NeurIPS
Collection
Accepted papers for NeurIPS (Conference on Neural Information Processing Systems), one dataset per year. • 13 items • Updated
paper_id string | title string | paper_url string | authors list | type string | primary_area string | abstract large_string | keywords list | TL;DR large_string | submission_number int64 | arxiv_id string | arxiv_id_source string |
|---|---|---|---|---|---|---|---|---|---|---|---|
018b59ce1fd616d874afad0f44ba338d | Eliciting Categorical Data for Optimal Aggregation | https://proceedings.neurips.cc/paper_files/paper/2016/hash/018b59ce1fd616d874afad0f44ba338d-Abstract.html | [
"Chien-Ju Ho",
"Rafael Frongillo",
"Yiling Chen"
] | null | null | Models for collecting and aggregating categorical data on crowdsourcing platforms typically fall into two broad categories: those assuming agents honest and consistent but with heterogeneous error rates, and those assuming agents strategic and seek to maximize their expected reward. The former often leads to tractable ... | [] | null | 1 | null | null |
01931a6925d3de09e5f87419d9d55055 | A Locally Adaptive Normal Distribution | https://proceedings.neurips.cc/paper_files/paper/2016/hash/01931a6925d3de09e5f87419d9d55055-Abstract.html | [
"Georgios Arvanitidis",
"Lars K. Hansen",
"Søren Hauberg"
] | null | null | The multivariate normal density is a monotonic function of the distance to the mean, and its ellipsoidal shape is due to the underlying Euclidean metric. We suggest to replace this metric with a locally adaptive, smoothly changing (Riemannian) metric that favors regions of high local density. The resulting locally adap... | [] | null | 2 | 1606.02518 | title_snapshot |
01eee509ee2f68dc6014898c309e86bf | Tagger: Deep Unsupervised Perceptual Grouping | https://proceedings.neurips.cc/paper_files/paper/2016/hash/01eee509ee2f68dc6014898c309e86bf-Abstract.html | [
"Klaus Greff",
"Antti Rasmus",
"Mathias Berglund",
"Tele Hao",
"Harri Valpola",
"Jürgen Schmidhuber"
] | null | null | We present a framework for efficient perceptual inference that explicitly reasons about the segmentation of its inputs and features. Rather than being trained for any specific segmentation, our framework learns the grouping process in an unsupervised manner or alongside any supervised task. We enable a neural network t... | [] | null | 3 | 1606.06724 | title_snapshot |
0233f3bb964cf325a30f8b1c2ed2da93 | Online Bayesian Moment Matching for Topic Modeling with Unknown Number of Topics | https://proceedings.neurips.cc/paper_files/paper/2016/hash/0233f3bb964cf325a30f8b1c2ed2da93-Abstract.html | [
"Wei-Shou Hsu",
"Pascal Poupart"
] | null | null | Latent Dirichlet Allocation (LDA) is a very popular model for topic modeling as well as many other problems with latent groups. It is both simple and effective. When the number of topics (or latent groups) is unknown, the Hierarchical Dirichlet Process (HDP) provides an elegant non-parametric extension; however, it is ... | [] | null | 4 | null | null |
0245952ecff55018e2a459517fdb40e3 | Conditional Generative Moment-Matching Networks | https://proceedings.neurips.cc/paper_files/paper/2016/hash/0245952ecff55018e2a459517fdb40e3-Abstract.html | [
"Yong Ren",
"Jun Zhu",
"Jialian Li",
"Yucen Luo"
] | null | null | Maximum mean discrepancy (MMD) has been successfully applied to learn deep generative models for characterizing a joint distribution of variables via kernel mean embedding. In this paper, we present conditional generative moment-matching networks (CGMMN), which learn a conditional distribution given some input variable... | [] | null | 5 | 1606.04218 | title_snapshot |
0266e33d3f546cb5436a10798e657d97 | Collaborative Recurrent Autoencoder: Recommend while Learning to Fill in the Blanks | https://proceedings.neurips.cc/paper_files/paper/2016/hash/0266e33d3f546cb5436a10798e657d97-Abstract.html | [
"Hao Wang",
"Xingjian SHI",
"Dit-Yan Yeung"
] | null | null | Hybrid methods that utilize both content and rating information are commonly used in many recommender systems. However, most of them use either handcrafted features or the bag-of-words representation as a surrogate for the content information but they are neither effective nor natural enough. To address this problem, w... | [] | null | 6 | 1611.00454 | title_snapshot |
03255088ed63354a54e0e5ed957e9008 | Bayesian Intermittent Demand Forecasting for Large Inventories | https://proceedings.neurips.cc/paper_files/paper/2016/hash/03255088ed63354a54e0e5ed957e9008-Abstract.html | [
"Matthias W Seeger",
"David Salinas",
"Valentin Flunkert"
] | null | null | We present a scalable and robust Bayesian method for demand forecasting in the context of a large e-commerce platform, paying special attention to intermittent and bursty target statistics. Inference is approximated by the Newton-Raphson algorithm, reduced to linear-time Kalman smoothing, which allows us to operate on ... | [] | null | 7 | 1709.07638 | title_judge |
03afdbd66e7929b125f8597834fa83a4 | Visual Dynamics: Probabilistic Future Frame Synthesis via Cross Convolutional Networks | https://proceedings.neurips.cc/paper_files/paper/2016/hash/03afdbd66e7929b125f8597834fa83a4-Abstract.html | [
"Tianfan Xue",
"Jiajun Wu",
"Katherine Bouman",
"Bill Freeman"
] | null | null | We study the problem of synthesizing a number of likely future frames from a single input image. In contrast to traditional methods, which have tackled this problem in a deterministic or non-parametric way, we propose a novel approach which models future frames in a probabilistic manner. Our proposed method is therefor... | [] | null | 8 | 1607.02586 | title_snapshot |
03e7ef47cee6fa4ae7567394b99912b7 | Achieving budget-optimality with adaptive schemes in crowdsourcing | https://proceedings.neurips.cc/paper_files/paper/2016/hash/03e7ef47cee6fa4ae7567394b99912b7-Abstract.html | [
"Ashish Khetan",
"Sewoong Oh"
] | null | null | Adaptive schemes, where tasks are assigned based on the data collected thus far, are widely used in practical crowdsourcing systems to efficiently allocate the budget. However, existing theoretical analyses of crowdsourcing systems suggest that the gain of adaptive task assignments is minimal. To bridge this gap, we in... | [] | null | 9 | 1602.03481 | title_snapshot |
03f544613917945245041ea1581df0c2 | Stochastic Gradient Richardson-Romberg Markov Chain Monte Carlo | https://proceedings.neurips.cc/paper_files/paper/2016/hash/03f544613917945245041ea1581df0c2-Abstract.html | [
"Alain Durmus",
"Umut Simsekli",
"Eric Moulines",
"Roland Badeau",
"Gaël RICHARD"
] | null | null | Stochastic Gradient Markov Chain Monte Carlo (SG-MCMC) algorithms have become increasingly popular for Bayesian inference in large-scale applications. Even though these methods have proved useful in several scenarios, their performance is often limited by their bias. In this study, we propose a novel sampling algorithm... | [] | null | 10 | null | null |
04025959b191f8f9de3f924f0940515f | Generating Videos with Scene Dynamics | https://proceedings.neurips.cc/paper_files/paper/2016/hash/04025959b191f8f9de3f924f0940515f-Abstract.html | [
"Carl Vondrick",
"Hamed Pirsiavash",
"Antonio Torralba"
] | null | null | We capitalize on large amounts of unlabeled video in order to learn a model of scene dynamics for both video recognition tasks (e.g. action classification) and video generation tasks (e.g. future prediction). We propose a generative adversarial network for video with a spatio-temporal convolutional architecture that un... | [] | null | 11 | 1609.02612 | title_snapshot |
046ddf96c233a273fd390c3d0b1a9aa4 | Approximate maximum entropy principles via Goemans-Williamson with applications to provable variational methods | https://proceedings.neurips.cc/paper_files/paper/2016/hash/046ddf96c233a273fd390c3d0b1a9aa4-Abstract.html | [
"Andrej Risteski",
"Yuanzhi Li"
] | null | null | The well known maximum-entropy principle due to Jaynes, which states that given mean parameters, the maximum entropy distribution matching them is in an exponential family has been very popular in machine learning due to its “Occam’s razor” interpretation. Unfortunately, calculating the potentials in the maximum entrop... | [] | null | 12 | 1607.03360 | title_snapshot |
