based on a sta- tistical feature mining technique for sequence and tree kernels. Since natural language data take discrete structures, convolution kernels, such as sequence and tree kernels, are advantageous for both the concept and accuracy of many natural language processing tasks. However, experi- ments have shown that the best results can only be achieved when lim- ited small sub-structures are dealt with by these kernels. This paper dis- cusses this issue of convolution kernels and then proposes a statistical feature selection that enable us to use larger sub-structures effectively. The proposed method, in order to execute efﬁciently, can be embedded into an original kernel calculation process by using sub-structure min- ing algorithms. Experiments on real NLP tasks conﬁrm the problem in the conventional method and compare the performance of a conventional method to that of the proposed method.
Faster Rates in Regression via Active Learning
Authors: Rebecca Willett, Robert Nowak, Rui M. Castro
Published in: Advances in Neural Information Processing Systems 18  (NIPS 2005) (Main Conference Track), 2005

Abstract: This paper presents a rigorous statistical analysis characterizing regimes in which active learning significantly outperforms classical passive learning. Active learning algorithms are able to make queries or select sample locations in an online fashion, depending on the results of the previous queries. In some regimes, this extra flexibility leads to significantly faster rates of error decay than those possible in classical passive learning settings. The nature of these regimes is explored by studying fundamental performance limits of active and passive learning in two illustrative nonparametric function classes. In addition to examining the theoretical potential of active learning, this paper describes a practical algorithm capable of exploiting the extra flexibility of the active setting and provably improving upon the classical passive techniques. Our active learning theory and methods show promise in a number of applications, including field estimation using wireless sensor networks and fault line detection.
Top-Down Control of Visual Attention: A Rational Account
Authors: Michael Shettel, Shaun Vecera, Michael Mozer
Published in: Advances in Neural Information Processing Systems 18  (NIPS 2005) (Main Conference Track), 2005

Abstract: Theories of visual attention commonly posit that early parallel processes extract con- spicuous features such as color contrast and motion from the visual field. These features are then combined into a saliency map, and attention is directed to the most salient regions first. Top-down attentional control is achieved by modulating the contribution of different feature types to the saliency map. A key source of data concerning attentional control comes from behavioral studies in which the effect of recent experience is exam- ined as individuals repeatedly perform a perceptual discrimination task (e.g., “what shape is the odd-colored object?”). The robust finding is that repetition of features of recent trials (e.g., target color) facilitates performance. We view this facilitation as an adaptation to the statistical structure of the environment. We propose a probabilistic model of the environment that is updated after each trial. Under the assumption that attentional control operates so as to make performance more efficient for more likely environmental states, we obtain parsimonious explanations for data from four different experiments. Further, our model provides a rational explanation for why the influence of past experience on attentional control is short lived.
The Role of Top-down and Bottom-up Processes in Guiding Eye Movements during Visual Search
Authors: Gregory Zelinsky, Wei Zhang, Bing Yu, Xin Chen, Dimitris Samaras
Published in: Advances in Neural Information Processing Systems 18  (NIPS 2005) (Main Conference Track), 2005

Abstract: To investigate how top-down (TD) and bottom-up (BU) information is weighted in the guidance of human search behavior, we manipulated the proportions of BU and TD components in a saliency-based model. The model is biologically plausible and implements an artiﬁcial retina and a neuronal population code. The BU component is based on feature- contrast. The TD component is deﬁned by a feature-template match to a stored target representation. We compared the model’s behavior at differ- ent mixtures of TD and BU components to the eye movement behavior of human observers performing the identical search task. We found that a purely TD model provides a much closer match to human behavior than any mixture model using BU information. Only when biological con- straints are removed (e.g., eliminating the retina) did a BU/TD mixture model begin to approximate human behavior.
A Bayesian Framework for Tilt Perception and Confidence
Authors: Odelia Schwartz, Peter Dayan, Terrence J. Sejnowski
Published in: Advances in Neural Information Processing Systems 18  (NIPS 2005) (Main Conference Track), 2005

