mtybilly/Instruct2DS huggingface dataset:
Learning and Forecasting Opinion Dynamics in Social Networks
Authors: Abir De, Isabel Valera, Niloy Ganguly, Sourangshu Bhattacharya, Manuel Gomez Rodriguez
Published in: Advances in Neural Information Processing Systems 29  (NIPS 2016) (Main Conference Track), 2016

Abstract: Social media and social networking sites have become a global pinboard for exposition and discussion of news, topics, and ideas, where social media users often update their opinions about a particular topic by learning from the opinions shared by their friends. In this context, can we learn a data-driven model of opinion dynamics that is able to accurately forecast users' opinions? In this paper, we introduce SLANT, a probabilistic modeling framework of opinion dynamics, which represents users' opinions over time by means of marked jump  diffusion stochastic differential equations, and allows for efficient model simulation and parameter estimation from historical fine grained event data. We then leverage our framework to derive a set of efficient predictive formulas for opinion forecasting and identify conditions under which opinions converge to a steady state. Experiments on data gathered from Twitter show that our model provides a good fit to the data and our formulas achieve more accurate forecasting than alternatives.
Lifelong Learning with Weighted Majority Votes
Authors: Anastasia Pentina, Ruth Urner
Published in: Advances in Neural Information Processing Systems 29  (NIPS 2016) (Main Conference Track), 2016

Abstract: Better understanding of the potential benefits of information transfer and representation learning is an important step towards the goal of building intelligent systems that are able to persist in the world and learn over time. In this work, we consider a setting where the learner encounters a stream of tasks but is able to retain only limited information from each encountered task, such as a learned predictor. In contrast to most previous works analyzing this scenario, we do not make any distributional assumptions on the task generating process. Instead, we formulate a complexity measure that captures the diversity of the observed tasks. We provide a lifelong learning algorithm with error guarantees for every observed task (rather than on average). We show sample complexity reductions in comparison to solving every task in isolation in terms of our task complexity measure. Further, our algorithmic framework can naturally be viewed as learning a representation from encountered tasks with a neural network.
Depth from a Single Image by Harmonizing Overcomplete Local Network Predictions
Authors: Ayan Chakrabarti, Jingyu Shao, Greg Shakhnarovich
Published in: Advances in Neural Information Processing Systems 29  (NIPS 2016) (Main Conference Track), 2016

Abstract: A single color image can contain many cues informative towards different aspects of local geometric structure. We approach the problem of monocular depth estimation by using a neural network to produce a mid-level representation that summarizes these cues. This network is trained to characterize local scene geometry by predicting, at every image location, depth derivatives of different orders, orientations and scales. However, instead of a single estimate for each derivative, the network outputs probability distributions that allow it to express confidence about some coefficients, and ambiguity about others. Scene depth is then estimated by harmonizing this overcomplete set of network predictions, using a globalization procedure that finds a single consistent depth map that best matches all the local derivative distributions. We demonstrate the efficacy of this approach through evaluation on the NYU v2 depth data set.
Ancestral Causal Inference
Authors: Sara Magliacane, Tom Claassen, Joris M. Mooij
Published in: Advances in Neural Information Processing Systems 29  (NIPS 2016) (Main Conference Track), 2016

Abstract: Constraint-based causal discovery from limited data is a notoriously difficult challenge due to the many borderline independence test decisions.  Several approaches to improve the reliability of the predictions by exploiting redundancy in the independence information have been proposed recently. Though promising, existing approaches can still be greatly improved in terms of accuracy and scalability. We present a novel method that reduces the combinatorial explosion of the search space by using a more coarse-grained representation of causal information, drastically reducing computation time. Additionally, we propose a method to score causal predictions based on their confidence. Crucially, our implementation also allows one to easily combine observational and interventional data and to incorporate various types of available background knowledge.  We prove soundness and asymptotic consistency of our method and demonstrate that it can outperform the state-of-the-art on synthetic data, achieving a speedup of several orders of magnitude. We illustrate its practical feasibility by applying it on a challenging protein data set.
Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation
Authors: Tejas D Kulkarni, Karthik Narasimhan, Ardavan Saeedi, Josh Tenenbaum
Published in: Advances in Neural Information Processing Systems 29  (NIPS 2016) (Main Conference Track), 2016

Abstract: Learning goal-directed behavior in environments with sparse feedback is a major challenge for reinforcement learning algorithms. One of the key difficulties is insufficient exploration, resulting in an agent being unable to learn robust policies. Intrinsically motivated agents can explore new behavior for their own sake rather than to directly solve external goals. Such intrinsic behaviors could eventually help the agent solve tasks posed by the environment. We present hierarchical-DQN (h-DQN), a framework to integrate hierarchical action-value functions, operating at different temporal scales, with goal-driven intrinsically motivated deep reinforcement learning. A top-level q-value function learns a policy over intrinsic goals, while a lower-level function learns a policy over atomic actions to satisfy the given goals. h-DQN allows for flexible goal specifications, such as functions over entities and relations. This provides an efficient space for exploration in complicated environments. We demonstrate the strength of our approach on two problems with very sparse and delayed feedback: (1) a complex discrete stochastic decision process with stochastic transitions, and (2) the classic ATARI game -- `Montezuma's Revenge'.
Agnostic Estimation for Misspecified Phase Retrieval Models
Authors: Matey Neykov, Zhaoran Wang, Han Liu
Published in: Advances in Neural Information Processing Systems 29  (NIPS 2016) (Main Conference Track), 2016