04df4d434d481c5bb723be1b6df1ee65 | Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering | https://proceedings.neurips.cc/paper_files/paper/2016/hash/04df4d434d481c5bb723be1b6df1ee65-Abstract.html | [
"Michaël Defferrard",
"Xavier Bresson",
"Pierre Vandergheynst"
] | null | null | In this work, we are interested in generalizing convolutional neural networks (CNNs) from low-dimensional regular grids, where image, video and speech are represented, to high-dimensional irregular domains, such as social networks, brain connectomes or words’ embedding, represented by graphs. We present a formulation o... | [] | null | 13 | 1606.09375 | title_snapshot |
052335232b11864986bb2fa20fa38748 | Fast Distributed Submodular Cover: Public-Private Data Summarization | https://proceedings.neurips.cc/paper_files/paper/2016/hash/052335232b11864986bb2fa20fa38748-Abstract.html | [
"Baharan Mirzasoleiman",
"Morteza Zadimoghaddam",
"Amin Karbasi"
] | null | null | In this paper, we introduce the public-private framework of data summarization motivated by privacy concerns in personalized recommender systems and online social services. Such systems have usually access to massive data generated by a large pool of users. A major fraction of the data is public and is visible to (and ... | [] | null | 14 | null | null |
06138bc5af6023646ede0e1f7c1eac75 | Exponential Family Embeddings | https://proceedings.neurips.cc/paper_files/paper/2016/hash/06138bc5af6023646ede0e1f7c1eac75-Abstract.html | [
"Maja Rudolph",
"Francisco Ruiz",
"Stephan Mandt",
"David Blei"
] | null | null | Word embeddings are a powerful approach to capturing semantic similarity among terms in a vocabulary. In this paper, we develop exponential family embeddings, which extends the idea of word embeddings to other types of high-dimensional data. As examples, we studied several types of data: neural data with real-valued ob... | [] | null | 15 | 1608.00778 | title_snapshot |
062ddb6c727310e76b6200b7c71f63b5 | A Non-parametric Learning Method for Confidently Estimating Patient's Clinical State and Dynamics | https://proceedings.neurips.cc/paper_files/paper/2016/hash/062ddb6c727310e76b6200b7c71f63b5-Abstract.html | [
"William Hoiles",
"Mihaela van der Schaar"
] | null | null | Estimating patient's clinical state from multiple concurrent physiological streams plays an important role in determining if a therapeutic intervention is necessary and for triaging patients in the hospital. In this paper we construct a non-parametric learning algorithm to estimate the clinical state of a patient. The ... | [] | null | 16 | null | null |
06409663226af2f3114485aa4e0a23b4 | Integrated perception with recurrent multi-task neural networks | https://proceedings.neurips.cc/paper_files/paper/2016/hash/06409663226af2f3114485aa4e0a23b4-Abstract.html | [
"Hakan Bilen",
"Andrea Vedaldi"
] | null | null | Modern discriminative predictors have been shown to match natural intelligences in specific perceptual tasks in image classification, object and part detection, boundary extraction, etc. However, a major advantage that natural intelligences still have is that they work well for all perceptual problems together, solving... | [] | null | 17 | 1606.01735 | title_snapshot |
07563a3fe3bbe7e3ba84431ad9d055af | Dialog-based Language Learning | https://proceedings.neurips.cc/paper_files/paper/2016/hash/07563a3fe3bbe7e3ba84431ad9d055af-Abstract.html | [
"Jason E Weston"
] | null | null | A long-term goal of machine learning research is to build an intelligent dialog agent. Most research in natural language understanding has focused on learning from fixed training sets of labeled data, with supervision either at the word level (tagging, parsing tasks) or sentence level (question answering, machine trans... | [] | null | 18 | 1604.06045 | title_snapshot |
076a0c97d09cf1a0ec3e19c7f2529f2b | A Theoretically Grounded Application of Dropout in Recurrent Neural Networks | https://proceedings.neurips.cc/paper_files/paper/2016/hash/076a0c97d09cf1a0ec3e19c7f2529f2b-Abstract.html | [
"Yarin Gal",
"Zoubin Ghahramani"
] | null | null | Recurrent neural networks (RNNs) stand at the forefront of many recent developments in deep learning. Yet a major difficulty with these models is their tendency to overfit, with dropout shown to fail when applied to recurrent layers. Recent results at the intersection of Bayesian modelling and deep learning offer a Bay... | [] | null | 19 | 1512.05287 | title_snapshot |
0771fc6f0f4b1d7d1bb73bbbe14e0e31 | Automatic Neuron Detection in Calcium Imaging Data Using Convolutional Networks | https://proceedings.neurips.cc/paper_files/paper/2016/hash/0771fc6f0f4b1d7d1bb73bbbe14e0e31-Abstract.html | [
"Noah Apthorpe",
"Alexander Riordan",
"Robert Aguilar",
"Jan Homann",
"Yi Gu",
"David Tank",
"H. Sebastian Seung"
] | null | null | Calcium imaging is an important technique for monitoring the activity of thousands of neurons simultaneously. As calcium imaging datasets grow in size, automated detection of individual neurons is becoming important. Here we apply a supervised learning approach to this problem and show that convolutional networks can a... | [] | null | 20 | 1606.07372 | title_snapshot |
07811dc6c422334ce36a09ff5cd6fe71 | Convolutional Neural Fabrics | https://proceedings.neurips.cc/paper_files/paper/2016/hash/07811dc6c422334ce36a09ff5cd6fe71-Abstract.html | [
"Shreyas Saxena",
"Jakob Verbeek"
] | null | null | Despite the success of CNNs, selecting the optimal architecture for a given task remains an open problem. Instead of aiming to select a single optimal architecture, we propose a ``fabric'' that embeds an exponentially large number of architectures. The fabric consists of a 3D trellis that connects response maps at diff... | [] | null | 21 | 1606.02492 | title_snapshot |
07cdfd23373b17c6b337251c22b7ea57 | Budgeted stream-based active learning via adaptive submodular maximization | https://proceedings.neurips.cc/paper_files/paper/2016/hash/07cdfd23373b17c6b337251c22b7ea57-Abstract.html | [
"Kaito Fujii",
"Hisashi Kashima"
] | null | null | Active learning enables us to reduce the annotation cost by adaptively selecting unlabeled instances to be labeled. For pool-based active learning, several effective methods with theoretical guarantees have been developed through maximizing some utility function satisfying adaptive submodularity. In contrast, there hav... | [] | null | 22 | null | null |
08e6bea8e90ba87af3c9554d94db6579 | An equivalence between high dimensional Bayes optimal inference and M-estimation | https://proceedings.neurips.cc/paper_files/paper/2016/hash/08e6bea8e90ba87af3c9554d94db6579-Abstract.html | [
"Madhu Advani",
"Surya Ganguli"
] | null | null | Due to the computational difficulty of performing MMSE (minimum mean squared error) inference, maximum a posteriori (MAP) is often used as a surrogate. However, the accuracy of MAP is suboptimal for high dimensional inference, where the number of model parameters is of the same order as the number of samples. In this w... | [] | null | 23 | 1609.07060 | title_snapshot |
093b60fd0557804c8ba0cbf1453da22f | A Sparse Interactive Model for Matrix Completion with Side Information | https://proceedings.neurips.cc/paper_files/paper/2016/hash/093b60fd0557804c8ba0cbf1453da22f-Abstract.html | [
"Jin Lu",
"Guannan Liang",
"Jiangwen Sun",
"Jinbo Bi"
] | null | null | Matrix completion methods can benefit from side information besides the partially observed matrix. The use of side features describing the row and column entities of a matrix has been shown to reduce the sample complexity for completing the matrix. We propose a novel sparse formulation that explicitly models the intera... | [] | null | 24 | null | null |
0966289037ad9846c5e994be2a91bafa | Bi-Objective Online Matching and Submodular Allocations | https://proceedings.neurips.cc/paper_files/paper/2016/hash/0966289037ad9846c5e994be2a91bafa-Abstract.html | [
"Hossein Esfandiari",
"Nitish Korula",
"Vahab Mirrokni"