Abstract: The misjudgement of tilt in images lies at the heart of entertaining visual illusions and rigorous perceptual psychophysics. A wealth of findings has attracted many mechanistic models, but few clear computational principles. We adopt a Bayesian approach to perceptual tilt estimation, showing how a smoothness prior offers a powerful way of addressing much confusing data. In particular, we faithfully model recent results showing that confidence in estimation can be systematically affected by the same aspects of images that affect bias. Confidence is central to Bayesian modeling approaches, and is applicable in many other perceptual domains. Perceptual anomalies and illusions, such as the misjudgements of motion and tilt evident in so many psychophysical experiments, have intrigued researchers for decades.13 A Bayesian view48 has been particularly influential in models of motion processing, treating such anomalies as the normative product of prior information (often statistically codifying Gestalt laws) with likelihood information from the actual scenes presented. Here, we expand the range of statistically normative accounts to tilt estimation, for which there are classes of results (on estimation confidence) that are so far not available for motion. The tilt illusion arises when the perceived tilt of a center target is misjudged (ie bias) in the presence of flankers. Another phenomenon, called Crowding, refers to a loss in the confidence (ie sensitivity) of perceived target tilt in the presence of flankers. Attempts have been made to formalize these phenomena quantitatively. Crowding has been modeled as compulsory feature pooling (ie averaging of orientations), ignoring spatial positions.9, 10 The tilt illusion has been explained by lateral interactions11, 12 in populations of orientationtuned units; and by calibration.13 However, most models of this form cannot explain a number of crucial aspects of the data. First, the geometry of the positional arrangement of the stimuli affects attraction versus repulsion in bias, as emphasized by Kapadia et al14 (figure 1A), and others.15, 16 Second, Solomon et al. recently measured bias and sensitivity simultaneously.11 The rich and surprising range of sensitivities, far from flat as a function of flanker angles (figure 1B), are outside the reach of standard models. Moreover, current explanations do not offer a computational account of tilt perception as the outcome of a normative inference process. Here, we demonstrate that a Bayesian framework for orientation estimation, with a prior favoring smoothness, can naturally explain a range of seemingly puzzling tilt data. We explicitly consider both the geometry of the stimuli, and the issue of confidence in the esti-
(A) 6 5 4 3 2 1 0 -1 -2
Multiple Instance Boosting for Object Detection
Authors: Cha Zhang, John C. Platt, Paul A. Viola
Published in: Advances in Neural Information Processing Systems 18  (NIPS 2005) (Main Conference Track), 2005

Abstract: A good image object detection algorithm is accurate, fast, and does not require exact locations of objects in a training set. We can create such an object detector by taking the architecture of the Viola-Jones detector cascade and training it with a new variant of boosting that we call MILBoost. MILBoost uses cost functions from the Multiple Instance Learning literature combined with the AnyBoost framework. We adapt the feature selection criterion of MILBoost to optimize the performance of the Viola-Jones cascade. Experiments show that the detection rate is up to 1.6 times better using MILBoost. This increased detection rate shows the advantage of simultaneously learning the locations and scales of the objects in the training set along with the parameters of the classifier.
Generalization to Unseen Cases
Authors: Teemu Roos, Peter Grünwald, Petri Myllymäki, Henry Tirri
Published in: Advances in Neural Information Processing Systems 18  (NIPS 2005) (Main Conference Track), 2005

Abstract: We analyze classification error on unseen cases, i.e. cases that are different from those in the training set. Unlike standard generalization error, this off-training-set error may differ significantly from the empirical error with high probability even with large sample sizes. We derive a datadependent bound on the difference between off-training-set and standard generalization error. Our result is based on a new bound on the missing mass, which for small samples is stronger than existing bounds based on Good-Turing estimators. As we demonstrate on UCI data-sets, our bound gives nontrivial generalization guarantees in many practical cases. In light of these results, we show that certain claims made in the No Free Lunch literature are overly pessimistic.
Nearest Neighbor Based Feature Selection for Regression and its Application to Neural Activity
Authors: Amir Navot, Lavi Shpigelman, Naftali Tishby, Eilon Vaadia
Published in: Advances in Neural Information Processing Systems 18  (NIPS 2005) (Main Conference Track), 2005

