pwc-archive/papers-with-abstracts huggingface dataset:
Selecting Receptive Fields in Deep Networks
Authors: Adam Coates, Andrew Y. Ng
Date: 2011-12-01 00:00:00
Proceeding: NeurIPS 2011 12

Recent deep learning and unsupervised feature learning systems that learn from unlabeled data have achieved high performance in benchmarks by using extremely large architectures with many features (hidden units) at each layer.  Unfortunately, for such large architectures the number of parameters usually grows quadratically in the width of the network, thus necessitating hand-coded "local receptive fields" that limit the number of connections from lower level features to higher ones (e.g., based on spatial locality).  In this paper we propose a fast method to choose these connections that may be incorporated into a wide variety of unsupervised training methods.  Specifically, we choose local receptive fields that group together those low-level features that are most similar to each other according to a pairwise similarity metric.  This approach  allows us to harness the advantages of local receptive fields (such  as improved scalability, and reduced data requirements) when we do  not know how to specify such receptive fields by hand or where our  unsupervised training algorithm has no obvious generalization to a  topographic setting.  We produce results showing how this method allows us to use even simple unsupervised training algorithms to train successful multi-layered etworks that achieve state-of-the-art results on CIFAR and STL datasets: 82.0% and 60.1% accuracy, respectively.
Convergent Fitted Value Iteration with Linear Function Approximation
Authors: Daniel J. Lizotte
Date: 2011-12-01 00:00:00
Proceeding: NeurIPS 2011 12
Tasks: regression

Fitted value iteration (FVI) with ordinary least squares regression is known to diverge. We present a new method, "Expansion-Constrained Ordinary Least Squares" (ECOLS), that produces a linear approximation but also guarantees convergence when used with FVI. To ensure convergence, we constrain the least squares regression operator to be a non-expansion in the infinity-norm. We show that the space of function approximators that satisfy this constraint is more rich than the space of "averagers," we prove a minimax property of the ECOLS residual error, and we give an efficient algorithm for computing the coefficients of ECOLS based on constraint generation. We illustrate the algorithmic convergence of FVI with ECOLS in a suite of experiments, and discuss its properties.
Algorithms for Hyper-Parameter Optimization
Authors: James S. Bergstra, Rémi Bardenet, Yoshua Bengio, Balázs Kégl
Date: 2011-12-01 00:00:00
Proceeding: NeurIPS 2011 12
Tasks: GPU, image-classification, Image Classification

Several recent advances to the state of the art in image classification benchmarks have come from better configurations of existing techniques rather than novel approaches to feature learning. Traditionally, hyper-parameter optimization has been the job of humans because they can be very efficient in regimes where only a few trials are possible. Presently, computer clusters and GPU processors make it possible to run more trials and we show that algorithmic approaches can find better results. We present hyper-parameter optimization results on tasks of training neural networks and deep belief networks (DBNs). We optimize hyper-parameters using random search and two new greedy sequential methods based on the expected improvement criterion. Random search has been shown to be sufficiently efficient for learning neural networks for several datasets, but we show it is unreliable for training DBNs. The sequential algorithms are applied to the most difficult DBN learning problems from [Larochelle et al., 2007] and find significantly better results than the best previously reported. This work contributes novel techniques for making response surface models P (y|x) in which many elements of hyper-parameter assignment (x) are known to be irrelevant given particular values of other elements.
Neural Reconstruction with Approximate Message Passing (NeuRAMP)
Authors: Alyson K. Fletcher, Sundeep Rangan, Lav R. Varshney, Aniruddha Bhargava
Date: 2011-12-01 00:00:00
Proceeding: NeurIPS 2011 12
Tasks: compressed sensing, parameter estimation