] | null | null | Online allocation problems have been widely studied due to their numerous practical applications (particularly to Internet advertising), as well as considerable theoretical interest. The main challenge in such problems is making assignment decisions in the face of uncertainty about future input; effective algorithms ne... | [] | null | 25 | null | null |
0a09c8844ba8f0936c20bd791130d6b6 | Interpretable Distribution Features with Maximum Testing Power | https://proceedings.neurips.cc/paper_files/paper/2016/hash/0a09c8844ba8f0936c20bd791130d6b6-Abstract.html | [
"Wittawat Jitkrittum",
"Zoltán Szabó",
"Kacper P Chwialkowski",
"Arthur Gretton"
] | null | null | Two semimetrics on probability distributions are proposed, given as the sum of differences of expectations of analytic functions evaluated at spatial or frequency locations (i.e, features). The features are chosen so as to maximize the distinguishability of the distributions, by optimizing a lower bound on test power f... | [] | null | 26 | 1605.06796 | title_snapshot |
0a0a0c8aaa00ade50f74a3f0ca981ed7 | Finding significant combinations of features in the presence of categorical covariates | https://proceedings.neurips.cc/paper_files/paper/2016/hash/0a0a0c8aaa00ade50f74a3f0ca981ed7-Abstract.html | [
"Laetitia Papaxanthos",
"Felipe Llinares-López",
"Dean Bodenham",
"Karsten Borgwardt"
] | null | null | In high-dimensional settings, where the number of features p is typically much larger than the number of samples n, methods which can systematically examine arbitrary combinations of features, a huge 2^p-dimensional space, have recently begun to be explored. However, none of the current methods is able to assess the as... | [] | null | 27 | null | null |
0a113ef6b61820daa5611c870ed8d5ee | A Non-convex One-Pass Framework for Generalized Factorization Machine and Rank-One Matrix Sensing | https://proceedings.neurips.cc/paper_files/paper/2016/hash/0a113ef6b61820daa5611c870ed8d5ee-Abstract.html | [
"Ming Lin",
"Jieping Ye"
] | null | null | We develop an efficient alternating framework for learning a generalized version of Factorization Machine (gFM) on steaming data with provable guarantees. When the instances are sampled from $d$ dimensional random Gaussian vectors and the target second order coefficient matrix in gFM is of rank $k$, our algorithm conve... | [] | null | 28 | 1608.05995 | title_snapshot |
0a87257e5308197df43230edf4ad1dae | Avoiding Imposters and Delinquents: Adversarial Crowdsourcing and Peer Prediction | https://proceedings.neurips.cc/paper_files/paper/2016/hash/0a87257e5308197df43230edf4ad1dae-Abstract.html | [
"Jacob Steinhardt",
"Gregory Valiant",
"Moses Charikar"
] | null | null | We consider a crowdsourcing model in which n workers are asked to rate the quality of n items previously generated by other workers. An unknown set of $\alpha n$ workers generate reliable ratings, while the remaining workers may behave arbitrarily and possibly adversarially. The manager of the experiment can also manua... | [] | null | 29 | 1606.05374 | title_snapshot |
0bf727e907c5fc9d5356f11e4c45d613 | Threshold Bandits, With and Without Censored Feedback | https://proceedings.neurips.cc/paper_files/paper/2016/hash/0bf727e907c5fc9d5356f11e4c45d613-Abstract.html | [
"Jacob D. Abernethy",
"Kareem Amin",
"Ruihao Zhu"
] | null | null | We consider the \emph{Threshold Bandit} setting, a variant of the classical multi-armed bandit problem in which the reward on each round depends on a piece of side information known as a \emph{threshold value}. The learner selects one of $K$ actions (arms), this action generates a random sample from a fixed distributio... | [] | null | 30 | null | null |
0c9ebb2ded806d7ffda75cd0b95eb70c | Variational Bayes on Monte Carlo Steroids | https://proceedings.neurips.cc/paper_files/paper/2016/hash/0c9ebb2ded806d7ffda75cd0b95eb70c-Abstract.html | [
"Aditya Grover",
"Stefano Ermon"
] | null | null | Variational approaches are often used to approximate intractable posteriors or normalization constants in hierarchical latent variable models. While often effective in practice, it is known that the approximation error can be arbitrarily large. We propose a new class of bounds on the marginal log-likelihood of directed... | [] | null | 31 | null | null |
0d4f4805c36dc6853edfa4c7e1638b48 | Finite-Dimensional BFRY Priors and Variational Bayesian Inference for Power Law Models | https://proceedings.neurips.cc/paper_files/paper/2016/hash/0d4f4805c36dc6853edfa4c7e1638b48-Abstract.html | [
"Juho Lee",
"Lancelot F James",
"Seungjin Choi"
] | null | null | Bayesian nonparametric methods based on the Dirichlet process (DP), gamma process and beta process, have proven effective in capturing aspects of various datasets arising in machine learning. However, it is now recognized that such processes have their limitations in terms of the ability to capture power law behavior. ... | [] | null | 32 | null | null |
0d73a25092e5c1c9769a9f3255caa65a | Maximal Sparsity with Deep Networks? | https://proceedings.neurips.cc/paper_files/paper/2016/hash/0d73a25092e5c1c9769a9f3255caa65a-Abstract.html | [
"Bo Xin",
"Yizhou Wang",
"Wen Gao",
"David Wipf",
"Baoyuan Wang"
] | null | null | The iterations of many sparse estimation algorithms are comprised of a fixed linear filter cascaded with a thresholding nonlinearity, which collectively resemble a typical neural network layer. Consequently, a lengthy sequence of algorithm iterations can be viewed as a deep network with shared, hand-crafted layer weigh... | [] | null | 33 | 1605.01636 | title_snapshot |
0deb1c54814305ca9ad266f53bc82511 | Single-Image Depth Perception in the Wild | https://proceedings.neurips.cc/paper_files/paper/2016/hash/0deb1c54814305ca9ad266f53bc82511-Abstract.html | [
"Weifeng Chen",
"Zhao Fu",
"Dawei Yang",
"Jia Deng"
] | null | null | This paper studies single-image depth perception in the wild, i.e., recovering depth from a single image taken in unconstrained settings. We introduce a new dataset “Depth in the Wild” consisting of images in the wild annotated with relative depth between pairs of random points. We also propose a new algorithm that lea... | [] | null | 34 | 1604.03901 | title_snapshot |
0e55666a4ad822e0e34299df3591d979 | Single Pass PCA of Matrix Products | https://proceedings.neurips.cc/paper_files/paper/2016/hash/0e55666a4ad822e0e34299df3591d979-Abstract.html | [
"Shanshan Wu",
"Srinadh Bhojanapalli",
"Sujay Sanghavi",
"Alexandros G Dimakis"
] | null | null | In this paper we present a new algorithm for computing a low rank approximation of the product $A^TB$ by taking only a single pass of the two matrices $A$ and $B$. The straightforward way to do this is to (a) first sketch $A$ and $B$ individually, and then (b) find the top components using PCA on the sketch. Our algori... | [] | null | 35 | 1610.06656 | title_snapshot |
0e65972dce68dad4d52d063967f0a705 | Optimal Sparse Linear Encoders and Sparse PCA | https://proceedings.neurips.cc/paper_files/paper/2016/hash/0e65972dce68dad4d52d063967f0a705-Abstract.html | [
"Malik Magdon-Ismail",
"Christos Boutsidis"
] | null | null | Principal components analysis~(PCA) is the optimal linear encoder of data. Sparse linear encoders (e.g., sparse PCA) produce more interpretable features that can promote better generalization. (\rn{1}) Given a level of sparsity, what is the best approximation to PCA? (\rn{2}) Are there efficient algorithms which can ac... | [] | null | 36 | 1502.06626 | title_judge |
0e9fa1f3e9e66792401a6972d477dcc3 | Measuring the reliability of MCMC inference with bidirectional Monte Carlo | https://proceedings.neurips.cc/paper_files/paper/2016/hash/0e9fa1f3e9e66792401a6972d477dcc3-Abstract.html | [
"Roger B Grosse",
"Siddharth Ancha",
"Daniel M. Roy"
] | null | null | Markov chain Monte Carlo (MCMC) is one of the main workhorses of probabilistic inference, but it is notoriously hard to measure the quality of approximate posterior samples. This challenge is particularly salient in black box inference methods, which can hide details and obscure inference failures. In this work, we ext... | [] | null | 37 | 1606.02275 | title_snapshot |
0ed9422357395a0d4879191c66f4faa2 | Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections | https://proceedings.neurips.cc/paper_files/paper/2016/hash/0ed9422357395a0d4879191c66f4faa2-Abstract.html | [
"Xiaojiao Mao",
