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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
0060ef47b12160b9198302ebdb144dcf | Real Time Image Saliency for Black Box Classifiers | https://proceedings.neurips.cc/paper_files/paper/2017/hash/0060ef47b12160b9198302ebdb144dcf-Abstract.html | [
"Piotr Dabkowski",
"Yarin Gal"
] | null | null | In this work we develop a fast saliency detection method that can be applied to any differentiable image classifier. We train a masking model to manipulate the scores of the classifier by masking salient parts of the input image. Our model generalises well to unseen images and requires a single forward pass to perform ... | [] | null | 1 | 1705.07857 | title_snapshot |
0070d23b06b1486a538c0eaa45dd167a | Joint distribution optimal transportation for domain adaptation | https://proceedings.neurips.cc/paper_files/paper/2017/hash/0070d23b06b1486a538c0eaa45dd167a-Abstract.html | [
"Nicolas Courty",
"Rémi Flamary",
"Amaury Habrard",
"Alain Rakotomamonjy"
] | null | null | This paper deals with the unsupervised domain adaptation problem, where one wants to estimate a prediction function $f$ in a given target domain without any labeled sample by exploiting the knowledge available from a source domain where labels are known. Our work makes the following assumption: there exists a non-linea... | [] | null | 2 | 1705.08848 | title_snapshot |
00a03ec6533ca7f5c644d198d815329c | Learning A Structured Optimal Bipartite Graph for Co-Clustering | https://proceedings.neurips.cc/paper_files/paper/2017/hash/00a03ec6533ca7f5c644d198d815329c-Abstract.html | [
"Feiping Nie",
"Xiaoqian Wang",
"Cheng Deng",
"Heng Huang"
] | null | null | Co-clustering methods have been widely applied to document clustering and gene expression analysis. These methods make use of the duality between features and samples such that the co-occurring structure of sample and feature clusters can be extracted. In graph based co-clustering methods, a bipartite graph is construc... | [] | null | 3 | null | null |
013a006f03dbc5392effeb8f18fda755 | Learning to Inpaint for Image Compression | https://proceedings.neurips.cc/paper_files/paper/2017/hash/013a006f03dbc5392effeb8f18fda755-Abstract.html | [
"Mohammad Haris Baig",
"Vladlen Koltun",
"Lorenzo Torresani"
] | null | null | We study the design of deep architectures for lossy image compression. We present two architectural recipes in the context of multi-stage progressive encoders and empirically demonstrate their importance on compression performance. Specifically, we show that: 1) predicting the original image data from residuals in a mu... | [] | null | 4 | 1709.08855 | title_snapshot |
01894d6f048493d2cacde3c579c315a3 | Inverse Filtering for Hidden Markov Models | https://proceedings.neurips.cc/paper_files/paper/2017/hash/01894d6f048493d2cacde3c579c315a3-Abstract.html | [
"Robert Mattila",
"Cristian Rojas",
"Vikram Krishnamurthy",
"Bo Wahlberg"
] | null | null | This paper considers a number of related inverse filtering problems for hidden Markov models (HMMs). In particular, given a sequence of state posteriors and the system dynamics; i) estimate the corresponding sequence of observations, ii) estimate the observation likelihoods, and iii) jointly estimate the observation li... | [] | null | 5 | null | null |
018dd1e07a2de4a08e6612341bf2323e | On clustering network-valued data | https://proceedings.neurips.cc/paper_files/paper/2017/hash/018dd1e07a2de4a08e6612341bf2323e-Abstract.html | [
"Soumendu Sundar Mukherjee",
"Purnamrita Sarkar",
"Lizhen Lin"
] | null | null | Community detection, which focuses on clustering nodes or detecting communities in (mostly) a single network, is a problem of considerable practical interest and has received a great deal of attention in the research community. While being able to cluster within a network is important, there are emerging needs to be ab... | [] | null | 6 | 1606.02401 | title_snapshot |
019d385eb67632a7e958e23f24bd07d7 | Langevin Dynamics with Continuous Tempering for Training Deep Neural Networks | https://proceedings.neurips.cc/paper_files/paper/2017/hash/019d385eb67632a7e958e23f24bd07d7-Abstract.html | [
"Nanyang Ye",
"Zhanxing Zhu",
"Rafal Mantiuk"