Abstract: The goal of noisy high-dimensional phase retrieval is to estimate an $s$-sparse parameter $\boldsymbol{\beta}^*\in \mathbb{R}^d$ from $n$ realizations of the model $Y = (\boldsymbol{X}^{\top} \boldsymbol{\beta}^*)^2 + \varepsilon$. Based on this model, we propose a significant semi-parametric generalization called misspecified phase retrieval (MPR), in which $Y = f(\boldsymbol{X}^{\top}\boldsymbol{\beta}^*, \varepsilon)$ with unknown $f$ and $\operatorname{Cov}(Y, (\boldsymbol{X}^{\top}\boldsymbol{\beta}^*)^2) > 0$. For example, MPR encompasses $Y = h(|\boldsymbol{X}^{\top} \boldsymbol{\beta}^*|) + \varepsilon$ with increasing $h$ as a special case. Despite the generality of the MPR model, it eludes the reach of most existing semi-parametric estimators. In this paper, we propose an estimation procedure, which consists of solving a cascade of two convex programs and provably recovers the direction of $\boldsymbol{\beta}^*$. Our theory is backed up by thorough numerical results.
Can Peripheral Representations Improve Clutter Metrics on Complex Scenes?
Authors: Arturo Deza, Miguel Eckstein
Published in: Advances in Neural Information Processing Systems 29  (NIPS 2016) (Main Conference Track), 2016

Abstract: Previous studies have proposed image-based clutter measures that correlate with human search times and/or eye movements. However, most models do not take into account the fact that the effects of clutter interact with the foveated nature of the human visual system: visual clutter further from the fovea has an increasing detrimental influence on perception. Here, we introduce a new foveated clutter model to predict the detrimental effects in target search utilizing a forced fixation search task. We use Feature Congestion (Rosenholtz et al.) as our non foveated clutter model, and we stack a peripheral architecture on top of Feature Congestion for our foveated model. We introduce the Peripheral Integration Feature Congestion (PIFC) coefficient, as a fundamental ingredient of our model that modulates clutter as a non-linear gain contingent on eccentricity. We finally show that Foveated Feature Congestion (FFC) clutter scores (r(44) = −0.82 ± 0.04, p < 0.0001) correlate better with target detection (hit rate) than regular Feature Congestion (r(44) = −0.19 ± 0.13, p = 0.0774) in forced fixation search; and we extend foveation to other clutter models showing stronger correlations in all cases. Thus, our model allows us to enrich clutter perception research by computing fixation specific clutter maps. Code for building peripheral representations is  available.
Proximal Deep Structured Models
Authors: Shenlong Wang, Sanja Fidler, Raquel Urtasun
Published in: Advances in Neural Information Processing Systems 29  (NIPS 2016) (Main Conference Track), 2016

Abstract: Many problems in real-world applications involve predicting continuous-valued random variables that are statistically related.  In this paper, we propose a powerful deep structured model that is able to learn complex non-linear functions which encode the   dependencies between continuous output variables.  We show that  inference in our  model using proximal methods can be efficiently solved as a feed-foward pass of a special  type of  deep recurrent neural network. We demonstrate the  effectiveness of our approach in the tasks of image denoising, depth refinement and optical flow estimation.
SPALS: Fast Alternating Least Squares via Implicit Leverage Scores Sampling
Authors: Dehua Cheng, Richard Peng, Yan Liu, Ioakeim Perros
Published in: Advances in Neural Information Processing Systems 29  (NIPS 2016) (Main Conference Track), 2016

Abstract: Tensor CANDECOMP/PARAFAC (CP) decomposition is a powerful but computationally challenging tool in modern data analytics. In this paper, we show ways of sampling intermediate steps of alternating minimization algorithms for computing low rank tensor CP decompositions, leading to the sparse alternating least squares (SPALS) method. Specifically, we sample the the Khatri-Rao product, which arises as an intermediate object during the iterations of alternating least squares. This product captures the interactions between different tensor modes, and form the main computational bottleneck for solving many tensor related tasks. By exploiting the spectral structures of the matrix Khatri-Rao product, we provide efficient access to its statistical leverage scores. When applied to the tensor CP decomposition, our method leads to the first algorithm that runs in sublinear time per-iteration and approximates the output of deterministic alternating least squares algorithms. Empirical evaluations of this approach show significantly speedups over existing randomized and deterministic routines for performing CP decomposition. On a tensor of the size 2.4m by 6.6m by 92k with over 2 billion nonzeros formed by Amazon product reviews, our routine converges in two minutes to the same error as deterministic ALS.
On Multiplicative Integration with Recurrent Neural Networks
Authors: Yuhuai Wu, Saizheng Zhang, Ying Zhang, Yoshua Bengio, Ruslan Salakhutdinov
Published in: Advances in Neural Information Processing Systems 29  (NIPS 2016) (Main Conference Track), 2016