Abstract: We present a non-linear, simple, yet effective, feature subset selection method for regression and use it in analyzing cortical neural activity. Our algorithm involves a feature-weighted version of the k-nearest-neighbor algorithm. It is able to capture complex dependency of the target func- tion on its input and makes use of the leave-one-out error as a natural regularization. We explain the characteristics of our algorithm on syn- thetic problems and use it in the context of predicting hand velocity from spikes recorded in motor cortex of a behaving monkey. By applying fea- ture selection we are able to improve prediction quality and suggest a novel way of exploring neural data.
Learning to Control an Octopus Arm with Gaussian Process Temporal Difference Methods
Authors: Yaakov Engel, Peter Szabo, Dmitry Volkinshtein
Published in: Advances in Neural Information Processing Systems 18  (NIPS 2005) (Main Conference Track), 2005

Abstract: The Octopus arm is a highly versatile and complex limb. How the Octo- pus controls such a hyper-redundant arm (not to mention eight of them!) is as yet unknown. Robotic arms based on the same mechanical prin- ciples may render present day robotic arms obsolete. In this paper, we tackle this control problem using an online reinforcement learning al- gorithm, based on a Bayesian approach to policy evaluation known as Gaussian process temporal difference (GPTD) learning. Our substitute for the real arm is a computer simulation of a 2-dimensional model of an Octopus arm. Even with the simpliﬁcations inherent to this model, the state space we face is a high-dimensional one. We apply a GPTD- based algorithm to this domain, and demonstrate its operation on several learning tasks of varying degrees of difﬁculty.
Improved risk tail bounds for on-line algorithms
Authors: Nicolò Cesa-bianchi, Claudio Gentile
Published in: Advances in Neural Information Processing Systems 18  (NIPS 2005) (Main Conference Track), 2005

Abstract: We prove the strongest known  bound for the risk of hypotheses selected  from the ensemble generated by running a learning algorithm incremen(cid:173) tally on the training data. Our result is based on proof techniques that are  remarkably  different from  the  standard  risk  analysis  based  on  uniform  convergence arguments.
Kernelized Infomax Clustering
Authors: David Barber, Felix V. Agakov
Published in: Advances in Neural Information Processing Systems 18  (NIPS 2005) (Main Conference Track), 2005

Abstract: We propose a simple information-theoretic approach to soft clus- tering based on maximizing the mutual information I(x, y) between the unknown cluster labels y and the training patterns x with re- spect to parameters of speciﬁcally constrained encoding distribu- tions. The constraints are chosen such that patterns are likely to be clustered similarly if they lie close to speciﬁc unknown vectors in the feature space. The method may be conveniently applied to learning the optimal aﬃnity matrix, which corresponds to learn- ing parameters of the kernelized encoder. The procedure does not require computations of eigenvalues of the Gram matrices, which makes it potentially attractive for clustering large data sets.
Temporally changing synaptic plasticity
Authors: Minija Tamosiunaite, Bernd Porr, Florentin Wörgötter
Published in: Advances in Neural Information Processing Systems 18  (NIPS 2005) (Main Conference Track), 2005

Abstract: Recent experimental results suggest that dendritic and back-propagating spikes can influence synaptic plasticity in different ways [1]. In this study we investigate how these signals could temporally interact at dendrites leading to changing plasticity properties at local synapse clusters. Similar to a previous study [2], we employ a differential Hebbian plasticity rule to emulate spike-timing dependent plasticity. We use dendritic (D-) and back-propagating (BP-) spikes as post-synaptic signals in the learning rule and investigate how their interaction will influence plasticity. We will analyze a situation where synapse plasticity characteristics change in the course of time, depending on the type of post-synaptic activity momentarily elicited. Starting with weak synapses, which only elicit local D-spikes, a slow, unspecific growth process is induced. As soon as the soma begins to spike this process is replaced by fast synaptic changes as the consequence of the much stronger and sharper BP-spike, which now dominates the plasticity rule. This way a winner-take-all-mechanism emerges in a two-stage process, enhancing the best-correlated inputs. These results suggest that synaptic plasticity is a temporal changing process by which the computational properties of dendrites or complete neurons can be substantially augmented.
Modeling Neural Population Spiking Activity with Gibbs Distributions
Authors: Frank Wood, Stefan Roth, Michael J. Black
Published in: Advances in Neural Information Processing Systems 18  (NIPS 2005) (Main Conference Track), 2005