] | null | null | Minimizing non-convex and high-dimensional objective functions is challenging, especially when training modern deep neural networks. In this paper, a novel approach is proposed which divides the training process into two consecutive phases to obtain better generalization performance: Bayesian sampling and stochastic op... | [] | null | 7 | 1703.04379 | title_snapshot |
01a0683665f38d8e5e567b3b15ca98bf | Beyond Worst-case: A Probabilistic Analysis of Affine Policies in Dynamic Optimization | https://proceedings.neurips.cc/paper_files/paper/2017/hash/01a0683665f38d8e5e567b3b15ca98bf-Abstract.html | [
"Omar El Housni",
"Vineet Goyal"
] | null | null | Affine policies (or control) are widely used as a solution approach in dynamic optimization where computing an optimal adjustable solution is usually intractable. While the worst case performance of affine policies can be significantly bad, the empirical performance is observed to be near-optimal for a large class of p... | [] | null | 8 | 1706.05737 | title_snapshot |
01e9565cecc4e989123f9620c1d09c09 | Few-Shot Learning Through an Information Retrieval Lens | https://proceedings.neurips.cc/paper_files/paper/2017/hash/01e9565cecc4e989123f9620c1d09c09-Abstract.html | [
"Eleni Triantafillou",
"Richard Zemel",
"Raquel Urtasun"
] | null | null | Few-shot learning refers to understanding new concepts from only a few examples. We propose an information retrieval-inspired approach for this problem that is motivated by the increased importance of maximally leveraging all the available information in this low-data regime. We define a training objective that aims to... | [] | null | 9 | 1707.02610 | title_snapshot |
024d7f84fff11dd7e8d9c510137a2381 | Accelerated consensus via Min-Sum Splitting | https://proceedings.neurips.cc/paper_files/paper/2017/hash/024d7f84fff11dd7e8d9c510137a2381-Abstract.html | [
"Patrick Rebeschini",
"Sekhar C Tatikonda"
] | null | null | We apply the Min-Sum message-passing protocol to solve the consensus problem in distributed optimization. We show that while the ordinary Min-Sum algorithm does not converge, a modified version of it known as Splitting yields convergence to the problem solution. We prove that a proper choice of the tuning parameters al... | [] | null | 10 | 1706.03807 | title_snapshot |
028ee724157b05d04e7bdcf237d12e60 | Saliency-based Sequential Image Attention with Multiset Prediction | https://proceedings.neurips.cc/paper_files/paper/2017/hash/028ee724157b05d04e7bdcf237d12e60-Abstract.html | [
"Sean Welleck",
"Jialin Mao",
"Kyunghyun Cho",
"Zheng Zhang"
] | null | null | Humans process visual scenes selectively and sequentially using attention. Central to models of human visual attention is the saliency map. We propose a hierarchical visual architecture that operates on a saliency map and uses a novel attention mechanism to sequentially focus on salient regions and take additional glim... | [] | null | 11 | 1711.05165 | title_snapshot |
02a32ad2669e6fe298e607fe7cc0e1a0 | Adaptive Bayesian Sampling with Monte Carlo EM | https://proceedings.neurips.cc/paper_files/paper/2017/hash/02a32ad2669e6fe298e607fe7cc0e1a0-Abstract.html | [
"Anirban Roychowdhury",
"Srinivasan Parthasarathy"
] | null | null | We present a novel technique for learning the mass matrices in samplers obtained from discretized dynamics that preserve some energy function. Existing adaptive samplers use Riemannian preconditioning techniques, where the mass matrices are functions of the parameters being sampled. This leads to significant complexiti... | [] | null | 12 | 1711.02159 | title_snapshot |
02b1be0d48924c327124732726097157 | Scalable Levy Process Priors for Spectral Kernel Learning | https://proceedings.neurips.cc/paper_files/paper/2017/hash/02b1be0d48924c327124732726097157-Abstract.html | [
"Phillip A Jang",
"Andrew Loeb",
"Matthew Davidow",
"Andrew G Wilson"
] | null | null | Gaussian processes are rich distributions over functions, with generalization properties determined by a kernel function. When used for long-range extrapolation, predictions are particularly sensitive to the choice of kernel parameters. It is therefore critical to account for kernel uncertainty in our predictive distri... | [] | null | 13 | 1802.00530 | title_snapshot |
