mtybilly/Instruct2DS huggingface dataset:
Anatomically Constrained Decoding of Finger Flexion from Electrocorticographic Signals
Authors: Zuoguan Wang, Gerwin Schalk, Qiang Ji
Published in: Advances in Neural Information Processing Systems 24  (NIPS 2011) (Main Conference Track), 2011

Abstract: Brain-computer interfaces (BCIs) use brain signals to convey a user's intent. Some BCI approaches begin by decoding kinematic parameters of movements from brain signals, and then proceed to using these signals, in absence of movements, to allow a user to control an output. Recent results have shown that electrocorticographic (ECoG) recordings from the surface of the brain in humans can give information about kinematic parameters (e.g., hand velocity or finger flexion). The decoding approaches in these demonstrations usually employed classical classification/regression algorithms that derive a linear mapping between brain signals and outputs. However, they typically only incorporate little prior information about the target kinematic parameter. In this paper, we show that different types of anatomical constraints that govern finger flexion can be exploited in this context. Specifically, we incorporate these constraints in the construction, structure, and the probabilistic functions of a switched non-parametric dynamic system (SNDS) model. We then apply the resulting SNDS decoder to infer the flexion of individual fingers from the same ECoG dataset used in a recent study. Our results show that the application of the proposed model, which incorporates anatomical constraints, improves decoding performance compared to the results in the previous work. Thus, the results presented in this paper may ultimately lead to neurally controlled hand prostheses with full fine-grained finger articulation.
Efficient coding of natural images with a population of noisy Linear-Nonlinear neurons
Authors: Yan Karklin, Eero P. Simoncelli
Published in: Advances in Neural Information Processing Systems 24  (NIPS 2011) (Main Conference Track), 2011

Abstract: Efficient coding provides a powerful principle for explaining early sensory coding. Most attempts to test this principle have been limited to linear, noiseless models, and when applied to natural images, have yielded oriented filters consistent with responses in primary visual cortex. Here we show that an efficient coding model that incorporates biologically realistic ingredients  input and output noise, nonlinear response functions, and a metabolic cost on the firing rate  predicts receptive fields and response nonlinearities similar to those observed in the retina. Specifically, we develop numerical methods for simultaneously learning the linear filters and response nonlinearities of a population of model neurons, so as to maximize information transmission subject to metabolic costs. When applied to an ensemble of natural images, the method yields filters that are center-surround and nonlinearities that are rectifying. The filters are organized into two populations, with On- and Off-centers, which independently tile the visual space. As observed in the primate retina, the Off-center neurons are more numerous and have filters with smaller spatial extent. In the absence of noise, our method reduces to a generalized version of independent components analysis, with an adapted nonlinear "contrast" function; in this case, the optimal filters are localized and oriented.
Multilinear Subspace Regression: An Orthogonal Tensor Decomposition Approach
Authors: Qibin Zhao, Cesar F. Caiafa, Danilo P. Mandic, Liqing Zhang, Tonio Ball, Andreas Schulze-bonhage, Andrzej S. Cichocki
Published in: Advances in Neural Information Processing Systems 24  (NIPS 2011) (Main Conference Track), 2011

Abstract: A multilinear subspace regression model based on so called latent variable decomposition is introduced. Unlike standard regression methods which typically employ matrix (2D) data representations followed by vector subspace transformations, the proposed approach uses tensor subspace transformations to model common latent variables across both the independent and dependent data. The proposed approach aims to maximize the correlation between the so derived latent variables and is shown to be suitable for the prediction of multidimensional dependent data from multidimensional independent data, where for the estimation of the latent variables we introduce an algorithm based on Multilinear Singular Value Decomposition (MSVD) on a specially defined cross-covariance tensor. It is next shown that in this way we are also able to unify the existing Partial Least Squares (PLS) and N-way PLS regression algorithms within the same framework. Simulations on benchmark synthetic data confirm the advantages of the proposed approach, in terms of its predictive ability and robustness, especially for small sample sizes. The potential of the proposed technique is further illustrated on a real world task of the decoding of human intracranial electrocorticogram (ECoG) from a simultaneously recorded scalp electroencephalograph (EEG).
Large-Scale Sparse Principal Component Analysis with Application to Text Data
Authors: Youwei Zhang, Laurent E. Ghaoui
Published in: Advances in Neural Information Processing Systems 24  (NIPS 2011) (Main Conference Track), 2011