Abstract: Probabilistic modeling of correlated neural population firing activity is central to understanding the neural code and building practical decoding algorithms. No parametric models currently exist for modeling multivariate correlated neural data and the high dimensional nature of the data makes fully non-parametric methods impractical. To address these problems we propose an energy-based model in which the joint probability of neural activity is represented using learned functions of the 1D marginal histograms of the data. The parameters of the model are learned using contrastive divergence and an optimization procedure for finding appropriate marginal directions. We evaluate the method using real data recorded from a population of motor cortical neurons. In particular, we model the joint probability of population spiking times and 2D hand position and show that the likelihood of test data under our model is significantly higher than under other models. These results suggest that our model captures correlations in the firing activity. Our rich probabilistic model of neural population activity is a step towards both measurement of the importance of correlations in neural coding and improved decoding of population activity.
Searching for Character Models
Authors: Jaety Edwards, David Forsyth
Published in: Advances in Neural Information Processing Systems 18  (NIPS 2005) (Main Conference Track), 2005

Abstract: We introduce a method to automatically improve character models for a handwritten script without the use of transcriptions and using a minimum of document speciﬁc training data. We show that we can use searches for the words in a dictionary to identify portions of the document whose transcriptions are unambiguous. Using templates extracted from those regions, we retrain our character prediction model to drastically improve our search retrieval performance for words in the document.
An Analog Visual Pre-Processing Processor Employing Cyclic Line Access in Only-Nearest-Neighbor-Interconnects Architecture
Authors: Yusuke Nakashita, Yoshio Mita, Tadashi Shibata
Published in: Advances in Neural Information Processing Systems 18  (NIPS 2005) (Main Conference Track), 2005

Abstract: An analog focal-plane processor having a 128128 photodiode array has been developed for directional edge ﬁltering. It can perform 44-pixel kernel convolution for entire pixels only with 256 steps of simple ana- log processing. Newly developed cyclic line access and row-parallel processing scheme in conjunction with the “only-nearest-neighbor in- terconnects” architecture has enabled a very simple implementation. A proof-of-concept chip was fabricated in a 0.35-(cid:0)m 2-poly 3-metal CMOS technology and the edge ﬁltering at a rate of 200 frames/sec. has been experimentally demonstrated.
The Curse of Highly Variable Functions for Local Kernel Machines
Authors: Yoshua Bengio, Olivier Delalleau, Nicolas L. Roux
Published in: Advances in Neural Information Processing Systems 18  (NIPS 2005) (Main Conference Track), 2005

Abstract: We present a series of theoretical arguments supporting the claim that a large class of modern learning algorithms that rely solely on the smoothness prior  with similarity between examples expressed with a local kernel  are sensitive to the curse of dimensionality, or more precisely to the variability of the target. Our discussion covers supervised, semisupervised and unsupervised learning algorithms. These algorithms are found to be local in the sense that crucial properties of the learned function at x depend mostly on the neighbors of x in the training set. This makes them sensitive to the curse of dimensionality, well studied for classical non-parametric statistical learning. We show in the case of the Gaussian kernel that when the function to be learned has many variations, these algorithms require a number of training examples proportional to the number of variations, which could be large even though there may exist short descriptions of the target function, i.e. their Kolmogorov complexity may be low. This suggests that there exist non-local learning algorithms that at least have the potential to learn about such structured but apparently complex functions (because locally they have many variations), while not using very specific prior domain knowledge.
Using ``epitomes'' to model genetic diversity: Rational design of HIV vaccine cocktails
Authors: Nebojsa Jojic, Vladimir Jojic, Christopher Meek, David Heckerman, Brendan J. Frey
Published in: Advances in Neural Information Processing Systems 18  (NIPS 2005) (Main Conference Track), 2005

Abstract: We introduce a new model of genetic diversity which summarizes a large input dataset into an epitome, a short sequence or a small set of short sequences of probability distributions capturing many overlapping subsequences from the dataset. The epitome as a representation has already been used in modeling real-valued signals, such as images and audio. The discrete sequence model we introduce in this paper targets applications in genetics, from multiple alignment to recombination and mutation inference. In our experiments, we concentrate on modeling the diversity of HIV where the epitome emerges as a natural model for producing relatively small vaccines covering a large number of immune system targets known as epitopes. Our experiments show that the epitome includes more epitopes than other vaccine designs of similar length, including cocktails of consensus strains, phylogenetic tree centers, and observed strains. We also discuss epitome designs that take into account uncertainty about Tcell cross reactivity and epitope presentation. In our experiments, we find that vaccine optimization is fairly robust to these uncertainties.