Abstract: We introduce a general simple structural design called “Multiplicative Integration” (MI) to improve recurrent neural networks (RNNs). MI changes the way of how the information flow gets integrated in the computational building block of an RNN, while introducing almost no extra parameters. The new structure can be easily embedded into many popular RNN models, including LSTMs and GRUs. We empirically analyze its learning behaviour and conduct evaluations on several tasks using different RNN models. Our experimental results demonstrate that Multiplicative Integration can provide a substantial performance boost over many of the existing RNN models.
The Generalized Reparameterization Gradient
Authors: Francisco R Ruiz, Michalis Titsias RC AUEB, David Blei
Published in: Advances in Neural Information Processing Systems 29  (NIPS 2016) (Main Conference Track), 2016

Abstract: The reparameterization gradient has become a widely used method to obtain Monte Carlo gradients to optimize the variational objective. However, this technique does not easily apply to commonly used distributions such as beta or gamma without further approximations, and most practical applications of the reparameterization gradient fit Gaussian distributions. In this paper, we introduce the generalized reparameterization gradient, a method that extends the reparameterization gradient to a wider class of variational distributions. Generalized reparameterizations use invertible transformations of the latent variables which lead to transformed distributions that weakly depend on the variational parameters. This results in new Monte Carlo gradients that combine reparameterization gradients and score function gradients. We demonstrate our approach on variational inference for two complex probabilistic models. The generalized reparameterization is effective: even a single sample from the variational distribution is enough to obtain a low-variance gradient.
Semiparametric Differential Graph Models
Authors: Pan Xu, Quanquan Gu
Published in: Advances in Neural Information Processing Systems 29  (NIPS 2016) (Main Conference Track), 2016

Abstract: In many cases of network analysis, it is more attractive to study how a network varies under  different conditions than an individual static network. We propose a novel graphical model, namely Latent Differential Graph Model, where the networks under two different conditions are represented by two semiparametric elliptical distributions respectively, and the variation of these two networks (i.e., differential graph) is characterized by the difference between their latent precision matrices. We propose an estimator for the differential graph based on quasi likelihood maximization with nonconvex regularization. We show that our estimator attains a faster statistical rate in parameter estimation than the state-of-the-art methods, and enjoys oracle property under mild conditions. Thorough experiments on both synthetic and real world data support our theory.
Neural Universal Discrete Denoiser
Authors: Taesup Moon, Seonwoo Min, Byunghan Lee, Sungroh Yoon
Published in: Advances in Neural Information Processing Systems 29  (NIPS 2016) (Main Conference Track), 2016

Abstract: We present a new framework of applying deep neural networks (DNN) to devise a universal discrete denoiser. Unlike other approaches that utilize supervised learning for denoising, we do not require any additional training data. In such setting, while the ground-truth label, i.e., the clean data, is not available, we devise ``pseudo-labels'' and a novel objective function such that DNN can be trained in a same way as supervised learning to become a discrete denoiser. We experimentally show that our resulting algorithm, dubbed as Neural DUDE, significantly outperforms the previous state-of-the-art in several applications with a systematic rule of choosing the hyperparameter, which is an attractive feature in practice.
Testing for Differences in Gaussian Graphical Models:  Applications to Brain Connectivity
Authors: Eugene Belilovsky, Gaël Varoquaux, Matthew B Blaschko
Published in: Advances in Neural Information Processing Systems 29  (NIPS 2016) (Main Conference Track), 2016

Abstract: Functional brain networks are well described and estimated from data with Gaussian Graphical Models (GGMs), e.g.\ using sparse inverse covariance estimators. Comparing functional connectivity of subjects in two populations calls for comparing these estimated GGMs. Our goal is to identify differences in GGMs known to have similar structure. We characterize the uncertainty of differences with confidence intervals obtained using a parametric distribution on parameters of a sparse estimator. Sparse penalties enable statistical guarantees and interpretable models even in high-dimensional and low-sample settings. Characterizing the distributions of sparse models is inherently challenging as the penalties produce a biased estimator. Recent work invokes the sparsity assumptions to effectively remove the bias from a sparse estimator such as the lasso.  These distributions can be used to give confidence intervals on edges in GGMs, and by extension their differences. However, in the case of comparing GGMs, these estimators do not make use of any assumed joint structure among the GGMs. Inspired by priors from brain functional connectivity we derive the distribution of parameter differences under a joint penalty when parameters are known to be sparse in the difference. This leads us to introduce the debiased multi-task fused lasso, whose distribution can be characterized in an efficient manner. We then show how the debiased lasso and multi-task fused lasso can be used to obtain confidence intervals on edge differences in GGMs. We validate the techniques proposed on a set of synthetic examples as well as neuro-imaging dataset created for the study of autism.
Learning to learn by gradient descent by gradient descent
Authors: Marcin Andrychowicz, Misha Denil, Sergio Gómez, Matthew W Hoffman, David Pfau, Tom Schaul, Brendan Shillingford, Nando de Freitas
Published in: Advances in Neural Information Processing Systems 29  (NIPS 2016) (Main Conference Track), 2016