Many functional descriptions of spiking neurons assume a cascade structure where inputs are passed through an initial linear filtering stage that produces a low-dimensional signal that drives subsequent nonlinear stages.  This paper presents a novel and systematic parameter estimation procedure for such models and applies the method to two neural estimation problems: (i) compressed-sensing based neural mapping from multi-neuron excitation, and (ii) estimation of neural receptive yields in sensory neurons.  The proposed estimation algorithm models the neurons via a graphical model and then estimates the parameters in the model using a recently-developed generalized approximate message passing (GAMP) method.  The GAMP method is based on Gaussian approximations of loopy belief propagation.  In the neural connectivity problem, the GAMP-based   method is shown to be computational efficient, provides a more exact modeling of the sparsity, can incorporate nonlinearities in the output and significantly outperforms previous compressed-sensing methods.  For the receptive field estimation, the GAMP method can also exploit inherent structured sparsity in the linear weights.  The method is validated on estimation of linear nonlinear Poisson (LNP) cascade models for receptive fields of salamander retinal ganglion cells.
Query-Aware MCMC
Authors: Michael L. Wick, Andrew McCallum
Date: 2011-12-01 00:00:00
Proceeding: NeurIPS 2011 12
Tasks: Computational Efficiency

Traditional approaches to probabilistic inference such as loopy belief propagation and Gibbs sampling typically compute marginals for it all the unobserved variables in a graphical model. However, in many real-world applications the user's interests are focused on a subset of the variables, specified by a query. In this case it would be wasteful to uniformly sample, say, one million variables when the query concerns only ten. In this paper we propose a query-specific approach to MCMC that accounts for the query variables and their generalized mutual information with neighboring variables in order to achieve higher computational efficiency. Surprisingly there has been almost no previous work on query-aware MCMC. We demonstrate the success of our approach with positive experimental results on a wide range of graphical models.
A reinterpretation of the policy oscillation phenomenon in approximate policy iteration
Authors: Paul Wagner
Date: 2011-12-01 00:00:00
Proceeding: NeurIPS 2011 12
Tasks: Policy Gradient Methods, Reinforcement Learning

A majority of approximate dynamic programming approaches to the reinforcement learning problem can be categorized into greedy value function methods and value-based policy gradient methods. The former approach, although fast, is well known to be susceptible to the policy oscillation phenomenon. We take a fresh view to this phenomenon by casting a considerable subset of the former approach as a limiting special case of the latter. We explain the phenomenon in terms of this view and illustrate the underlying mechanism with artificial examples. We also use it to derive the constrained natural actor-critic algorithm that can interpolate between the aforementioned approaches. In addition, it has been suggested in the literature that the oscillation phenomenon might be subtly connected to the grossly suboptimal performance in the Tetris benchmark problem of all attempted approximate dynamic programming methods. We report empirical evidence against such a connection and in favor of an alternative explanation. Finally, we report scores in the Tetris problem that improve on existing dynamic programming based results.
Inferring spike-timing-dependent plasticity from spike train data
Authors: Ian Stevenson, Konrad Koerding
Date: 2011-12-01 00:00:00
Proceeding: NeurIPS 2011 12

Synaptic plasticity underlies learning and is thus central for development, memory, and recovery from injury. However, it is often difficult to detect changes in synaptic strength in vivo, since intracellular recordings are experimentally challenging. Here we present two methods aimed at inferring changes in the coupling between pairs of neurons from extracellularly recorded spike trains. First, using a generalized bilinear model with Poisson output we estimate time-varying coupling assuming that all changes are spike-timing-dependent. This approach allows model-based estimation of STDP modification functions from pairs of spike trains. Then, using recursive point-process adaptive filtering methods we estimate more general variation in coupling strength over time. Using simulations of neurons undergoing spike-timing dependent modification, we show that the true modification function can be recovered. Using multi-electrode data from motor cortex we then illustrate the use of this technique on in vivo data.
Automated Refinement of Bayes Networks' Parameters based on Test Ordering Constraints
Authors: Omar Z. Khan, Pascal Poupart, John-Mark M. Agosta
Date: 2011-12-01 00:00:00
Proceeding: NeurIPS 2011 12
Tasks: Diagnostic