Abstract: Sparse PCA provides a linear combination of small number of features that maximizes variance across data. Although Sparse PCA has apparent advantages compared to PCA, such as better interpretability, it is generally thought to be computationally much more expensive. In this paper, we demonstrate the surprising fact that sparse PCA can be easier than PCA in practice, and that it can be reliably applied to very large data sets. This comes from a rigorous feature elimination  pre-processing result, coupled with the favorable fact that features in real-life data typically have exponentially decreasing variances, which allows for many features to be eliminated. We introduce a fast block coordinate ascent algorithm with much better computational complexity than the existing first-order ones. We provide  experimental results obtained on text corpora involving millions of documents and hundreds of thousands of features. These results illustrate how Sparse PCA can help organize a large corpus of text data in a user-interpretable way, providing an attractive alternative approach to topic models.
Nearest Neighbor based Greedy Coordinate Descent
Authors: Inderjit S. Dhillon, Pradeep K. Ravikumar, Ambuj Tewari
Published in: Advances in Neural Information Processing Systems 24  (NIPS 2011) (Main Conference Track), 2011

Abstract: Increasingly, optimization problems in machine learning, especially those arising from high-dimensional statistical estimation, have a large number of variables. Modern statistical estimators developed over the past decade have statistical or sample complexity that depends only weakly on the number of parameters when there is some structure to the problem, such as sparsity. A central question is whether similar advances can be made in their computational complexity as well. In this paper, we propose strategies that indicate that such advances can indeed be made. In particular, we investigate the greedy coordinate descent algorithm, and note that performing the greedy step efficiently weakens the costly dependence on the problem size provided the solution is sparse. We then propose a suite of methods that perform these greedy steps efficiently by a reduction to nearest neighbor search.   We also devise a more amenable form of greedy descent for composite non-smooth objectives; as well as several approximate variants of such greedy descent. We develop a practical implementation of our algorithm that combines greedy coordinate descent with locality sensitive hashing. Without tuning the latter data structure, we are not only able to significantly speed up the vanilla greedy method, but also outperform cyclic descent when the problem size becomes large. Our results indicate the effectiveness of our nearest neighbor strategies, and also point to many open questions regarding the development of computational geometric techniques tailored towards first-order optimization methods.
Convergent Bounds on the Euclidean Distance
Authors: Yoonho Hwang, Hee-kap Ahn
Published in: Advances in Neural Information Processing Systems 24  (NIPS 2011) (Main Conference Track), 2011

Abstract: Given a set V of n vectors in d-dimensional space, we provide an efficient method  for computing quality upper and lower bounds of the Euclidean distances between  a pair of the vectors in V . For this purpose, we define a distance measure, called  the MS-distance, by using the mean and the standard deviation values of vectors in  V . Once we compute the mean and the standard deviation values of vectors in V in  O(dn) time, the MS-distance between them provides upper and lower bounds of  Euclidean distance between a pair of vectors in V in constant time. Furthermore,  these bounds can be refined further such that they converge monotonically to the  exact Euclidean distance within d refinement steps. We also provide an analysis on  a random sequence of refinement steps which can justify why MS-distance should  be refined to provide very tight bounds in a few steps of a typical sequence. The  MS-distance can be used to various problems where the Euclidean distance is used  to measure the proximity or similarity between objects. We provide experimental  results on the nearest and the farthest neighbor searches.
Video Annotation and Tracking with Active Learning
Authors: Carl Vondrick, Deva Ramanan
Published in: Advances in Neural Information Processing Systems 24  (NIPS 2011) (Main Conference Track), 2011

Abstract: We introduce a novel active learning framework for video annotation. By  judiciously choosing which frames a user should annotate, we can obtain highly  accurate tracks with minimal user effort.  We cast this problem as one of  active learning, and show that we can obtain excellent performance by querying  frames that, if annotated, would produce a large expected change in the  estimated object track. We implement a constrained tracker and compute the  expected change for putative annotations with efficient dynamic programming  algorithms.  We demonstrate our framework on four datasets, including two  benchmark datasets constructed with key frame annotations obtained by Amazon  Mechanical Turk. Our results indicate that we could obtain equivalent labels  for a small fraction of the original cost.
PiCoDes: Learning a Compact Code for Novel-Category Recognition
Authors: Alessandro Bergamo, Lorenzo Torresani, Andrew W. Fitzgibbon
Published in: Advances in Neural Information Processing Systems 24  (NIPS 2011) (Main Conference Track), 2011

Abstract: We introduce PiCoDes: a very compact image descriptor which nevertheless allows high performance on object category recognition. In particular, we address novel-category recognition: the task of defining indexing structures and image representations which enable a large collection of images to be searched for an object category that was not known when the index was built. Instead, the training images defining the category are supplied at query time. We explicitly learn descriptors of a given length (from as small as 16 bytes per image) which have good object-recognition performance. In contrast to previous work in the domain of object recognition, we do not choose an arbitrary intermediate representation, but explicitly learn short codes. In contrast to previous approaches to learn compact codes, we optimize explicitly for (an upper bound on) classification performance. Optimization directly for binary features is difficult and nonconvex, but we present an alternation scheme and convex upper bound which demonstrate excellent performance in practice. PiCoDes of 256 bytes match the accuracy of the current best known classifier for the Caltech256 benchmark, but they decrease the database storage size by a factor of 100 and speed-up the training and testing of novel classes by orders of magnitude.
Linearized Alternating Direction Method with Adaptive Penalty for Low-Rank Representation
Authors: Zhouchen Lin, Risheng Liu, Zhixun Su
Published in: Advances in Neural Information Processing Systems 24  (NIPS 2011) (Main Conference Track), 2011