"Chunhua Shen",
"Yu-Bin Yang"
] | null | null | In this paper, we propose a very deep fully convolutional encoding-decoding framework for image restoration such as denoising and super-resolution. The network is composed of multiple layers of convolution and deconvolution operators, learning end-to-end mappings from corrupted images to the original ones. The convolut... | [] | null | 38 | 1603.09056 | title_snapshot |
0efe32849d230d7f53049ddc4a4b0c60 | On Valid Optimal Assignment Kernels and Applications to Graph Classification | https://proceedings.neurips.cc/paper_files/paper/2016/hash/0efe32849d230d7f53049ddc4a4b0c60-Abstract.html | [
"Nils M. Kriege",
"Pierre-Louis Giscard",
"Richard Wilson"
] | null | null | The success of kernel methods has initiated the design of novel positive semidefinite functions, in particular for structured data. A leading design paradigm for this is the convolution kernel, which decomposes structured objects into their parts and sums over all pairs of parts. Assignment kernels, in contrast, are ob... | [] | null | 39 | 1606.01141 | title_snapshot |
0f96613235062963ccde717b18f97592 | Multi-view Anomaly Detection via Robust Probabilistic Latent Variable Models | https://proceedings.neurips.cc/paper_files/paper/2016/hash/0f96613235062963ccde717b18f97592-Abstract.html | [
"Tomoharu Iwata",
"Makoto Yamada"
] | null | null | We propose probabilistic latent variable models for multi-view anomaly detection, which is the task of finding instances that have inconsistent views given multi-view data. With the proposed model, all views of a non-anomalous instance are assumed to be generated from a single latent vector. On the other hand, an anoma... | [] | null | 40 | 1411.3413 | title_judge |
0fe473396242072e84af286632d3f0ff | Optimal Architectures in a Solvable Model of Deep Networks | https://proceedings.neurips.cc/paper_files/paper/2016/hash/0fe473396242072e84af286632d3f0ff-Abstract.html | [
"Jonathan Kadmon",
"Haim Sompolinsky"
] | null | null | Deep neural networks have received a considerable attention due to the success of their training for real world machine learning applications. They are also of great interest to the understanding of sensory processing in cortical sensory hierarchies. The purpose of this work is to advance our theoretical understanding ... | [] | null | 41 | null | null |
10907813b97e249163587e6246612e21 | Efficient state-space modularization for planning: theory, behavioral and neural signatures | https://proceedings.neurips.cc/paper_files/paper/2016/hash/10907813b97e249163587e6246612e21-Abstract.html | [
"Daniel McNamee",
"Daniel M. Wolpert",
"Mate Lengyel"
] | null | null | Even in state-spaces of modest size, planning is plagued by the “curse of dimensionality”. This problem is particularly acute in human and animal cognition given the limited capacity of working memory, and the time pressures under which planning often occurs in the natural environment. Hierarchically organized modular ... | [] | null | 42 | null | null |
10a5ab2db37feedfdeaab192ead4ac0e | A Communication-Efficient Parallel Algorithm for Decision Tree | https://proceedings.neurips.cc/paper_files/paper/2016/hash/10a5ab2db37feedfdeaab192ead4ac0e-Abstract.html | [
"Qi Meng",
"Guolin Ke",
"Taifeng Wang",
"Wei Chen",
"Qiwei Ye",
"Zhi-Ming Ma",
"Tie-Yan Liu"
] | null | null | Decision tree (and its extensions such as Gradient Boosting Decision Trees and Random Forest) is a widely used machine learning algorithm, due to its practical effectiveness and model interpretability. With the emergence of big data, there is an increasing need to parallelize the training process of decision tree. Howe... | [] | null | 43 | 1611.01276 | title_snapshot |
10c66082c124f8afe3df4886f5e516e0 | Supervised Word Mover's Distance | https://proceedings.neurips.cc/paper_files/paper/2016/hash/10c66082c124f8afe3df4886f5e516e0-Abstract.html | [
"Gao Huang",
"Chuan Guo",
"Matt J Kusner",
"Yu Sun",
"Fei Sha",
"Kilian Q. Weinberger"
] | null | null | Accurately measuring the similarity between text documents lies at the core of many real world applications of machine learning. These include web-search ranking, document recommendation, multi-lingual document matching, and article categorization. Recently, a new document metric, the word mover's distance (WMD), has b... | [] | null | 44 | null | null |
1145a30ff80745b56fb0cecf65305017 | Fast and accurate spike sorting of high-channel count probes with KiloSort | https://proceedings.neurips.cc/paper_files/paper/2016/hash/1145a30ff80745b56fb0cecf65305017-Abstract.html | [
"Marius Pachitariu",
"Nicholas A Steinmetz",
"Shabnam N Kadir",
"Matteo Carandini",
"Kenneth D Harris"
] | null | null | New silicon technology is enabling large-scale electrophysiological recordings in vivo from hundreds to thousands of channels. Interpreting these recordings requires scalable and accurate automated methods for spike sorting, which should minimize the time required for manual curation of the results. Here we introduce K... | [] | null | 45 | null | null |
130f1a8e9e102707f3f91b010f151b0b | Learning brain regions via large-scale online structured sparse dictionary learning | https://proceedings.neurips.cc/paper_files/paper/2016/hash/130f1a8e9e102707f3f91b010f151b0b-Abstract.html | [
"Elvis DOHMATOB",
"Arthur Mensch",
"Gael Varoquaux",
"Bertrand Thirion"
] | null | null | We propose a multivariate online dictionary-learning method for obtaining decompositions of brain images with structured and sparse components (aka atoms). Sparsity is to be understood in the usual sense: the dictionary atoms are constrained to contain mostly zeros. This is imposed via an $\ell_1$-norm constraint. By "... | [] | null | 46 | null | null |
139f0874f2ded2e41b0393c4ac5644f7 | Improving PAC Exploration Using the Median Of Means | https://proceedings.neurips.cc/paper_files/paper/2016/hash/139f0874f2ded2e41b0393c4ac5644f7-Abstract.html | [
"Jason Pazis",
"Ronald E Parr",
"Jonathan P How"
] | null | null | We present the first application of the median of means in a PAC exploration algorithm for MDPs. Using the median of means allows us to significantly reduce the dependence of our bounds on the range of values that the value function can take, while introducing a dependence on the (potentially much smaller) variance of ... | [] | null | 47 | null | null |
13f320e7b5ead1024ac95c3b208610db | Active Nearest-Neighbor Learning in Metric Spaces | https://proceedings.neurips.cc/paper_files/paper/2016/hash/13f320e7b5ead1024ac95c3b208610db-Abstract.html | [
"Aryeh Kontorovich",
"Sivan Sabato",
"Ruth Urner"
] | null | null | We propose a pool-based non-parametric active learning algorithm for general metric spaces, called MArgin Regularized Metric Active Nearest Neighbor (MARMANN), which outputs a nearest-neighbor classifier. We give prediction error guarantees that depend on the noisy-margin properties of the input sample, and are competi... | [] | null | 48 | 1605.06792 | title_snapshot |
140f6969d5213fd0ece03148e62e461e | Learning from Small Sample Sets by Combining Unsupervised Meta-Training with CNNs | https://proceedings.neurips.cc/paper_files/paper/2016/hash/140f6969d5213fd0ece03148e62e461e-Abstract.html | [
"Yu-Xiong Wang",
"Martial Hebert"
] | null | null | This work explores CNNs for the recognition of novel categories from few examples. Inspired by the transferability properties of CNNs, we introduce an additional unsupervised meta-training stage that exposes multiple top layer units to a large amount of unlabeled real-world images. By encouraging these units to learn d... | [] | null | 49 | null | null |
144a3f71a03ab7c4f46f9656608efdb2 | Learning Bayesian networks with ancestral constraints | https://proceedings.neurips.cc/paper_files/paper/2016/hash/144a3f71a03ab7c4f46f9656608efdb2-Abstract.html | [
"Eunice Yuh-Jie Chen",
"Yujia Shen",
"Arthur Choi",
"Adnan Darwiche"
] | null | null | We consider the problem of learning Bayesian networks optimally, when subject to background knowledge in the form of ancestral constraints. Our approach is based on a recently proposed framework for optimal structure learning based on non-decomposable scores, which is general enough to accommodate ancestral constraints... | [] | null | 50 | null | null |