In this paper, we derive a method to refine a Bayes network diagnostic model by exploiting constraints implied by expert decisions on test ordering. At each step, the expert executes an evidence gathering test, which suggests the test's relative diagnostic value. We demonstrate that consistency with an expert's test selection leads to non-convex constraints on the model parameters.  We incorporate these constraints by augmenting the network with nodes that represent the constraint likelihoods. Gibbs sampling, stochastic hill climbing and greedy search algorithms are proposed to find a MAP estimate that takes into account test ordering constraints and any data available. We demonstrate our approach on diagnostic sessions from a manufacturing scenario.
A Collaborative Mechanism for Crowdsourcing Prediction Problems
Authors: Jacob D. Abernethy, Rafael M. Frongillo
Date: 2011-12-01 00:00:00
Proceeding: NeurIPS 2011 12
Tasks: Prediction

Machine Learning competitions such as the Netflix Prize have proven reasonably successful as a method of “crowdsourcing” prediction tasks. But these compe- titions have a number of weaknesses, particularly in the incentive structure they create for the participants. We propose a new approach, called a Crowdsourced Learning Mechanism, in which participants collaboratively “learn” a hypothesis for a given prediction task. The approach draws heavily from the concept of a prediction market, where traders bet on the likelihood of a future event. In our framework, the mechanism continues to publish the current hypothesis, and par- ticipants can modify this hypothesis by wagering on an update. The critical in- centive property is that a participant will profit an amount that scales according to how much her update improves performance on a released test set.
Hierarchically Supervised Latent Dirichlet Allocation
Authors: Adler J. Perotte, Frank Wood, Noemie Elhadad, Nicholas Bartlett
Date: 2011-12-01 00:00:00
Proceeding: NeurIPS 2011 12
Tasks: Product Categorization

We introduce hierarchically supervised latent Dirichlet allocation (HSLDA), a model for hierarchically and multiply labeled bag-of-word data. Examples of such data include web pages and their placement in directories, product descriptions and associated categories from product hierarchies, and free-text clinical records and their assigned diagnosis codes. Out-of-sample label prediction is the primary goal of this work, but improved lower-dimensional representations of the bag-of-word data are also of interest. We demonstrate HSLDA on large-scale data from clinical document labeling and retail product categorization tasks. We show that leveraging the structure from hierarchical labels improves out-of-sample label prediction substantially when compared to models that do not.
Select and Sample - A Model of Efficient Neural Inference and Learning
Authors: Jacquelyn A. Shelton, Abdul S. Sheikh, Pietro Berkes, Joerg Bornschein, Joerg Luecke
Date: 2011-12-01 00:00:00
Proceeding: NeurIPS 2011 12

An increasing number of experimental studies indicate that perception encodes a posterior probability distribution over possible causes of sensory stimuli, which is used to act close to optimally in the environment. One outstanding difficulty with this hypothesis is that the exact posterior will in general be too complex to be represented directly, and thus neurons will have to represent an approximation of this distribution. Two influential proposals of efficient posterior representation by neural populations are: 1) neural activity represents samples of the underlying distribution, or 2) they represent a parametric representation of a variational approximation of the posterior. We show that these approaches can be combined for an inference scheme that retains the advantages of both: it is able to represent multiple modes and arbitrary correlations, a feature of sampling methods, and it reduces the represented space to regions of high probability mass, a strength of variational approximations. Neurally, the combined method can be interpreted as a feed-forward preselection of the relevant state space, followed by a neural dynamics implementation of Markov Chain Monte Carlo (MCMC) to approximate the posterior over the relevant states. We demonstrate the effectiveness and efficiency of this approach on a sparse coding model. In numerical experiments on artificial data and image patches, we compare the performance of the algorithms to that of exact EM, variational state space selection alone, MCMC alone, and the combined select and sample approach. The select and sample approach integrates the advantages of the sampling and variational approximations, and forms a robust, neurally plausible, and very efficient model of processing and learning in cortical networks. For sparse coding we show applications easily exceeding a thousand observed and a thousand hidden dimensions.
Selecting the State-Representation in Reinforcement Learning
Authors: Odalric-Ambrym Maillard, Daniil Ryabko, Rémi Munos
Date: 2011-12-01 00:00:00
Proceeding: NeurIPS 2011 12
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The problem of selecting the right state-representation in a reinforcement learning problem is considered.   Several models (functions mapping past observations to a finite set) of the observations are given, and it is known   that for at least one of these models the resulting state dynamics are indeed Markovian. Without  knowing neither which of the models is the correct one, nor what are the probabilistic characteristics of the resulting   MDP, it is required to obtain as much reward as the optimal policy for the correct model (or for the best of the correct models, if there are several). We propose an algorithm that achieves that,   with a regret of order T^{2/3} where T is the horizon time.
Periodic Finite State Controllers for Efficient POMDP and DEC-POMDP Planning
Authors: Joni K. Pajarinen, Jaakko Peltonen
Date: 2011-12-01 00:00:00
Proceeding: NeurIPS 2011 12