Abstract: Many machine learning and signal processing problems can be formulated as linearly constrained convex programs, which could be efficiently solved by the alternating direction method (ADM). However, usually the subproblems in ADM are easily solvable only when the linear mappings in the constraints are identities. To address this issue, we propose a linearized ADM (LADM) method by linearizing the quadratic penalty term and adding a proximal term when solving the subproblems. For fast convergence, we also allow the penalty to change adaptively according a novel update rule. We prove the global convergence of LADM with adaptive penalty (LADMAP). As an example, we apply LADMAP to solve low-rank representation (LRR), which is an important subspace clustering technique yet suffers from high computation cost. By combining LADMAP with a skinny SVD representation technique, we are able to reduce the complexity $O(n^3)$  of the original ADM based method to $O(rn^2)$, where $r$ and $n$ are the rank and size of the representation matrix, respectively, hence making LRR possible for large scale applications. Numerical experiments verify that for LRR our LADMAP based methods are much faster than state-of-the-art algorithms.
Sparse Filtering
Authors: Jiquan Ngiam, Zhenghao Chen, Sonia A. Bhaskar, Pang W. Koh, Andrew Y. Ng
Published in: Advances in Neural Information Processing Systems 24  (NIPS 2011) (Main Conference Track), 2011

Abstract: Unsupervised feature learning has been shown to be effective at learning representations that perform well on image, video and audio classification. However, many existing feature learning algorithms are hard to use and require extensive hyperparameter tuning. In this work, we present sparse filtering, a simple new algorithm which is efficient and only has one hyperparameter, the number of features to learn.  In contrast to most other feature learning methods, sparse filtering does not explicitly attempt to construct a model of the data distribution. Instead, it optimizes a simple cost function -- the sparsity of L2-normalized features -- which can easily be implemented in a few lines of MATLAB code. Sparse filtering scales gracefully to handle high-dimensional inputs, and can also be used to learn meaningful features in additional layers with greedy layer-wise stacking. We evaluate sparse filtering on natural images, object classification (STL-10), and phone classification (TIMIT), and show that our method works well on a range of different modalities.
Beyond Spectral Clustering - Tight Relaxations of Balanced Graph Cuts
Authors: Matthias Hein, Simon Setzer
Published in: Advances in Neural Information Processing Systems 24  (NIPS 2011) (Main Conference Track), 2011

Abstract: Spectral clustering is based on the spectral relaxation of the normalized/ratio graph cut criterion. While the  spectral relaxation is known to be loose, it has been shown recently that a non-linear  eigenproblem yields a tight relaxation of the Cheeger cut.   In this paper, we extend this result considerably by providing a   characterization of all balanced graph cuts which allow for a tight relaxation. Although the resulting optimization problems are non-convex and non-smooth, we provide   an efficient first-order scheme which scales to large graphs. Moreover, our approach  comes with the quality guarantee that given any partition as initialization the   algorithm either outputs a better partition or it stops immediately.
Why The Brain Separates Face Recognition From Object Recognition
Authors: Joel Z. Leibo, Jim Mutch, Tomaso Poggio
Published in: Advances in Neural Information Processing Systems 24  (NIPS 2011) (Main Conference Track), 2011

Abstract: Many studies have uncovered evidence that visual cortex contains specialized regions involved in processing faces but not other object classes.  Recent electrophysiology studies of cells in several of these specialized regions revealed that at least some of these regions are organized in a hierarchical manner with viewpoint-specific cells projecting to downstream viewpoint-invariant identity-specific cells (Freiwald and Tsao 2010).  A separate computational line of reasoning leads to the claim that some transformations of visual inputs that preserve viewed object identity are class-specific.  In particular, the 2D images evoked by a face undergoing a 3D rotation are not produced by the same image transformation (2D) that would produce the images evoked by an object of another class undergoing the same 3D rotation.  However, within the class of faces, knowledge of the image transformation evoked by 3D rotation can be reliably transferred from previously viewed faces to help identify a novel face at a new viewpoint.  We show, through computational simulations, that an architecture which applies this method of gaining invariance to class-specific transformations is effective when restricted to faces and fails spectacularly when applied across object classes.  We argue here that in order to accomplish viewpoint-invariant face identification from a single example view, visual cortex must separate the circuitry involved in discounting 3D rotations of faces from the generic circuitry involved in processing other objects.  The resulting model of the ventral stream of visual cortex is consistent with the recent physiology results showing the hierarchical organization of the face processing network.
Analytical Results for the Error in Filtering of Gaussian Processes
Authors: Alex K. Susemihl, Ron Meir, Manfred Opper
Published in: Advances in Neural Information Processing Systems 24  (NIPS 2011) (Main Conference Track), 2011