Abstract: The move from hand-designed features to learned features in machine learning has been wildly successful. In spite of this, optimization algorithms are still designed by hand. In this paper we show how the design of an optimization algorithm can be cast as a learning problem, allowing the algorithm to learn to exploit structure in the problems of interest in an automatic way. Our learned algorithms, implemented by LSTMs, outperform generic, hand-designed competitors on the tasks for which they are trained, and also generalize well to new tasks with similar structure. We demonstrate this on a number of tasks, including simple convex problems, training neural networks, and styling images with neural art.
Mixed vine copulas as joint models of spike counts and local field potentials
Authors: Arno Onken, Stefano Panzeri
Published in: Advances in Neural Information Processing Systems 29  (NIPS 2016) (Main Conference Track), 2016

Abstract: Concurrent measurements of neural activity at multiple scales, sometimes performed with multimodal techniques, become increasingly important for studying brain function. However, statistical methods for their concurrent analysis are currently lacking. Here we introduce such techniques in a framework based on vine copulas with mixed margins to construct multivariate stochastic models. These models can describe detailed mixed interactions between discrete variables such as neural spike counts, and continuous variables such as local field potentials. We propose efficient methods for likelihood calculation, inference, sampling and mutual information estimation within this framework. We test our methods on simulated data and demonstrate applicability on mixed data generated by a biologically realistic neural network. Our methods hold the promise to considerably improve statistical analysis of neural data recorded simultaneously at different scales.
Can Active Memory Replace Attention?
Authors: Łukasz Kaiser, Samy Bengio
Published in: Advances in Neural Information Processing Systems 29  (NIPS 2016) (Main Conference Track), 2016

Abstract: Several mechanisms to focus attention of a neural network on selected parts of its input or memory have been used successfully in deep learning models in recent years. Attention has improved image classification, image captioning, speech recognition, generative models, and learning algorithmic tasks, but it had probably the largest impact on neural machine translation.  Recently, similar improvements have been obtained using  alternative mechanisms that do not focus on a single part of a memory but operate on all of it in parallel, in a uniform way. Such mechanism, which we call active memory, improved over attention in algorithmic tasks, image processing, and in generative modelling.  So far, however, active memory has not improved over attention for most natural language processing tasks, in particular for machine translation. We analyze this shortcoming in this paper and propose an extended model of active memory that matches existing attention models on neural machine translation and generalizes better to longer sentences. We investigate this model and explain why previous active memory models did not succeed. Finally, we discuss when active memory brings most benefits and where attention can be a better choice.
Fast Active Set Methods for Online Spike Inference from Calcium Imaging
Authors: Johannes Friedrich, Liam Paninski
Published in: Advances in Neural Information Processing Systems 29  (NIPS 2016) (Main Conference Track), 2016

Abstract: Fluorescent calcium indicators are a popular means for observing the spiking activity of large neuronal populations. Unfortunately, extracting the spike train of each neuron from raw fluorescence calcium imaging data is a nontrivial problem. We present a fast online active set method to solve this sparse nonnegative deconvolution problem. Importantly, the algorithm progresses through each time series sequentially from beginning to end, thus enabling real-time online spike inference during the imaging session. Our algorithm is a generalization of the pool adjacent violators algorithm (PAVA) for isotonic regression and inherits its linear-time computational complexity. We gain remarkable increases in processing speed:  more than one order of magnitude compared to currently employed state of the art convex solvers relying on interior point methods. Our method can exploit warm starts; therefore optimizing model hyperparameters only requires a handful of passes through the data. The algorithm enables real-time simultaneous deconvolution of $O(10^5)$ traces of whole-brain zebrafish imaging data on a laptop.
Human Decision-Making under Limited Time
Authors: Pedro A Ortega, Alan Stocker
Published in: Advances in Neural Information Processing Systems 29  (NIPS 2016) (Main Conference Track), 2016

Abstract: Abstract Subjective expected utility theory assumes that decision-makers possess unlimited computational resources to reason about their choices; however, virtually all decisions in everyday life are made under resource constraints---i.e. decision-makers are bounded in their rationality. Here we experimentally tested the predictions made by a formalization of bounded rationality based on ideas from statistical mechanics and information-theory. We systematically tested human subjects in their ability to solve combinatorial puzzles under different time limitations. We found that our bounded-rational model accounts well for the data. The decomposition of the fitted model parameter into the subjects' expected utility function and resource parameter provide interesting insight into the subjects' information capacity limits. Our results confirm that humans gradually fall back on their learned prior choice patterns when confronted with increasing resource limitations.
Dueling Bandits: Beyond Condorcet Winners to General Tournament Solutions
Authors: Siddartha Y. Ramamohan, Arun Rajkumar, Shivani Agarwal, Shivani Agarwal
Published in: Advances in Neural Information Processing Systems 29  (NIPS 2016) (Main Conference Track), 2016

Abstract: Recent work on deriving $O(\log T)$ anytime regret bounds for stochastic dueling bandit problems has considered mostly Condorcet winners, which do not always exist, and more recently, winners defined by the Copeland set, which do always exist. In this work, we consider a broad notion of winners defined by tournament solutions in social choice theory, which include the Copeland set as a special case but also include several other notions of winners such as the top cycle, uncovered set, and Banks set, and which, like the Copeland set, always exist. We develop a family of UCB-style dueling bandit algorithms for such general tournament solutions, and show $O(\log T)$ anytime regret bounds for them. Experiments confirm the ability of our algorithms to achieve low regret relative to the target winning set of interest.
Visual Question Answering with Question Representation Update (QRU)
Authors: Ruiyu Li, Jiaya Jia
Published in: Advances in Neural Information Processing Systems 29  (NIPS 2016) (Main Conference Track), 2016