Applications such as robot control and wireless communication require planning under uncertainty. Partially observable Markov decision processes (POMDPs) plan policies for single agents under uncertainty and their decentralized versions (DEC-POMDPs) find a policy for multiple agents. The policy in infinite-horizon POMDP and DEC-POMDP problems has been represented as finite state controllers (FSCs). We introduce a novel class of periodic FSCs, composed of layers connected only to the previous and next layer. Our periodic FSC method finds a deterministic finite-horizon policy and converts it to an initial periodic infinite-horizon policy. This policy is optimized by a new infinite-horizon algorithm to yield deterministic periodic policies, and by a new expectation maximization algorithm to yield stochastic periodic policies. Our method yields better results than earlier planning methods and can compute larger solutions than with regular FSCs.
On the Universality of Online Mirror Descent
Authors: Nati Srebro, Karthik Sridharan, Ambuj Tewari
Date: 2011-12-01 00:00:00
Proceeding: NeurIPS 2011 12

We show that for a general class of convex online learning  problems, Mirror Descent can always achieve a (nearly) optimal regret guarantee.
Demixed Principal Component Analysis
Authors: Wieland Brendel, Ranulfo Romo, Christian K. Machens
Date: 2011-12-01 00:00:00
Proceeding: NeurIPS 2011 12
Tasks: Dimensionality Reduction

In many experiments, the data points collected live in high-dimensional observation spaces, yet can be assigned a set of labels or parameters. In electrophysiological recordings, for instance, the responses of populations of neurons generally depend on mixtures of experimentally controlled parameters. The heterogeneity and diversity of these parameter dependencies can make visualization and interpretation of such data extremely difficult. Standard dimensionality reduction techniques such as principal component analysis (PCA) can provide a succinct and complete description of the data, but the description is constructed independent of the relevant task variables and is often hard to interpret. Here, we start with the assumption that a particularly informative description is one that reveals the dependency of the high-dimensional data on the individual parameters. We show how to modify the loss function of PCA so that the principal components seek to capture both the maximum amount of variance about the data, while also depending on a minimum number of parameters. We call this method demixed principal component analysis (dPCA) as the principal components here segregate the parameter dependencies. We phrase the problem as a probabilistic graphical model, and present a fast Expectation-Maximization (EM) algorithm. We demonstrate the use of this algorithm for electrophysiological data and show that it serves to demix the parameter-dependence of a neural population response.
EigenNet: A Bayesian hybrid of generative and conditional models for sparse learning
Authors: Feng Yan, Yuan Qi
Date: 2011-12-01 00:00:00
Proceeding: NeurIPS 2011 12
Tasks: Sparse Learning, Variable Selection

For many real-world applications, we often need to select correlated variables---such as genetic variations and imaging features associated with Alzheimer's disease---in a high dimensional space. The correlation between variables presents a challenge to classical variable selection methods. To address this challenge, the elastic net has been developed and successfully applied to many applications. Despite its great success, the elastic net does not exploit the correlation information embedded in the data to select correlated variables. To overcome this limitation, we present a novel hybrid model, EigenNet, that uses the eigenstructures of data to guide variable selection. Specifically, it integrates a sparse conditional classification model with a generative model capturing variable correlations in a principled Bayesian framework. We develop an efficient active-set algorithm to estimate the model via evidence maximization. Experiments on synthetic data and imaging genetics data demonstrated the superior predictive performance of the EigenNet over the lasso, the elastic net, and the automatic relevance determination.
Hashing Algorithms for Large-Scale Learning
Authors: Ping Li, Anshumali Shrivastava, Joshua L. Moore, Arnd C. König
Date: 2011-12-01 00:00:00
Proceeding: NeurIPS 2011 12