Abstract: Bayesian filtering of stochastic stimuli has received a great deal of attention re- cently. It has been applied to describe the way in which biological systems dy- namically represent and make decisions about the environment. There have been no exact results for the error in the biologically plausible setting of inference on point process, however. We present an exact analysis of the evolution of the mean- squared error in a state estimation task using Gaussian-tuned point processes as sensors. This allows us to study the dynamics of the error of an optimal Bayesian decoder, providing insights into the limits obtainable in this task. This is done for Markovian and a class of non-Markovian Gaussian processes. We find that there is an optimal tuning width for which the error is minimized. This leads to a char- acterization of the optimal encoding for the setting as a function of the statistics of the stimulus, providing a mathematically sound primer for an ecological theory of sensory processing.
Periodic Finite State Controllers for Efficient POMDP and DEC-POMDP Planning
Authors: Joni K. Pajarinen, Jaakko Peltonen
Published in: Advances in Neural Information Processing Systems 24  (NIPS 2011) (Main Conference Track), 2011

Abstract: 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.
Spectral Methods for Learning Multivariate Latent Tree Structure
Authors: Animashree Anandkumar, Kamalika Chaudhuri, Daniel J. Hsu, Sham M. Kakade, Le Song, Tong Zhang
Published in: Advances in Neural Information Processing Systems 24  (NIPS 2011) (Main Conference Track), 2011

Abstract: This work considers the problem of learning the structure of multivariate linear tree models, which include a variety of directed tree graphical models with continuous, discrete, and mixed latent variables such as linear-Gaussian models, hidden Markov models, Gaussian mixture models, and Markov evolutionary trees.  The setting is one where we only have samples from certain observed variables in the tree, and our goal is to estimate the tree structure (i.e., the graph of how the underlying hidden variables are connected to each other and to the observed variables).  We propose the Spectral Recursive Grouping algorithm, an efficient and simple bottom-up procedure for recovering the tree structure from independent samples of the observed variables.  Our finite sample size bounds for exact recovery of the tree structure  reveal certain natural dependencies on underlying statistical and structural properties of the underlying joint distribution.  Furthermore, our sample complexity guarantees have no explicit dependence on the dimensionality of the observed variables, making the algorithm applicable to many high-dimensional settings.  At the heart of our algorithm is a spectral quartet test for determining the relative topology of a quartet of variables from second-order statistics.
Query-Aware MCMC
Authors: Michael L. Wick, Andrew McCallum
Published in: Advances in Neural Information Processing Systems 24  (NIPS 2011) (Main Conference Track), 2011

Abstract: 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.
Hogwild!: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent
Authors: Benjamin Recht, Christopher Re, Stephen Wright, Feng Niu
Published in: Advances in Neural Information Processing Systems 24  (NIPS 2011) (Main Conference Track), 2011

Abstract: Stochastic Gradient Descent (SGD) is a popular algorithm that can achieve state-of-the-art performance on a variety of machine  learning tasks.  Several researchers have recently proposed schemes  to parallelize SGD, but all require  performance-destroying memory locking and synchronization. This work aims to show using novel theoretical analysis, algorithms,  and implementation that SGD can be implemented without any locking. We present an update scheme called Hogwild which allows processors access to shared memory with the possibility of overwriting each other's work. We show that when the associated optimization problem is sparse, meaning most gradient updates only modify small parts of the decision variable, then Hogwild achieves a nearly optimal rate of convergence.  We demonstrate experimentally that Hogwild outperforms alternative schemes that use locking by an order of magnitude.
ICA with Reconstruction Cost for Efficient Overcomplete Feature Learning
Authors: Quoc V. Le, Alexandre Karpenko, Jiquan Ngiam, Andrew Y. Ng
Published in: Advances in Neural Information Processing Systems 24  (NIPS 2011) (Main Conference Track), 2011