148510031349642de5ca0c544f31b2ef | Exponential expressivity in deep neural networks through transient chaos | https://proceedings.neurips.cc/paper_files/paper/2016/hash/148510031349642de5ca0c544f31b2ef-Abstract.html | [
"Ben Poole",
"Subhaneil Lahiri",
"Maithra Raghu",
"Jascha Sohl-Dickstein",
"Surya Ganguli"
] | null | null | We combine Riemannian geometry with the mean field theory of high dimensional chaos to study the nature of signal propagation in deep neural networks with random weights. Our results reveal a phase transition in the expressivity of random deep networks, with networks in the chaotic phase computing nonlinear functions w... | [] | null | 51 | 1606.05340 | title_snapshot |
14cfdb59b5bda1fc245aadae15b1984a | MetaGrad: Multiple Learning Rates in Online Learning | https://proceedings.neurips.cc/paper_files/paper/2016/hash/14cfdb59b5bda1fc245aadae15b1984a-Abstract.html | [
"Tim van Erven",
"Wouter M. Koolen"
] | null | null | In online convex optimization it is well known that certain subclasses of objective functions are much easier than arbitrary convex functions. We are interested in designing adaptive methods that can automatically get fast rates in as many such subclasses as possible, without any manual tuning. Previous adaptive method... | [] | null | 52 | 1604.08740 | title_snapshot |
14d9e8007c9b41f57891c48e07c23f57 | Learning under uncertainty: a comparison between R-W and Bayesian approach | https://proceedings.neurips.cc/paper_files/paper/2016/hash/14d9e8007c9b41f57891c48e07c23f57-Abstract.html | [
"He Huang",
"Martin Paulus"
] | null | null | Accurately differentiating between what are truly unpredictably random and systematic changes that occur at random can have profound effect on affect and cognition. To examine the underlying computational principles that guide different learning behavior in an uncertain environment, we compared an R-W model and a Bayes... | [] | null | 53 | null | null |
1579779b98ce9edb98dd85606f2c119d | End-to-End Goal-Driven Web Navigation | https://proceedings.neurips.cc/paper_files/paper/2016/hash/1579779b98ce9edb98dd85606f2c119d-Abstract.html | [
"Rodrigo Nogueira",
"Kyunghyun Cho"
] | null | null | We propose a goal-driven web navigation as a benchmark task for evaluating an agent with abilities to understand natural language and plan on partially observed environments. In this challenging task, an agent navigates through a website, which is represented as a graph consisting of web pages as nodes and hyperlinks a... | [] | null | 54 | 1602.02261 | title_snapshot |
158fc2ddd52ec2cf54d3c161f2dd6517 | Higher-Order Factorization Machines | https://proceedings.neurips.cc/paper_files/paper/2016/hash/158fc2ddd52ec2cf54d3c161f2dd6517-Abstract.html | [
"Mathieu Blondel",
"Akinori Fujino",
"Naonori Ueda",
"Masakazu Ishihata"
] | null | null | Factorization machines (FMs) are a supervised learning approach that can use second-order feature combinations even when the data is very high-dimensional. Unfortunately, despite increasing interest in FMs, there exists to date no efficient training algorithm for higher-order FMs (HOFMs). In this paper, we present the ... | [] | null | 55 | 1607.07195 | title_snapshot |
15de21c670ae7c3f6f3f1f37029303c9 | Efficient Second Order Online Learning by Sketching | https://proceedings.neurips.cc/paper_files/paper/2016/hash/15de21c670ae7c3f6f3f1f37029303c9-Abstract.html | [
"Haipeng Luo",
"Alekh Agarwal",
"Nicolò Cesa-Bianchi",
"John Langford"
] | null | null | We propose Sketched Online Newton (SON), an online second order learning algorithm that enjoys substantially improved regret guarantees for ill-conditioned data. SON is an enhanced version of the Online Newton Step, which, via sketching techniques enjoys a running time linear in the dimension and sketch size. We furthe... | [] | null | 56 | 1602.02202 | title_snapshot |
16026d60ff9b54410b3435b403afd226 | Professor Forcing: A New Algorithm for Training Recurrent Networks | https://proceedings.neurips.cc/paper_files/paper/2016/hash/16026d60ff9b54410b3435b403afd226-Abstract.html | [
"Alex M Lamb",
"Anirudh Goyal ALIAS PARTH GOYAL",
"Ying Zhang",
"Saizheng Zhang",
"Aaron C. Courville",
"Yoshua Bengio"
] | null | null | The Teacher Forcing algorithm trains recurrent networks by supplying observed sequence values as inputs during training and using the network’s own one-step-ahead predictions to do multi-step sampling. We introduce the Professor Forcing algorithm, which uses adversarial domain adaptation to encourage the dynamics of th... | [] | null | 57 | 1610.09038 | title_snapshot |
1679091c5a880faf6fb5e6087eb1b2dc | Deep ADMM-Net for Compressive Sensing MRI | https://proceedings.neurips.cc/paper_files/paper/2016/hash/1679091c5a880faf6fb5e6087eb1b2dc-Abstract.html | [
"yan yang",
"Jian Sun",
"Huibin Li",
"Zongben Xu"
] | null | null | Compressive Sensing (CS) is an effective approach for fast Magnetic Resonance Imaging (MRI). It aims at reconstructing MR image from a small number of under-sampled data in k-space, and accelerating the data acquisition in MRI. To improve the current MRI system in reconstruction accuracy and computational speed, in thi... | [] | null | 58 | null | null |
1714726c817af50457d810aae9d27a2e | Adaptive Averaging in Accelerated Descent Dynamics | https://proceedings.neurips.cc/paper_files/paper/2016/hash/1714726c817af50457d810aae9d27a2e-Abstract.html | [
"Walid Krichene",
"Alexandre Bayen",
"Peter L Bartlett"
] | null | null | We study accelerated descent dynamics for constrained convex optimization. This dynamics can be described naturally as a coupling of a dual variable accumulating gradients at a given rate $\eta(t)$, and a primal variable obtained as the weighted average of the mirrored dual trajectory, with weights $w(t)$. Using a Lyap... | [] | null | 59 | null | null |
1728efbda81692282ba642aafd57be3a | Dynamic Mode Decomposition with Reproducing Kernels for Koopman Spectral Analysis | https://proceedings.neurips.cc/paper_files/paper/2016/hash/1728efbda81692282ba642aafd57be3a-Abstract.html | [
"Yoshinobu Kawahara"
] | null | null | A spectral analysis of the Koopman operator, which is an infinite dimensional linear operator on an observable, gives a (modal) description of the global behavior of a nonlinear dynamical system without any explicit prior knowledge of its governing equations. In this paper, we consider a spectral analysis of the Koopma... | [] | null | 60 | null | null |
17ed8abedc255908be746d245e50263a | Total Variation Classes Beyond 1d: Minimax Rates, and the Limitations of Linear Smoothers | https://proceedings.neurips.cc/paper_files/paper/2016/hash/17ed8abedc255908be746d245e50263a-Abstract.html | [
"Veeranjaneyulu Sadhanala",
"Yu-Xiang Wang",
"Ryan J Tibshirani"
] | null | null | We consider the problem of estimating a function defined over $n$ locations on a $d$-dimensional grid (having all side lengths equal to $n^{1/d}$). When the function is constrained to have discrete total variation bounded by $C_n$, we derive the minimax optimal (squared) $\ell_2$ estimation error rate, parametrized by ... | [] | null | 61 | 1605.08400 | title_snapshot |
184260348236f9554fe9375772ff966e | Hardness of Online Sleeping Combinatorial Optimization Problems | https://proceedings.neurips.cc/paper_files/paper/2016/hash/184260348236f9554fe9375772ff966e-Abstract.html | [
"Satyen Kale",
"Chansoo Lee",
"David Pal"
] | null | null | We show that several online combinatorial optimization problems that admit efficient no-regret algorithms become computationally hard in the sleeping setting where a subset of actions becomes unavailable in each round. Specifically, we show that the sleeping versions of these problems are at least as hard as PAC learni... | [] | null | 62 | 1509.03600 | title_snapshot |
185c29dc24325934ee377cfda20e414c | Density Estimation via Discrepancy Based Adaptive Sequential Partition | https://proceedings.neurips.cc/paper_files/paper/2016/hash/185c29dc24325934ee377cfda20e414c-Abstract.html | [
"Dangna Li",
"Kun Yang",
"Wing Hung Wong"