02f039058bd48307e6f653a2005c9dd2 | Model-Powered Conditional Independence Test | https://proceedings.neurips.cc/paper_files/paper/2017/hash/02f039058bd48307e6f653a2005c9dd2-Abstract.html | [
"Rajat Sen",
"Ananda Theertha Suresh",
"Karthikeyan Shanmugam",
"Alexandros G Dimakis",
"Sanjay Shakkottai"
] | null | null | We consider the problem of non-parametric Conditional Independence testing (CI testing) for continuous random variables. Given i.i.d samples from the joint distribution $f(x,y,z)$ of continuous random vectors $X,Y$ and $Z,$ we determine whether $X \independent Y \vert Z$. We approach this by converting the conditional ... | [] | null | 14 | 1709.06138 | title_snapshot |
03e0704b5690a2dee1861dc3ad3316c9 | Learning Multiple Tasks with Multilinear Relationship Networks | https://proceedings.neurips.cc/paper_files/paper/2017/hash/03e0704b5690a2dee1861dc3ad3316c9-Abstract.html | [
"Mingsheng Long",
"ZHANGJIE CAO",
"Jianmin Wang",
"Philip S Yu"
] | null | null | Deep networks trained on large-scale data can learn transferable features to promote learning multiple tasks. Since deep features eventually transition from general to specific along deep networks, a fundamental problem of multi-task learning is how to exploit the task relatedness underlying parameter tensors and impro... | [] | null | 15 | 1506.02117 | title_snapshot |
03e7ef47cee6fa4ae7567394b99912b7 | Query Complexity of Clustering with Side Information | https://proceedings.neurips.cc/paper_files/paper/2017/hash/03e7ef47cee6fa4ae7567394b99912b7-Abstract.html | [
"Arya Mazumdar",
"Barna Saha"
] | null | null | Suppose, we are given a set of $n$ elements to be clustered into $k$ (unknown) clusters, and an oracle/expert labeler that can interactively answer pair-wise queries of the form, ``do two elements $u$ and $v$ belong to the same cluster?''. The goal is to recover the optimum clustering by asking the minimum number of qu... | [] | null | 16 | 1706.07719 | title_snapshot |
04048aeca2c0f5d84639358008ed2ae7 | Non-parametric Structured Output Networks | https://proceedings.neurips.cc/paper_files/paper/2017/hash/04048aeca2c0f5d84639358008ed2ae7-Abstract.html | [
"Andreas Lehrmann",
"Leonid Sigal"
] | null | null | Deep neural networks (DNNs) and probabilistic graphical models (PGMs) are the two main tools for statistical modeling. While DNNs provide the ability to model rich and complex relationships between input and output variables, PGMs provide the ability to encode dependencies among the output variables themselves. End-to-... | [] | null | 17 | null | null |
044a23cadb567653eb51d4eb40acaa88 | Robust Imitation of Diverse Behaviors | https://proceedings.neurips.cc/paper_files/paper/2017/hash/044a23cadb567653eb51d4eb40acaa88-Abstract.html | [
"Ziyu Wang",
"Josh S Merel",
"Scott E Reed",
"Nando de Freitas",
"Gregory Wayne",
"Nicolas Heess"
] | null | null | Deep generative models have recently shown great promise in imitation learning for motor control. Given enough data, even supervised approaches can do one-shot imitation learning; however, they are vulnerable to cascading failures when the agent trajectory diverges from the demonstrations. Compared to purely supervised... | [] | null | 18 | 1707.02747 | title_snapshot |
051928341be67dcba03f0e04104d9047 | High-Order Attention Models for Visual Question Answering | https://proceedings.neurips.cc/paper_files/paper/2017/hash/051928341be67dcba03f0e04104d9047-Abstract.html | [
"Idan Schwartz",
"Alexander Schwing",
"Tamir Hazan"
] | null | null | The quest for algorithms that enable cognitive abilities is an important part of machine learning. A common trait in many recently investigated cognitive-like tasks is that they take into account different data modalities, such as visual and textual input. In this paper we propose a novel and generally applicable form ... | [] | null | 19 | 1711.04323 | title_snapshot |
05546b0e38ab9175cd905eebcc6ebb76 | FALKON: An Optimal Large Scale Kernel Method | https://proceedings.neurips.cc/paper_files/paper/2017/hash/05546b0e38ab9175cd905eebcc6ebb76-Abstract.html | [
"Alessandro Rudi",
"Luigi Carratino",
"Lorenzo Rosasco"