Abstract: Independent Components Analysis (ICA) and its variants have been successfully used for unsupervised feature learning. However, standard ICA requires an orthonoramlity constraint to be enforced, which makes it difﬁcult to learn overcomplete features. In addition, ICA is sensitive to whitening. These properties make it challenging to scale ICA to high dimensional data. In this paper, we propose a robust soft reconstruction cost for ICA that allows us to learn highly overcomplete sparse features even on unwhitened data. Our formulation reveals formal connections between ICA and sparse autoencoders, which have previously been observed only empirically. Our algorithm can be used in conjunction with off-the-shelf fast unconstrained optimizers. We show that the soft reconstruction cost can also be used to prevent replicated features in tiled convolutional neural networks. Using our method to learn highly overcomplete sparse features and tiled convolutional neural networks, we obtain competitive performances on a wide variety of object recognition tasks. We achieve state-of-the-art test accuracies on the STL-10 and Hollywood2 datasets.
Sparse Recovery with Brownian Sensing
Authors: Alexandra Carpentier, Odalric-ambrym Maillard, Rémi Munos
Published in: Advances in Neural Information Processing Systems 24  (NIPS 2011) (Main Conference Track), 2011

Abstract: We consider the problem of recovering the parameter alpha in R^K of a sparse function f, i.e. the number of non-zero entries of alpha is small compared to the number K of features, given noisy evaluations of f at a set of well-chosen sampling points. We introduce an additional randomisation process, called Brownian sensing, based on the computation of stochastic integrals, which produces a Gaussian sensing matrix, for which good recovery properties are proven independently on the number of sampling points N, even when the features are arbitrarily non-orthogonal. Under the assumption that f is Hölder continuous with exponent at least 1/2, we provide an estimate a of the parameter such that ||\alpha - a||2 = O(||eta||2\sqrt{N}), where eta is the observation noise. The method uses a set of sampling points uniformly distributed along a one-dimensional curve selected according to the features. We report numerical experiments illustrating our method.
Learning Anchor Planes for Classification
Authors: Ziming Zhang, Lubor Ladicky, Philip Torr, Amir Saffari
Published in: Advances in Neural Information Processing Systems 24  (NIPS 2011) (Main Conference Track), 2011

Abstract: Local Coordinate Coding (LCC) [18] is a method for modeling functions of data lying on non-linear manifolds. It provides a set of anchor points which form a local coordinate system, such that each data point on the manifold can be approximated by a linear combination of its anchor points, and the linear weights become the local coordinate coding. In this paper we propose encoding data using orthogonal  anchor planes, rather than anchor points. Our method needs only a few orthogonal anchor planes for coding, and it can linearize any (\alpha,\beta,p)-Lipschitz smooth nonlinear  function with a fixed expected value of the upper-bound approximation error on any high dimensional data. In practice, the orthogonal coordinate system can be easily learned by minimizing this upper bound using singular value decomposition (SVD). We apply our method to model the coordinates locally in linear SVMs for classification tasks, and our experiment on MNIST shows that using only 50 anchor planes our method achieves 1.72% error rate, while LCC achieves 1.90% error rate using 4096 anchor points.
Ranking annotators for crowdsourced labeling tasks
Authors: Vikas C. Raykar, Shipeng Yu
Published in: Advances in Neural Information Processing Systems 24  (NIPS 2011) (Main Conference Track), 2011

Abstract: With the advent of crowdsourcing services it has become quite cheap and reasonably effective to get a dataset labeled by multiple annotators in a short amount of time. Various methods have been proposed to estimate the consensus labels by correcting for the bias of annotators with different kinds of expertise. Often we have low quality annotators or spammers--annotators who assign labels randomly (e.g., without actually looking at the instance). Spammers can make the cost of acquiring labels very expensive and can potentially degrade the quality of the consensus labels. In this paper we formalize the notion of a spammer and define a score which can be used to rank the annotators---with the spammers having a score close to zero and the good annotators having a high score close to one.
Metric Learning with Multiple Kernels
Authors: Jun Wang, Huyen T. Do, Adam Woznica, Alexandros Kalousis
Published in: Advances in Neural Information Processing Systems 24  (NIPS 2011) (Main Conference Track), 2011

Abstract: Metric learning has become a very active research field. The most popular representative--Mahalanobis metric learning--can be seen as learning a linear transformation and then computing the Euclidean metric in the transformed space. Since a linear transformation might not always be appropriate for a given learning problem, kernelized versions of various metric learning algorithms exist. However, the problem then becomes finding the appropriate kernel function. Multiple kernel learning addresses this limitation by learning a linear combination of a number of predefined kernels; this approach can be also readily used in the context of multiple-source learning to fuse different data sources. Surprisingly, and despite the extensive work on multiple kernel learning for SVMs, there has been no work in   the area of metric learning with multiple kernel learning. In this paper we fill this gap and present a general approach for metric learning with multiple kernel learning. Our approach can be instantiated with different metric learning algorithms provided that they satisfy some constraints. Experimental evidence suggests that our approach outperforms metric learning with an unweighted kernel combination and metric learning with cross-validation based kernel selection.
A Brain-Machine Interface Operating with a Real-Time Spiking Neural Network Control Algorithm
Authors: Julie Dethier, Paul Nuyujukian, Chris Eliasmith, Terrence C. Stewart, Shauki A. Elasaad, Krishna V. Shenoy, Kwabena A. Boahen
Published in: Advances in Neural Information Processing Systems 24  (NIPS 2011) (Main Conference Track), 2011