] | null | null | Given $iid$ observations from an unknown continuous distribution defined on some domain $\Omega$, we propose a nonparametric method to learn a piecewise constant function to approximate the underlying probability density function. Our density estimate is a piecewise constant function defined on a binary partition of $\... | [] | null | 63 | 1404.1425 | title_snapshot |
186a157b2992e7daed3677ce8e9fe40f | Quantized Random Projections and Non-Linear Estimation of Cosine Similarity | https://proceedings.neurips.cc/paper_files/paper/2016/hash/186a157b2992e7daed3677ce8e9fe40f-Abstract.html | [
"Ping Li",
"Michael Mitzenmacher",
"Martin Slawski"
] | null | null | Random projections constitute a simple, yet effective technique for dimensionality reduction with applications in learning and search problems. In the present paper, we consider the problem of estimating cosine similarities when the projected data undergo scalar quantization to $b$ bits. We here argue that the maximum ... | [] | null | 64 | null | null |
186fb23a33995d91ce3c2212189178c8 | Algorithms and matching lower bounds for approximately-convex optimization | https://proceedings.neurips.cc/paper_files/paper/2016/hash/186fb23a33995d91ce3c2212189178c8-Abstract.html | [
"Andrej Risteski",
"Yuanzhi Li"
] | null | null | In recent years, a rapidly increasing number of applications in practice requires solving non-convex objectives, like training neural networks, learning graphical models, maximum likelihood estimation etc. Though simple heuristics such as gradient descent with very few modifications tend to work well, theoretical under... | [] | null | 65 | null | null |
18d10dc6e666eab6de9215ae5b3d54df | The Parallel Knowledge Gradient Method for Batch Bayesian Optimization | https://proceedings.neurips.cc/paper_files/paper/2016/hash/18d10dc6e666eab6de9215ae5b3d54df-Abstract.html | [
"Jian Wu",
"Peter Frazier"
] | null | null | In many applications of black-box optimization, one can evaluate multiple points simultaneously, e.g. when evaluating the performances of several different neural network architectures in a parallel computing environment. In this paper, we develop a novel batch Bayesian optimization algorithm --- the parallel knowledge... | [] | null | 66 | 1606.04414 | title_snapshot |
1a0a283bfe7c549dee6c638a05200e32 | Edge-exchangeable graphs and sparsity | https://proceedings.neurips.cc/paper_files/paper/2016/hash/1a0a283bfe7c549dee6c638a05200e32-Abstract.html | [
"Diana Cai",
"Trevor Campbell",
"Tamara Broderick"
] | null | null | Many popular network models rely on the assumption of (vertex) exchangeability, in which the distribution of the graph is invariant to relabelings of the vertices. However, the Aldous-Hoover theorem guarantees that these graphs are dense or empty with probability one, whereas many real-world graphs are sparse. We prese... | [] | null | 67 | 1603.06898 | title_snapshot |
1aa48fc4880bb0c9b8a3bf979d3b917e | Stochastic Variance Reduction Methods for Saddle-Point Problems | https://proceedings.neurips.cc/paper_files/paper/2016/hash/1aa48fc4880bb0c9b8a3bf979d3b917e-Abstract.html | [
"Balamurugan Palaniappan",
"Francis Bach"
] | null | null | We consider convex-concave saddle-point problems where the objective functions may be split in many components, and extend recent stochastic variance reduction methods (such as SVRG or SAGA) to provide the first large-scale linearly convergent algorithms for this class of problems which are common in machine learning. ... | [] | null | 68 | 1605.06398 | title_snapshot |
1abb1e1ea5f481b589da52303b091cbb | A Probabilistic Model of Social Decision Making based on Reward Maximization | https://proceedings.neurips.cc/paper_files/paper/2016/hash/1abb1e1ea5f481b589da52303b091cbb-Abstract.html | [
"Koosha Khalvati",
"Seongmin A. Park",
"Jean-Claude Dreher",
"Rajesh P. Rao"
] | null | null | A fundamental problem in cognitive neuroscience is how humans make decisions, act, and behave in relation to other humans. Here we adopt the hypothesis that when we are in an interactive social setting, our brains perform Bayesian inference of the intentions and cooperativeness of others using probabilistic representat... | [] | null | 69 | null | null |
1ce927f875864094e3906a4a0b5ece68 | Bootstrap Model Aggregation for Distributed Statistical Learning | https://proceedings.neurips.cc/paper_files/paper/2016/hash/1ce927f875864094e3906a4a0b5ece68-Abstract.html | [
"JUN HAN",
"Qiang Liu"
] | null | null | In distributed, or privacy-preserving learning, we are often given a set of probabilistic models estimated from different local repositories, and asked to combine them into a single model that gives efficient statistical estimation. A simple method is to linearly average the parameters of the local models, which, howev... | [] | null | 70 | 1607.01036 | title_snapshot |
1d94108e907bb8311d8802b48fd54b4a | Unsupervised Learning of 3D Structure from Images | https://proceedings.neurips.cc/paper_files/paper/2016/hash/1d94108e907bb8311d8802b48fd54b4a-Abstract.html | [
"Danilo Jimenez Rezende",
"S. M. Ali Eslami",
"Shakir Mohamed",
"Peter Battaglia",
"Max Jaderberg",
"Nicolas Heess"
] | null | null | A key goal of computer vision is to recover the underlying 3D structure that gives rise to 2D observations of the world. If endowed with 3D understanding, agents can abstract away from the complexity of the rendering process to form stable, disentangled representations of scene elements. In this paper we learn strong d... | [] | null | 71 | 1607.00662 | title_snapshot |
1e8c391abfde9abea82d75a2d60278d4 | beta-risk: a New Surrogate Risk for Learning from Weakly Labeled Data | https://proceedings.neurips.cc/paper_files/paper/2016/hash/1e8c391abfde9abea82d75a2d60278d4-Abstract.html | [
"Valentina Zantedeschi",
"Rémi Emonet",
"Marc Sebban"
] | null | null | During the past few years, the machine learning community has paid attention to developping new methods for learning from weakly labeled data. This field covers different settings like semi-supervised learning, learning with label proportions, multi-instance learning, noise-tolerant learning, etc. This paper presents a... | [] | null | 72 | null | null |
1f34004ebcb05f9acda6016d5cc52d5e | Learning Supervised PageRank with Gradient-Based and Gradient-Free Optimization Methods | https://proceedings.neurips.cc/paper_files/paper/2016/hash/1f34004ebcb05f9acda6016d5cc52d5e-Abstract.html | [
"Lev Bogolubsky",
"Pavel Dvurechenskii",
"Alexander Gasnikov",
"Gleb Gusev",
"Yurii Nesterov",
"Andrei M Raigorodskii",
"Aleksey Tikhonov",
"Maksim Zhukovskii"
] | null | null | In this paper, we consider a non-convex loss-minimization problem of learning Supervised PageRank models, which can account for features of nodes and edges. We propose gradient-based and random gradient-free methods to solve this problem. Our algorithms are based on the concept of an inexact oracle and unlike the state... | [] | null | 73 | 1603.00717 | title_snapshot |
1f4477bad7af3616c1f933a02bfabe4e | Globally Optimal Training of Generalized Polynomial Neural Networks with Nonlinear Spectral Methods | https://proceedings.neurips.cc/paper_files/paper/2016/hash/1f4477bad7af3616c1f933a02bfabe4e-Abstract.html | [
"Antoine Gautier",
"Quynh N Nguyen",
"Matthias Hein"
] | null | null | The optimization problem behind neural networks is highly non-convex. Training with stochastic gradient descent and variants requires careful parameter tuning and provides no guarantee to achieve the global optimum. In contrast we show under quite weak assumptions on the data that a particular class of feedforward neur... | [] | null | 74 | 1610.09300 | title_snapshot |
1f50893f80d6830d62765ffad7721742 | Optimal Black-Box Reductions Between Optimization Objectives | https://proceedings.neurips.cc/paper_files/paper/2016/hash/1f50893f80d6830d62765ffad7721742-Abstract.html | [
"Zeyuan Allen-Zhu",
"Elad Hazan"
] | null | null | The diverse world of machine learning applications has given rise to a plethora of algorithms and optimization methods, finely tuned to the specific regression or classification task at hand. We reduce the complexity of algorithm design for machine learning by reductions: we develop reductions that take a method develo... | [] | null | 75 | 1603.05642 | title_snapshot |