Abstract: Our method aims at reasoning over natural language questions and visual images. Given a natural language question about an image, our model updates the question representation iteratively by selecting image regions relevant to the query and learns to give the correct answer. Our model contains several reasoning layers, exploiting complex visual relations in the visual question answering (VQA) task. The proposed network is end-to-end trainable through back-propagation, where its weights are initialized using pre-trained convolutional neural network (CNN) and gated recurrent unit (GRU). Our method is evaluated on challenging datasets of COCO-QA and VQA and yields state-of-the-art performance.
Optimal Learning for Multi-pass Stochastic Gradient Methods
Authors: Junhong Lin, Lorenzo Rosasco
Published in: Advances in Neural Information Processing Systems 29  (NIPS 2016) (Main Conference Track), 2016

Abstract: We analyze the learning  properties of the stochastic gradient method when multiple passes over the data and mini-batches are allowed. In particular, we consider the square loss and show that    for  a universal step-size choice, the number of passes acts as a regularization parameter, and optimal finite sample bounds  can be achieved by early-stopping. Moreover, we show that larger step-sizes are allowed when considering mini-batches. Our analysis is based on  a unifying approach, encompassing both batch and stochastic gradient methods as special cases.
General Tensor Spectral Co-clustering for Higher-Order Data
Authors: Tao Wu, Austin R Benson, David F Gleich
Published in: Advances in Neural Information Processing Systems 29  (NIPS 2016) (Main Conference Track), 2016

Abstract: Spectral clustering and co-clustering are well-known techniques in data analysis, and recent work has extended spectral clustering to square, symmetric tensors and hypermatrices derived from a network.  We develop a new tensor spectral co-clustering method that simultaneously clusters the rows, columns, and slices of a nonnegative three-mode tensor and generalizes to tensors with any number of modes.  The algorithm is based on a new random walk model which we call the super-spacey random surfer.  We show that our method out-performs state-of-the-art co-clustering methods on several synthetic datasets with ground truth clusters and then use the algorithm to analyze several real-world datasets.
Balancing Suspense and Surprise: Timely Decision Making with Endogenous Information Acquisition
Authors: Ahmed M. Alaa, Mihaela van der Schaar
Published in: Advances in Neural Information Processing Systems 29  (NIPS 2016) (Main Conference Track), 2016

Abstract: We develop a Bayesian model for decision-making under time pressure with endogenous information acquisition. In our model, the decision-maker decides when to observe (costly) information by sampling an underlying continuous-time stochastic process (time series) that conveys information about the potential occurrence/non-occurrence of an adverse event which will terminate the decision-making process. In her attempt to predict the occurrence of the adverse event, the decision-maker follows a policy that determines when to acquire information from the time series (continuation), and when to stop acquiring information and make a final prediction (stopping). We show that the optimal policy has a "rendezvous" structure, i.e. a structure in which whenever a new information sample is gathered from the time series, the optimal "date" for acquiring the next sample becomes computable. The optimal interval between two information samples balances a trade-off between the decision maker’s "surprise", i.e. the drift in her posterior belief after observing new information, and "suspense", i.e. the probability that the adverse event occurs in the time interval between two information samples. Moreover, we characterize the continuation and stopping regions in the decision-maker’s state-space, and show that they depend not only on the decision-maker’s beliefs, but also on the "context", i.e. the current realization of the time series.
Generating Long-term Trajectories Using Deep Hierarchical Networks
Authors: Stephan Zheng, Yisong Yue, Jennifer Hobbs
Published in: Advances in Neural Information Processing Systems 29  (NIPS 2016) (Main Conference Track), 2016

Abstract: We study the problem of modeling spatiotemporal trajectories over long time horizons using expert demonstrations. For instance, in sports, agents often choose action sequences with long-term goals in mind, such as achieving a certain strategic position. Conventional policy learning approaches, such as those based on Markov decision processes, generally fail at learning cohesive long-term behavior in such high-dimensional state spaces, and are only effective when fairly myopic decision-making yields the desired behavior. The key difficulty is that conventional models are ``single-scale'' and only learn a single state-action policy. We instead propose a hierarchical policy class that automatically reasons about both long-term and short-term goals, which we instantiate as a hierarchical neural network. We showcase our approach in a case study on learning to imitate demonstrated basketball trajectories, and show that it generates significantly more realistic trajectories compared to non-hierarchical baselines as judged by professional sports analysts.
Natural-Parameter Networks: A Class of Probabilistic Neural Networks
Authors: Hao Wang, Xingjian SHI, Dit-Yan Yeung
Published in: Advances in Neural Information Processing Systems 29  (NIPS 2016) (Main Conference Track), 2016