Abstract: Motor prostheses aim to restore function to disabled patients. Despite compelling proof of concept systems, barriers to clinical translation remain. One challenge is to develop a low-power, fully-implantable system that dissipates only minimal power so as not to damage tissue. To this end, we implemented a Kalman-filter based decoder via a spiking neural network (SNN) and tested it in brain-machine interface (BMI) experiments with a rhesus monkey. The Kalman filter was trained to predict the arm’s velocity and mapped on to the SNN using the Neural Engineer- ing Framework (NEF). A 2,000-neuron embedded Matlab SNN implementation runs in real-time and its closed-loop performance is quite comparable to that of the standard Kalman filter. The success of this closed-loop decoder holds promise for hardware SNN implementations of statistical signal processing algorithms on neuromorphic chips, which may offer power savings necessary to overcome a major obstacle to the successful clinical translation of neural motor prostheses.
Understanding the Intrinsic Memorability of Images
Authors: Phillip Isola, Devi Parikh, Antonio Torralba, Aude Oliva
Published in: Advances in Neural Information Processing Systems 24  (NIPS 2011) (Main Conference Track), 2011

Abstract: Artists, advertisers, and photographers are routinely presented with the task of creating an image that a viewer will remember. While it may seem like image memorability is purely subjective, recent work shows that it is not an inexplicable phenomenon: variation in memorability of images is consistent across subjects, suggesting that some images are intrinsically more memorable than others, independent of a subjects' contexts and biases. In this paper, we used the publicly available memorability dataset of Isola et al., and augmented the object and scene annotations with interpretable spatial, content, and aesthetic image properties. We used a feature-selection scheme with desirable explaining-away properties to determine a compact set of attributes that characterizes the memorability of any individual image. We find that images of enclosed spaces containing people with visible faces are memorable, while images of vistas and peaceful scenes are not. Contrary to popular belief, unusual or aesthetically pleasing scenes do not tend to be highly memorable. This work represents one of the first attempts at understanding intrinsic image memorability, and opens a new domain of investigation at the interface between human cognition and computer vision.
Two is better than one: distinct roles for familiarity and recollection in retrieving palimpsest memories
Authors: Cristina Savin, Peter Dayan, Máté Lengyel
Published in: Advances in Neural Information Processing Systems 24  (NIPS 2011) (Main Conference Track), 2011

Abstract: Storing a new pattern in a palimpsest memory system comes at the cost of interfering with the memory traces of previously stored items. Knowing the age of a pattern thus becomes critical for recalling it faithfully. This implies that there should be a tight coupling between estimates of age, as a form of familiarity, and the neural dynamics of recollection, something which current theories omit. Using a normative model of autoassociative memory, we show that a dual memory system, consisting of two interacting modules for familiarity and recollection, has best performance for both recollection and recognition. This finding provides a new window onto actively contentious psychological and neural aspects of recognition memory.
Automated Refinement of Bayes Networks' Parameters based on Test Ordering Constraints
Authors: Omar Z. Khan, Pascal Poupart, John-mark M. Agosta
Published in: Advances in Neural Information Processing Systems 24  (NIPS 2011) (Main Conference Track), 2011

Abstract: 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.
Structure Learning for Optimization
Authors: Shulin Yang, Ali Rahimi
Published in: Advances in Neural Information Processing Systems 24  (NIPS 2011) (Main Conference Track), 2011

Abstract: We describe a family of global optimization procedures that automatically decompose optimization problems into smaller loosely coupled problems, then combine the solutions of these with message passing algorithms.  We show empirically that these methods excel in avoiding local minima and produce better solutions with fewer function evaluations than existing global optimization methods.  To develop these methods, we introduce a notion of coupling between variables of optimization that generalizes the notion of coupling that arises from factoring functions into terms that involve small subsets of the variables. It therefore subsumes the notion of independence between random variables in statistics, sparseness of the Hessian in nonlinear optimization, and the generalized distributive law. Despite being more general, this notion of coupling is easier to verify empirically -- making structure estimation easy -- yet it allows us to migrate well-established inference methods on graphical models to the setting of global optimization.
Multiclass Boosting: Theory and Algorithms
Authors: Mohammad J. Saberian, Nuno Vasconcelos
Published in: Advances in Neural Information Processing Systems 24  (NIPS 2011) (Main Conference Track), 2011