208e43f0e45c4c78cafadb83d2888cb6 | Sequential Neural Models with Stochastic Layers | https://proceedings.neurips.cc/paper_files/paper/2016/hash/208e43f0e45c4c78cafadb83d2888cb6-Abstract.html | [
"Marco Fraccaro",
"Søren Kaae Sønderby",
"Ulrich Paquet",
"Ole Winther"
] | null | null | How can we efficiently propagate uncertainty in a latent state representation with recurrent neural networks? This paper introduces stochastic recurrent neural networks which glue a deterministic recurrent neural network and a state space model together to form a stochastic and sequential neural generative model. The c... | [] | null | 76 | 1605.07571 | title_snapshot |
20c9f5700da1088260df60fcc5df2b53 | Iterative Refinement of the Approximate Posterior for Directed Belief Networks | https://proceedings.neurips.cc/paper_files/paper/2016/hash/20c9f5700da1088260df60fcc5df2b53-Abstract.html | [
"Devon Hjelm",
"Ruslan Salakhutdinov",
"Kyunghyun Cho",
"Nebojsa Jojic",
"Vince Calhoun",
"Junyoung Chung"
] | null | null | Variational methods that rely on a recognition network to approximate the posterior of directed graphical models offer better inference and learning than previous methods. Recent advances that exploit the capacity and flexibility in this approach have expanded what kinds of models can be trained. However, as a proposal... | [] | null | 77 | 1511.06382 | title_snapshot |
20d135f0f28185b84a4cf7aa51f29500 | Stochastic Multiple Choice Learning for Training Diverse Deep Ensembles | https://proceedings.neurips.cc/paper_files/paper/2016/hash/20d135f0f28185b84a4cf7aa51f29500-Abstract.html | [
"Stefan Lee",
"Senthil Purushwalkam Shiva Prakash",
"Michael Cogswell",
"Viresh Ranjan",
"David Crandall",
"Dhruv Batra"
] | null | null | Many practical perception systems exist within larger processes which often include interactions with users or additional components that are capable of evaluating the quality of predicted solutions. In these contexts, it is beneficial to provide these oracle mechanisms with multiple highly likely hypotheses rather tha... | [] | null | 78 | 1606.07839 | title_snapshot |
228499b55310264a8ea0e27b6e7c6ab6 | Learning shape correspondence with anisotropic convolutional neural networks | https://proceedings.neurips.cc/paper_files/paper/2016/hash/228499b55310264a8ea0e27b6e7c6ab6-Abstract.html | [
"Davide Boscaini",
"Jonathan Masci",
"Emanuele Rodolà",
"Michael Bronstein"
] | null | null | Convolutional neural networks have achieved extraordinary results in many computer vision and pattern recognition applications; however, their adoption in the computer graphics and geometry processing communities is limited due to the non-Euclidean structure of their data. In this paper, we propose Anisotropic Convolut... | [] | null | 79 | 1605.06437 | title_snapshot |
22ac3c5a5bf0b520d281c122d1490650 | Learning Tree Structured Potential Games | https://proceedings.neurips.cc/paper_files/paper/2016/hash/22ac3c5a5bf0b520d281c122d1490650-Abstract.html | [
"Vikas Garg",
"Tommi Jaakkola"
] | null | null | Many real phenomena, including behaviors, involve strategic interactions that can be learned from data. We focus on learning tree structured potential games where equilibria are represented by local maxima of an underlying potential function. We cast the learning problem within a max margin setting and show that the pr... | [] | null | 80 | null | null |
231141b34c82aa95e48810a9d1b33a79 | RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism | https://proceedings.neurips.cc/paper_files/paper/2016/hash/231141b34c82aa95e48810a9d1b33a79-Abstract.html | [
"Edward Choi",
"Mohammad Taha Bahadori",
"Jimeng Sun",
"Joshua Kulas",
"Andy Schuetz",
"Walter Stewart"
] | null | null | Accuracy and interpretability are two dominant features of successful predictive models. Typically, a choice must be made in favor of complex black box models such as recurrent neural networks (RNN) for accuracy versus less accurate but more interpretable traditional models such as logistic regression. This tradeoff po... | [] | null | 81 | 1608.05745 | title_snapshot |
2387337ba1e0b0249ba90f55b2ba2521 | PAC Reinforcement Learning with Rich Observations | https://proceedings.neurips.cc/paper_files/paper/2016/hash/2387337ba1e0b0249ba90f55b2ba2521-Abstract.html | [
"Akshay Krishnamurthy",
"Alekh Agarwal",
"John Langford"
] | null | null | We propose and study a new model for reinforcement learning with rich observations, generalizing contextual bandits to sequential decision making. These models require an agent to take actions based on observations (features) with the goal of achieving long-term performance competitive with a large set of policies. To ... | [] | null | 82 | 1602.02722 | title_snapshot |
23ad3e314e2a2b43b4c720507cec0723 | Generative Shape Models: Joint Text Recognition and Segmentation with Very Little Training Data | https://proceedings.neurips.cc/paper_files/paper/2016/hash/23ad3e314e2a2b43b4c720507cec0723-Abstract.html | [
"Xinghua Lou",
"Ken Kansky",
"Wolfgang Lehrach",
"CC Laan",
"Bhaskara Marthi",
"D. Phoenix",
"Dileep George"
] | null | null | We demonstrate that a generative model for object shapes can achieve state of the art results on challenging scene text recognition tasks, and with orders of magnitude fewer training images than required for competing discriminative methods. In addition to transcribing text from challenging images, our method performs ... | [] | null | 83 | 1611.02788 | title_snapshot |
23c97e9cb93576e45d2feaf00d0e8502 | Probabilistic Linear Multistep Methods | https://proceedings.neurips.cc/paper_files/paper/2016/hash/23c97e9cb93576e45d2feaf00d0e8502-Abstract.html | [
"Onur Teymur",
"Kostas Zygalakis",
"Ben Calderhead"
] | null | null | We present a derivation and theoretical investigation of the Adams-Bashforth and Adams-Moulton family of linear multistep methods for solving ordinary differential equations, starting from a Gaussian process (GP) framework. In the limit, this formulation coincides with the classical deterministic methods, which have be... | [] | null | 84 | 1610.08417 | title_snapshot |
2421fcb1263b9530df88f7f002e78ea5 | Computational and Statistical Tradeoffs in Learning to Rank | https://proceedings.neurips.cc/paper_files/paper/2016/hash/2421fcb1263b9530df88f7f002e78ea5-Abstract.html | [
"Ashish Khetan",
"Sewoong Oh"
] | null | null | For massive and heterogeneous modern data sets, it is of fundamental interest to provide guarantees on the accuracy of estimation when computational resources are limited. In the application of learning to rank, we provide a hierarchy of rank-breaking mechanisms ordered by the complexity in thus generated sketch of the... | [] | null | 85 | 1608.06203 | title_snapshot |
2451041557a22145b3701b0184109cab | Split LBI: An Iterative Regularization Path with Structural Sparsity | https://proceedings.neurips.cc/paper_files/paper/2016/hash/2451041557a22145b3701b0184109cab-Abstract.html | [
"Chendi Huang",
"Xinwei Sun",
"Jiechao Xiong",
"Yuan Yao"
] | null | null | An iterative regularization path with structural sparsity is proposed in this paper based on variable splitting and the Linearized Bregman Iteration, hence called \emph{Split LBI}. Despite its simplicity, Split LBI outperforms the popular generalized Lasso in both theory and experiments. A theory of path consistency is... | [] | null | 86 | null | null |
2596a54cdbb555cfd09cd5d991da0f55 | Incremental Variational Sparse Gaussian Process Regression | https://proceedings.neurips.cc/paper_files/paper/2016/hash/2596a54cdbb555cfd09cd5d991da0f55-Abstract.html | [
"Ching-An Cheng",
"Byron Boots"
] | null | null | Recent work on scaling up Gaussian process regression (GPR) to large datasets has primarily focused on sparse GPR, which leverages a small set of basis functions to approximate the full Gaussian process during inference. However, the majority of these approaches are batch methods that operate on the entire training dat... | [] | null | 87 | null | null |
25ddc0f8c9d3e22e03d3076f98d83cb2 | Sublinear Time Orthogonal Tensor Decomposition | https://proceedings.neurips.cc/paper_files/paper/2016/hash/25ddc0f8c9d3e22e03d3076f98d83cb2-Abstract.html | [