Abstract: Neural networks (NN) have achieved state-of-the-art performance in various applications. Unfortunately in applications where training data is insufficient, they are often prone to overfitting. One effective way to alleviate this problem is to exploit the Bayesian approach by using Bayesian neural networks (BNN). Another shortcoming of NN is the lack of flexibility to customize different distributions for the weights and neurons according to the data, as is often done in probabilistic graphical models. To address these problems, we propose a class of probabilistic neural networks, dubbed natural-parameter networks (NPN), as a novel and lightweight Bayesian treatment of NN. NPN allows the usage of arbitrary exponential-family distributions to model the weights and neurons. Different from traditional NN and BNN, NPN takes distributions as input and goes through layers of transformation before producing distributions to match the target output distributions. As a Bayesian treatment, efficient backpropagation (BP) is performed to learn the natural parameters for the distributions over both the weights and neurons. The output distributions of each layer, as byproducts, may be used as second-order representations for the associated tasks such as link prediction. Experiments on real-world datasets show that NPN can achieve state-of-the-art performance.
Minimizing Quadratic Functions in Constant Time
Authors: Kohei Hayashi, Yuichi Yoshida
Published in: Advances in Neural Information Processing Systems 29  (NIPS 2016) (Main Conference Track), 2016

Abstract: A sampling-based optimization method for quadratic functions is   proposed. Our method approximately solves the following   $n$-dimensional quadratic minimization problem in constant time,   which is independent of $n$:   $z^*=\min_{\bv \in \bbR^n}\bracket{\bv}{A \bv} +   n\bracket{\bv}{\diag(\bd)\bv} + n\bracket{\bb}{\bv}$,   where $A \in \bbR^{n \times n}$ is a matrix and $\bd,\bb \in \bbR^n$   are vectors. Our theoretical analysis specifies the number of   samples $k(\delta, \epsilon)$ such that the approximated solution   $z$ satisfies $|z - z^*| = O(\epsilon n^2)$ with probability   $1-\delta$. The empirical performance (accuracy and runtime) is   positively confirmed by numerical experiments.
SIMPLIFYING NEURAL NETS BY DISCOVERING FLAT MINIMA
Authors: Sepp Hochreiter, Jürgen Schmidhuber
Published in: Advances in Neural Information Processing Systems 7  (NIPS 1994) (Main Conference Track), 1994

Abstract: We present a new algorithm for finding low complexity networks  with high generalization capability. The algorithm searches for  large connected regions of so-called ''fiat'' minima of the error func(cid:173) tion. In the weight-space environment of a "flat" minimum, the  error remains approximately constant. Using an MDL-based ar(cid:173) gument, flat minima can be shown to correspond to low expected  overfitting. Although our algorithm requires the computation of  second order derivatives, it has backprop's order of complexity.  Experiments with feedforward and recurrent nets are described. In  an application to stock market prediction, the method outperforms  conventional backprop, weight decay, and "optimal brain surgeon" .
A Mixture Model System for Medical and Machine Diagnosis
Authors: Magnus Stensmo, Terrence J. Sejnowski
Published in: Advances in Neural Information Processing Systems 7  (NIPS 1994) (Main Conference Track), 1994

Abstract: Diagnosis of human disease or machine fault is a missing data problem  since many variables are initially unknown. Additional information needs  to be obtained. The j oint probability distribution of the data can be used to  solve this problem. We model this with mixture models whose parameters  are estimated by the EM algorithm.  This gives the benefit that missing  data in the database itself can also be handled correctly.  The request for  new information to refine the diagnosis is performed using the maximum  utility  principle.  Since  the  system  is  based  on  learning  it  is  domain  independent and less labor intensive than expert systems or probabilistic  networks.  An example using a heart disease database is presented.
Learning with Preknowledge: Clustering with Point and Graph Matching Distance Measures
Authors: Steven Gold, Anand Rangarajan, Eric Mjolsness
Published in: Advances in Neural Information Processing Systems 7  (NIPS 1994) (Main Conference Track), 1994

Abstract: Prior constraints are imposed upon a learning problem in the form  of distance measures. Prototypical 2-D point sets and graphs are  learned by clustering with point matching and graph matching dis(cid:173) tance measures. The point matching distance measure is approx.  invariant under affine transformations - translation, rotation, scale  and shear - and permutations. It operates between noisy images  with missing and spurious points. The graph matching distance  measure operates on weighted graphs and is invariant under per(cid:173) mutations. Learning is formulated as an optimization problem .  Large objectives so formulated ('" million variables) are efficiently  minimized using a combination of optimization techniques - alge(cid:173) braic transformations, iterative projective scaling, clocked objec(cid:173) tives, and deterministic annealing.
Learning Local Error Bars for Nonlinear Regression
Authors: David A. Nix, Andreas S. Weigend
Published in: Advances in Neural Information Processing Systems 7  (NIPS 1994) (Main Conference Track), 1994