Abstract: The problem of  multiclass boosting is considered. A new framework,based on multi-dimensional codewords and predictors is introduced. The optimal set of codewords is derived, and a margin enforcing loss proposed. The resulting risk is minimized by gradient descent on a multidimensional functional space. Two algorithms are proposed: 1) CD-MCBoost, based on coordinate descent, updates one predictor component at a time, 2) GD-MCBoost, based on gradient descent, updates all components jointly. The algorithms differ in the weak learners that they support but are both shown to be 1) Bayes consistent, 2) margin enforcing, and 3) convergent to the global minimum of the risk. They also reduce to AdaBoost when there are only two classes. Experiments show that both methods outperform previous multiclass boosting approaches on a number of datasets.
Composite Multiclass Losses
Authors: Elodie Vernet, Mark D. Reid, Robert C. Williamson
Published in: Advances in Neural Information Processing Systems 24  (NIPS 2011) (Main Conference Track), 2011

Abstract: We consider loss functions for multiclass prediction problems. We   show when a  multiclass loss can be expressed as a proper   composite loss'', which is the composition of a proper loss and a link   function. We extend existing results for binary losses to   multiclass losses.  We determine the stationarity condition,   Bregman representation, order-sensitivity, existence and uniqueness   of the composite representation for multiclass losses.  We also   show that the integral representation  for binary proper losses can   not be extended to  multiclass losses. We subsume existing results   onclassification calibration'' by relating it to properness.  We   draw conclusions concerning the design of multiclass losses.
Scalable Training of Mixture Models via Coresets
Authors: Dan Feldman, Matthew Faulkner, Andreas Krause
Published in: Advances in Neural Information Processing Systems 24  (NIPS 2011) (Main Conference Track), 2011

Abstract: How can we train a statistical mixture model on a massive data set? In this paper, we show how to construct coresets for mixtures of Gaussians and natural generalizations. A coreset is a weighted subset of the data, which guarantees that models fitting the coreset will also provide a good fit for the original data set. We show that, perhaps surprisingly, Gaussian mixtures admit coresets of size independent of the size of the data set. More precisely, we prove that a weighted set of $O(dk^3/\eps^2)$ data points suffices for computing a $(1+\eps)$-approximation for the optimal model on the original $n$ data points. Moreover, such coresets can be efficiently constructed in a map-reduce style computation, as well as in a streaming setting. Our results rely on a novel reduction of statistical estimation to problems in computational geometry, as well as new complexity results about mixtures of Gaussians. We empirically evaluate our algorithms on several real data sets, including a density estimation problem in the context of earthquake detection using accelerometers in  mobile phones.
Recovering Intrinsic Images with a Global Sparsity Prior on Reflectance
Authors: Carsten Rother, Martin Kiefel, Lumin Zhang, Bernhard Schölkopf, Peter V. Gehler
Published in: Advances in Neural Information Processing Systems 24  (NIPS 2011) (Main Conference Track), 2011

Abstract: We address the challenging task of decoupling material properties from lighting properties given a single image. In the last two decades virtually all works have concentrated on exploiting edge information to address this problem. We take a different route by introducing a new prior on reflectance, that models reflectance values as being drawn from a sparse set of basis colors. This results in a Random Field model with global, latent variables (basis colors) and pixel-accurate output reflectance values. We show that without edge information high-quality results can be achieved, that are on par with methods exploiting this source of information. Finally, we present competitive results by integrating an additional edge model. We believe that our approach is a solid starting point for future development in this domain.
Sparse Inverse Covariance Matrix Estimation Using Quadratic Approximation
Authors: Cho-jui Hsieh, Inderjit S. Dhillon, Pradeep K. Ravikumar, Mátyás A. Sustik
Published in: Advances in Neural Information Processing Systems 24  (NIPS 2011) (Main Conference Track), 2011

Abstract: The L_1 regularized Gaussian maximum likelihood estimator has been shown to have strong statistical guarantees in recovering a sparse inverse covariance matrix, or alternatively the underlying graph structure of a Gaussian Markov Random Field, from very limited samples. We propose a novel algorithm for solving the resulting optimization problem which is a regularized log-determinant program.  In contrast to other state-of-the-art methods that largely use first order gradient information, our algorithm is based on Newton's method and employs a quadratic approximation, but with some modifications that leverage the structure of the sparse Gaussian MLE problem. We show that our method is superlinearly convergent, and also present experimental results using synthetic and real application data that demonstrate the considerable improvements in performance of our method when  compared to other state-of-the-art methods.
Testing a Bayesian Measure of Representativeness Using a Large Image Database
Authors: Joshua T. Abbott, Katherine A. Heller, Zoubin Ghahramani, Thomas L. Griffiths
Published in: Advances in Neural Information Processing Systems 24  (NIPS 2011) (Main Conference Track), 2011