Minwise hashing is a standard technique in the context of search for efficiently computing set similarities. The recent development of b-bit minwise hashing provides a  substantial improvement by storing only the lowest b bits of each hashed value. In this paper, we demonstrate that  b-bit minwise hashing can be naturally integrated with linear learning algorithms such as linear SVM and logistic regression, to solve large-scale and high-dimensional statistical learning tasks, especially when the data do not fit in memory.   We  compare $b$-bit minwise hashing with  the Count-Min (CM)  and  Vowpal Wabbit (VW) algorithms, which have essentially the same variances as random projections. Our theoretical and empirical comparisons illustrate that b-bit minwise hashing is significantly more accurate (at the same storage cost) than VW (and random projections) for binary data.
Rapid Deformable Object Detection using Dual-Tree Branch-and-Bound
Authors: Iasonas Kokkinos
Date: 2011-12-01 00:00:00
Proceeding: NeurIPS 2011 12
Tasks: object-detection, Object Detection

In this work we use Branch-and-Bound (BB) to efficiently detect objects with deformable part models.  Instead of evaluating the classifier score exhaustively over image locations and scales, we use BB to focus on promising image  locations.  The core problem is to compute bounds that accommodate part deformations; for this we adapt the Dual Trees data structure  to our problem.    We evaluate our approach using  Mixture-of-Deformable Part Models.  We obtain exactly the same results but are 10-20 times faster on average.  We also develop a multiple-object detection  variation of the system, where  hypotheses for 20 categories are inserted in a common   priority queue. For the problem of finding the strongest category in an image this results in up to a 100-fold speedup.
Hierarchical Multitask Structured Output Learning for Large-scale Sequence Segmentation
Authors: Nico Goernitz, Christian Widmer, Georg Zeller, Andre Kahles, Gunnar Rätsch, Sören Sonnenburg
Date: 2011-12-01 00:00:00
Proceeding: NeurIPS 2011 12

We present a novel regularization-based Multitask Learning (MTL) formulation  for Structured Output (SO) prediction for the case of hierarchical task relations.  Structured output learning often results in difﬁcult inference problems and   requires large amounts of training data to obtain accurate models. We propose to  use MTL to exploit information available for related structured output learning  tasks by means of hierarchical regularization. Due to the combination of example   sets, the cost of training models for structured output prediction can easily  become infeasible for real world applications. We thus propose an efﬁcient   algorithm based on bundle methods to solve the optimization problems resulting from  MTL structured output learning. We demonstrate the performance of our approach  on gene ﬁnding problems from the application domain of computational biology.  We show that 1) our proposed solver achieves much faster convergence than previous   methods and 2) that the Hierarchical SO-MTL approach clearly outperforms  considered non-MTL methods.
Predicting response time and error rates in visual search
Authors: Bo Chen, Vidhya Navalpakkam, Pietro Perona
Date: 2011-12-01 00:00:00
Proceeding: NeurIPS 2011 12
Tasks: TAR

A model of human visual search is proposed. It predicts both response time (RT) and error rates (RT) as a function of image parameters such as target contrast and clutter. The model is an ideal observer, in that it optimizes the Bayes ratio of tar- get present vs target absent. The ratio is computed on the firing pattern of V1/V2 neurons, modeled by Poisson distributions. The optimal mechanism for integrat- ing information over time is shown to be a ‘soft max’ of diffusions, computed over the visual field by ‘hypercolumns’ of neurons that share the same receptive field and have different response properties to image features. An approximation of the optimal Bayesian observer, based on integrating local decisions, rather than diffusions, is also derived; it is shown experimentally to produce very similar pre- dictions. A psychophyisics experiment is proposed that may discriminate between which mechanism is used in the human brain.