Abstract: We  present a  new  method for  obtaining  local  error bars for  nonlinear  regression,  i.e.,  estimates of the confidence in predicted values that de(cid:173) pend on the input.  We approach this problem by applying a maximum(cid:173) likelihood framework to an assumed distribution of errors.  We demon(cid:173) strate our method first on computer-generated data with locally varying,  normally distributed target noise.  We then apply it to laser data from the  Santa Fe  Time  Series Competition where the underlying system noise is  known quantization error and the error bars give local estimates of model  misspecification.  In  both  cases,  the method also provides a  weighted(cid:173) regression effect that improves generalization performance. 
1  Learning Local Error Bars Using a Maximum Likelihood 
Framework:  Motivation, Concept, and Mechanics 
Feed-forward artificial neural networks used for nonlinear regression can be interpreted as  predicting the  mean of the  target distribution as  a function  of (conditioned on)  the  input  pattern (e.g., Buntine & Weigend, 1991; Bishop, 1994), typically using one linear output unit  per output variable. If parameterized, this conditional target distribution (CID) may also be 
·http://www.cs.colorado.edu/~andreas/Home.html. 
This  paper  is  available  with  figures  in  colors  as  ftp://ftp.cs.colorado.edu/pub/  Time-Series/MyPapers/nix.weigenCLnips7.ps.Z  . 
490 
David A.  Nix,  Andreas S.  Weigend 
viewed as an error model (Rumelhart et al.,  1995).  Here, we present a simple method that  provides higher-order information about the cm than simply the mean.  Such additional  information could come from  attempting  to  estimate  the  entire cm with  connectionist  methods (e.g., "Mixture Density Networks," Bishop, 1994; "fractional binning, "Srivastava  & Weigend,  1994) or with non-connectionist methods such as  a Monte Carlo on a hidden  Markov model (Fraser & Dimitriadis, 1994). While non-parametric estimates of the shape  of a  C1D require large quantities of data,  our less data-hungry method (Weigend & Nix,  1994) assumes a specific parameterized form of the C1D (e.g., Gaussian) and gives us the  value of the error bar (e.g., the width of the Gaussian) by finding those parameters which  maximize the likelihood that the target data was generated by a particular network model.  In this paper we derive the specific update rules for the Gaussian case.  We  would like to  emphasize, however, that any parameterized unimodal distribution can be used for the em  in the method presented here. 
j------------,  I  I------T-------,  ,  I  A  I  A2,.  )  o y(x)  0  cr IX  /\  i  '.
Reinforcement Learning Methods for Continuous-Time Markov Decision Problems
Authors: Steven J. Bradtke, Michael O. Duff
Published in: Advances in Neural Information Processing Systems 7  (NIPS 1994) (Main Conference Track), 1994

Abstract: Semi-Markov  Decision  Problems  are  continuous  time  generaliza(cid:173) tions  of discrete  time  Markov  Decision  Problems.  A  number  of  reinforcement  learning  algorithms  have  been  developed  recently  for  the  solution  of Markov  Decision  Problems,  based on  the ideas  of asynchronous dynamic programming and stochastic approxima(cid:173) tion.  Among these  are TD(,x), Q-Iearning,  and Real-time Dynamic  Programming.  After  reviewing  semi-Markov  Decision  Problems  and Bellman's optimality equation in  that context,  we  propose al(cid:173) gorithms similar  to those  named above,  adapted to the solution  of  semi-Markov Decision  Problems.  We demonstrate these algorithms  by  applying  them to the  problem of determining  the  optimal con(cid:173) trol for  a  simple  queueing  system.  We  conclude  with  a  discussion  of circumstances  under which these  algorithms may be usefully  ap(cid:173) plied.
Connectionist Speaker Normalization with Generalized Resource Allocating Networks
Authors: Cesare Furlanello, Diego Giuliani, Edmondo Trentin
Published in: Advances in Neural Information Processing Systems 7  (NIPS 1994) (Main Conference Track), 1994

Abstract: The paper presents  a  rapid speaker-normalization technique based  on  neural  network  spectral  mapping.  The  neural  network  is  used  as a front-end  of a  continuous speech  recognition system  (speaker(cid:173) dependent,  HMM-based) to normalize the input acoustic data from  a  new  speaker.  The  spectral  difference  between  speakers  can  be  reduced  using  a  limited amount  of new  acoustic  data (40  phonet(cid:173) ically  rich  sentences).  Recognition  error  of phone  units  from  the  acoustic-phonetic  continuous  speech  corpus  APASCI  is  decreased  with an adaptability ratio of 25%.  We used  local basis networks of  elliptical  Gaussian  kernels,  with  recursive  allocation  of units  and  on-line  optimization of parameters  (GRAN model).  For  this  ap(cid:173) plication,  the  model included  a  linear  term.  The results  compare  favorably  with  multivariate linear  mapping based  on  constrained  orthonormal transformations.
A Novel Reinforcement Model of Birdsong Vocalization Learning
Authors: Kenji Doya, Terrence J. Sejnowski
Published in: Advances in Neural Information Processing Systems 7  (NIPS 1994) (Main Conference Track), 1994

Abstract: Songbirds learn to imitate a tutor song through auditory and motor learn(cid:173) ing.  We  have developed a theoretical framework  for song learning that  accounts  for response properties of neurons that have been  observed in  many  of the nuclei  that are involved in  song learning.  Specifically,  we  suggest that the anteriorforebrain pathway, which is not needed for song  production  in  the  adult  but is  essential  for  song  acquisition,  provides  synaptic perturbations and adaptive evaluations for syllable vocalization  learning.  A computer model based on reinforcement learning was  con(cid:173) structed  that could replicate a real  zebra finch  song with 90% accuracy  based on a spectrographic measure.  The second generation of the bird(cid:173) song model replicated the tutor song with 96% accuracy.
Hierarchical Mixtures of Experts Methodology Applied to Continuous Speech Recognition
Authors: Ying Zhao, Richard M. Schwartz, Jason J. Sroka, John Makhoul
Published in: Advances in Neural Information Processing Systems 7  (NIPS 1994) (Main Conference Track), 1994