"Zhao Song",
"David Woodruff",
"Huan Zhang"
] | null | null | A recent work (Wang et. al., NIPS 2015) gives the fastest known algorithms for orthogonal tensor decomposition with provable guarantees. Their algorithm is based on computing sketches of the input tensor, which requires reading the entire input. We show in a number of cases one can achieve the same theoretical guarante... | [] | null | 88 | null | null |
26f5bd4aa64fdadf96152ca6e6408068 | Mapping Estimation for Discrete Optimal Transport | https://proceedings.neurips.cc/paper_files/paper/2016/hash/26f5bd4aa64fdadf96152ca6e6408068-Abstract.html | [
"Michaël Perrot",
"Nicolas Courty",
"Rémi Flamary",
"Amaury Habrard"
] | null | null | We are interested in the computation of the transport map of an Optimal Transport problem. Most of the computational approaches of Optimal Transport use the Kantorovich relaxation of the problem to learn a probabilistic coupling $\mgamma$ but do not address the problem of learning the underlying transport map $\funcT$ ... | [] | null | 89 | null | null |
277a78fc05c8864a170e9a56ceeabc4c | Greedy Feature Construction | https://proceedings.neurips.cc/paper_files/paper/2016/hash/277a78fc05c8864a170e9a56ceeabc4c-Abstract.html | [
"Dino Oglic",
"Thomas Gärtner"
] | null | null | We present an effective method for supervised feature construction. The main goal of the approach is to construct a feature representation for which a set of linear hypotheses is of sufficient capacity -- large enough to contain a satisfactory solution to the considered problem and small enough to allow good generaliza... | [] | null | 90 | null | null |
2823f4797102ce1a1aec05359cc16dd9 | Dynamic Network Surgery for Efficient DNNs | https://proceedings.neurips.cc/paper_files/paper/2016/hash/2823f4797102ce1a1aec05359cc16dd9-Abstract.html | [
"Yiwen Guo",
"Anbang Yao",
"Yurong Chen"
] | null | null | Deep learning has become a ubiquitous technology to improve machine intelligence. However, most of the existing deep models are structurally very complex, making them difficult to be deployed on the mobile platforms with limited computational power. In this paper, we propose a novel network compression method called dy... | [] | null | 91 | 1608.04493 | title_snapshot |
286674e3082feb7e5afb92777e48821f | Graph Clustering: Block-models and model free results | https://proceedings.neurips.cc/paper_files/paper/2016/hash/286674e3082feb7e5afb92777e48821f-Abstract.html | [
"Yali Wan",
"Marina Meila"
] | null | null | Clustering graphs under the Stochastic Block Model (SBM) and extensions are well studied. Guarantees of correctness exist under the assumption that the data is sampled from a model. In this paper, we propose a framework, in which we obtain "correctness" guarantees without assuming the data comes from a model. The guara... | [] | null | 92 | null | null |
289dff07669d7a23de0ef88d2f7129e7 | CMA-ES with Optimal Covariance Update and Storage Complexity | https://proceedings.neurips.cc/paper_files/paper/2016/hash/289dff07669d7a23de0ef88d2f7129e7-Abstract.html | [
"Oswin Krause",
"Dídac Rodríguez Arbonès",
"Christian Igel"
] | null | null | The covariance matrix adaptation evolution strategy (CMA-ES) is arguably one of the most powerful real-valued derivative-free optimization algorithms, finding many applications in machine learning. The CMA-ES is a Monte Carlo method, sampling from a sequence of multi-variate Gaussian distributions. Given the function v... | [] | null | 93 | null | null |
28b60a16b55fd531047c0c958ce14b95 | Feature selection in functional data classification with recursive maxima hunting | https://proceedings.neurips.cc/paper_files/paper/2016/hash/28b60a16b55fd531047c0c958ce14b95-Abstract.html | [
"José L. Torrecilla",
"Alberto Suárez"
] | null | null | Dimensionality reduction is one of the key issues in the design of effective machine learning methods for automatic induction. In this work, we introduce recursive maxima hunting (RMH) for variable selection in classification problems with functional data. In this context, variable selection techniques are especially a... | [] | null | 94 | 1806.02922 | title_snapshot |
28e209b61a52482a0ae1cb9f5959c792 | Cyclades: Conflict-free Asynchronous Machine Learning | https://proceedings.neurips.cc/paper_files/paper/2016/hash/28e209b61a52482a0ae1cb9f5959c792-Abstract.html | [
"Xinghao Pan",
"Maximilian Lam",
"Stephen Tu",
"Dimitris Papailiopoulos",
"Ce Zhang",
"Michael I Jordan",
"Kannan Ramchandran",
"Christopher Ré"
] | null | null | We present Cyclades, a general framework for parallelizing stochastic optimization algorithms in a shared memory setting. Cyclades is asynchronous during model updates, and requires no memory locking mechanisms, similar to Hogwild!-type algorithms. Unlike Hogwild!, Cyclades introduces no conflicts during parallel execu... | [] | null | 95 | 1605.09721 | title_snapshot |
291597a100aadd814d197af4f4bab3a7 | Proximal Stochastic Methods for Nonsmooth Nonconvex Finite-Sum Optimization | https://proceedings.neurips.cc/paper_files/paper/2016/hash/291597a100aadd814d197af4f4bab3a7-Abstract.html | [
"Sashank J. Reddi",
"Suvrit Sra",
"Barnabas Poczos",
"Alexander J Smola"
] | null | null | We analyze stochastic algorithms for optimizing nonconvex, nonsmooth finite-sum problems, where the nonsmooth part is convex. Surprisingly, unlike the smooth case, our knowledge of this fundamental problem is very limited. For example, it is not known whether the proximal stochastic gradient method with constant miniba... | [] | null | 96 | null | null |
296472c9542ad4d4788d543508116cbc | Spectral Learning of Dynamic Systems from Nonequilibrium Data | https://proceedings.neurips.cc/paper_files/paper/2016/hash/296472c9542ad4d4788d543508116cbc-Abstract.html | [
"Hao Wu",
"Frank Noe"
] | null | null | Observable operator models (OOMs) and related models are one of the most important and powerful tools for modeling and analyzing stochastic systems. They exactly describe dynamics of finite-rank systems and can be efficiently and consistently estimated through spectral learning under the assumption of identically distr... | [] | null | 97 | 1609.00932 | title_snapshot |
299570476c6f0309545110c592b6a63b | Dimension-Free Iteration Complexity of Finite Sum Optimization Problems | https://proceedings.neurips.cc/paper_files/paper/2016/hash/299570476c6f0309545110c592b6a63b-Abstract.html | [
"Yossi Arjevani",
"Ohad Shamir"
] | null | null | Many canonical machine learning problems boil down to a convex optimization problem with a finite sum structure. However, whereas much progress has been made in developing faster algorithms for this setting, the inherent limitations of these problems are not satisfactorily addressed by existing lower bounds. Indeed, cu... | [] | null | 98 | 1606.09333 | title_snapshot |
299a23a2291e2126b91d54f3601ec162 | Hierarchical Object Representation for Open-Ended Object Category Learning and Recognition | https://proceedings.neurips.cc/paper_files/paper/2016/hash/299a23a2291e2126b91d54f3601ec162-Abstract.html | [
"Seyed Hamidreza Kasaei",
"Ana Maria Tomé",
"Luís Seabra Lopes"
] | null | null | Most robots lack the ability to learn new objects from past experiences. To migrate a robot to a new environment one must often completely re-generate the knowledge- base that it is running with. Since in open-ended domains the set of categories to be learned is not predefined, it is not feasible to assume that one can... | [] | null | 99 | null | null |
299fb2142d7de959380f91c01c3a293c | Active Learning with Oracle Epiphany | https://proceedings.neurips.cc/paper_files/paper/2016/hash/299fb2142d7de959380f91c01c3a293c-Abstract.html | [
"Tzu-Kuo Huang",
"Lihong Li",
"Ara Vartanian",
"Saleema Amershi",
"Xiaojin Zhu"
] | null | null | We present a theoretical analysis of active learning with more realistic interactions with human oracles. Previous empirical studies have shown oracles abstaining on difficult queries until accumulating enough information to make label decisions. We formalize this phenomenon with an “oracle epiphany model” and analyze ... | [] | null | 100 | null | null |
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