Abstract: How do people determine which elements of a set are most representative of that set? We extend an existing Bayesian measure of representativeness, which indicates the representativeness of a sample from a distribution, to define a measure of the representativeness of an item to a set. We show that this measure is formally related to a machine learning method known as Bayesian Sets. Building on this connection, we derive an analytic expression for the representativeness of objects described by a sparse vector of binary features. We then apply this measure to a large database of images, using it to determine which images are the most representative members of different sets. Comparing the resulting predictions to human judgments of representativeness provides a test of this measure with naturalistic stimuli, and illustrates how databases that are more commonly used in computer vision and machine learning can be used to evaluate psychological theories.
Dynamical segmentation of single trials from population neural data
Authors: Biljana Petreska, Byron M. Yu, John P. Cunningham, Gopal Santhanam, Stephen I. Ryu, Krishna V. Shenoy, Maneesh Sahani
Published in: Advances in Neural Information Processing Systems 24  (NIPS 2011) (Main Conference Track), 2011

Abstract: Simultaneous recordings of many neurons embedded within a  recurrently-connected cortical network may provide concurrent views into the dynamical processes of that network, and thus its computational function.  In principle, these dynamics might be identified by purely unsupervised, statistical means.  Here, we show that a Hidden Switching Linear Dynamical Systems (HSLDS) model---in which multiple linear dynamical laws approximate a nonlinear and potentially non-stationary dynamical process---is able to distinguish different dynamical regimes within single-trial motor cortical activity associated with the preparation and initiation of hand movements.  The regimes are identified without reference to behavioural or experimental epochs, but nonetheless transitions between them correlate strongly with external events whose timing may vary from trial to trial.  The HSLDS model also performs better than recent comparable models in predicting the firing rate of an isolated neuron based on the firing rates of others, suggesting that it  captures more of the "shared variance" of the data.  Thus, the method is able to trace the dynamical processes underlying the coordinated evolution of network activity in a way that appears to reflect its computational role.
Approximating Semidefinite Programs in Sublinear Time
Authors: Dan Garber, Elad Hazan
Published in: Advances in Neural Information Processing Systems 24  (NIPS 2011) (Main Conference Track), 2011

Abstract: In recent years semidefinite optimization has become a tool of major importance in various optimization and machine learning problems. In many of these problems the amount of data in practice is so large that there is a constant need for faster algorithms. In this work we present the first  sublinear time approximation algorithm for semidefinite programs which we believe may be useful for such problems in which the size of data may cause even linear time algorithms to have prohibitive running times in practice. We present the algorithm and its analysis alongside with some theoretical lower bounds and an improved algorithm for the special problem of supervised learning of a distance metric.
Active Classification based on Value of Classifier
Authors: Tianshi Gao, Daphne Koller
Published in: Advances in Neural Information Processing Systems 24  (NIPS 2011) (Main Conference Track), 2011

Abstract: Modern classification tasks usually involve many class labels and can be informed by a broad range of features. Many of these tasks are tackled by constructing a set of classifiers, which are then applied at test time and then pieced together in a fixed procedure determined in advance or at training time. We present an active classification process at the test time, where each classifier in a large ensemble is viewed as a potential observation that might inform our classification process. Observations are then selected dynamically based on previous observations, using a value-theoretic computation that balances an estimate of the expected classification gain from each observation as well as its computational cost. The expected classification gain is computed using a probabilistic model that uses the outcome from previous observations. This active classification process is applied at test time for each individual test instance, resulting in an efficient instance-specific decision path. We demonstrate the benefit of the active scheme on various real-world datasets, and show that it can achieve comparable or even higher classification accuracy at a fraction of the computational costs of traditional methods.
Efficient Learning of Generalized Linear and Single Index Models with Isotonic Regression
Authors: Sham M. Kakade, Varun Kanade, Ohad Shamir, Adam Kalai
Published in: Advances in Neural Information Processing Systems 24  (NIPS 2011) (Main Conference Track), 2011

Abstract: Generalized Linear Models (GLMs) and Single Index Models (SIMs) provide powerful generalizations of linear regression, where the target variable is assumed to be a (possibly unknown) 1-dimensional function of a linear predictor. In general, these problems entail non-convex estimation procedures, and, in practice, iterative local search heuristics are often used.  Kalai and Sastry (2009) provided the first provably efficient method, the \emph{Isotron} algorithm, for learning SIMs and GLMs, under the assumption that the data is in fact generated under a GLM and under certain monotonicity and Lipschitz (bounded slope) constraints. The Isotron algorithm interleaves steps of perceptron-like updates with isotonic regression (fitting a one-dimensional non-decreasing function). However, to obtain provable performance, the method requires a fresh sample every iteration. In this paper, we provide algorithms for learning GLMs and SIMs, which are both computationally and statistically efficient. We modify the isotonic regression step in Isotron to fit a Lipschitz monotonic function, and also provide an efficient $O(n \log(n))$ algorithm for this step, improving upon the previous $O(n^2)$ algorithm. We provide a brief empirical study, demonstrating the feasibility of our algorithms in practice.