Abstract: In  this  paper,  we  incorporate  the  Hierarchical  Mixtures  of Experts  (HME)  method  of probability  estimation,  developed  by  Jordan  [1],  into  an  HMM(cid:173) based  continuous  speech  recognition  system.  The  resulting  system  can  be  thought of as a continuous-density HMM system, but instead of using gaussian  mixtures,  the HME system employs a large set of hierarchically organized but  relatively  small  neural  networks  to  perform the probability density estimation.  The  hierarchical  structure  is  reminiscent  of  a  decision  tree  except  for  two  important differences:  each  "expert" or neural  net  performs a  "soft" decision  rather than a hard decision, and,  unlike ordinary decision trees,  the parameters  of all  the  neural  nets  in  the  HME  are  automatically  trainable  using  the  EM  algorithm.  We  report results on  the ARPA  5,OOO-word  and 4O,OOO-word Wall  Street Journal  corpus using  HME  models.
A Comparison of Discrete-Time Operator Models for Nonlinear System Identification
Authors: Andrew D. Back, Ah Chung Tsoi
Published in: Advances in Neural Information Processing Systems 7  (NIPS 1994) (Main Conference Track), 1994

Abstract: We present a unifying view of discrete-time operator models used in the  context of finite word length linear signal processing.  Comparisons are  made between the recently presented gamma operator model, and the delta  and rho operator models for performing nonlinear system identification  and prediction using neural networks.  A new model based on an adaptive  bilinear  transformation  which  generalizes  all  of the  above  models  is  presented.
Multidimensional Scaling and Data Clustering
Authors: Thomas Hofmann, Joachim Buhmann
Published in: Advances in Neural Information Processing Systems 7  (NIPS 1994) (Main Conference Track), 1994

Abstract: Visualizing and structuring pairwise dissimilarity data are difficult combinatorial op(cid:173) timization problems known as multidimensional scaling or pairwise data clustering.  Algorithms for embedding dissimilarity data set in a Euclidian space, for clustering  these data and for actively selecting data to support the clustering process are discussed  in the maximum entropy framework. Active data selection provides a strategy to discover  structure in a data set efficiently with partially unknown data.
Predicting the Risk of Complications in Coronary Artery Bypass Operations using Neural Networks
Authors: Richard P Lippmann, Linda Kukolich, David Shahian
Published in: Advances in Neural Information Processing Systems 7  (NIPS 1994) (Main Conference Track), 1994

Abstract: Experiments  demonstrated  that  sigmoid  multilayer  perceptron  (MLP)  networks  provide  slightly  better  risk  prediction  than  conventional  logistic  regression  when  used  to  predict  the  risk  of death,  stroke,  and  renal  failure  on  1257  patients  who  underwent  coronary  artery  bypass  operations at the Lahey Clinic. MLP networks with no hidden layer and  networks  with one hidden  layer were trained  using  stochastic  gradient  descent with early stopping. MLP networks and logistic regression used  the  same  input  features  and  were  evaluated  using  bootstrap  sampling  with  50  replications.  ROC  areas  for  predicting  mortality  using  preoperative  input  features  were  70.5%  for  logistic  regression  and  76.0%  for  MLP  networks.  Regularization  provided  by  early  stopping  was  an  important  component  of improved  perfonnance.  A  simplified  approach  to  generating  confidence  intervals  for  MLP risk  predictions  using  an  auxiliary  "confidence  MLP"  was  developed.  The  confidence  MLP  is  trained  to  reproduce  confidence  intervals  that  were  generated  during  training  using  the  outputs  of  50  MLP  networks  trained  with  different bootstrap samples.
Spatial Representations in the Parietal Cortex May Use Basis Functions
Authors: Alexandre Pouget, Terrence J. Sejnowski
Published in: Advances in Neural Information Processing Systems 7  (NIPS 1994) (Main Conference Track), 1994

Abstract: The  parietal  cortex  is  thought  to  represent  the  egocentric  posi(cid:173) tions  of objects  in  particular  coordinate systems.  We  propose  an  alternative  approach  to  spatial  perception  of objects  in  the  pari(cid:173) etal  cortex from  the  perspective  of sensorimotor  transformations.  The responses of single parietal neurons can be modeled as  a gaus(cid:173) sian  function  of retinal  position  multiplied by  a  sigmoid function  of eye  position,  which  form  a  set of basis functions.  We  show  here  how  these  basis functions  can  be  used  to generate  receptive  fields  in  either  retinotopic or head-centered  coordinates by simple linear  transformations.  This raises the possibility that the parietal cortex  does  not  attempt to compute the positions of objects  in  a  partic(cid:173) ular  frame  of  reference  but  instead  computes  a  general  purpose  representation  of the retinal  location and eye  position from  which  any  transformation can  be  synthesized  by  direct  projection.  This  representation  predicts that hemineglect,  a  neurological syndrome  produced  by  parietal lesions,  should  not  be  confined  to egocentric  coordinates,  but should be observed in multiple frames of reference  in  single patients,  a  prediction supported  by several experiments. 
158 
Alexandre Pouget,  Terrence J.  Sejnowski