] | null | null | Kernel methods provide a principled way to perform non linear, nonparametric learning. They rely on solid functional analytic foundations and enjoy optimal statistical properties. However, at least in their basic form, they have limited applicability in large scale scenarios because of stringent computational requireme... | [] | null | 20 | 1705.10958 | title_snapshot |
061412e4a03c02f9902576ec55ebbe77 | Generalized Linear Model Regression under Distance-to-set Penalties | https://proceedings.neurips.cc/paper_files/paper/2017/hash/061412e4a03c02f9902576ec55ebbe77-Abstract.html | [
"Jason Xu",
"Eric Chi",
"Kenneth Lange"
] | null | null | Estimation in generalized linear models (GLM) is complicated by the presence of constraints. One can handle constraints by maximizing a penalized log-likelihood. Penalties such as the lasso are effective in high dimensions but often lead to severe shrinkage. This paper explores instead penalizing the squared distance t... | [] | null | 21 | 1711.01341 | title_snapshot |
07042ac7d03d3b9911a00da43ce0079a | Fisher GAN | https://proceedings.neurips.cc/paper_files/paper/2017/hash/07042ac7d03d3b9911a00da43ce0079a-Abstract.html | [
"Youssef Mroueh",
"Tom Sercu"
] | null | null | Generative Adversarial Networks (GANs) are powerful models for learning complex distributions. Stable training of GANs has been addressed in many recent works which explore different metrics between distributions. In this paper we introduce Fisher GAN that fits within the Integral Probability Metrics (IPM) framework fo... | [] | null | 22 | 1705.09675 | title_snapshot |
070dbb6024b5ef93784428afc71f2146 | Minimax Estimation of Bandable Precision Matrices | https://proceedings.neurips.cc/paper_files/paper/2017/hash/070dbb6024b5ef93784428afc71f2146-Abstract.html | [
"Addison Hu",
"Sahand Negahban"
] | null | null | The inverse covariance matrix provides considerable insight for understanding statistical models in the multivariate setting. In particular, when the distribution over variables is assumed to be multivariate normal, the sparsity pattern in the inverse covariance matrix, commonly referred to as the precision matrix, cor... | [] | null | 23 | 1710.07006 | title_snapshot |
07211688a0869d995947a8fb11b215d6 | Kernel functions based on triplet comparisons | https://proceedings.neurips.cc/paper_files/paper/2017/hash/07211688a0869d995947a8fb11b215d6-Abstract.html | [
"Matthäus Kleindessner",
"Ulrike von Luxburg"
] | null | null | Given only information in the form of similarity triplets "Object A is more similar to object B than to object C" about a data set, we propose two ways of defining a kernel function on the data set. While previous approaches construct a low-dimensional Euclidean embedding of the data set that reflects the given similar... | [] | null | 24 | 1607.08456 | title_snapshot |
072b030ba126b2f4b2374f342be9ed44 | Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization | https://proceedings.neurips.cc/paper_files/paper/2017/hash/072b030ba126b2f4b2374f342be9ed44-Abstract.html | [
"Fabian Pedregosa",
"Rémi Leblond",
"Simon Lacoste-Julien"
] | null | null | Due to their simplicity and excellent performance, parallel asynchronous variants of stochastic gradient descent have become popular methods to solve a wide range of large-scale optimization problems on multi-core architectures. Yet, despite their practical success, support for nonsmooth objectives is still lacking, ma... | [] | null | 25 | 1707.06468 | title_snapshot |
07563a3fe3bbe7e3ba84431ad9d055af | A New Theory for Matrix Completion | https://proceedings.neurips.cc/paper_files/paper/2017/hash/07563a3fe3bbe7e3ba84431ad9d055af-Abstract.html | [
"Guangcan Liu",
"Qingshan Liu",
"Xiaotong Yuan"
] | null | null | Prevalent matrix completion theories reply on an assumption that the locations of the missing data are distributed uniformly and randomly (i.e., uniform sampling). Nevertheless, the reason for observations being missing often depends on the unseen observations themselves, and thus the missing data in practice usually o... | [] | null | 26 | null | null |
076023edc9187cf1ac1f1163470e479a | A Bayesian Data Augmentation Approach for Learning Deep Models | https://proceedings.neurips.cc/paper_files/paper/2017/hash/076023edc9187cf1ac1f1163470e479a-Abstract.html | [
"Toan Tran",
"Trung Pham",
"Gustavo Carneiro",
"Lyle Palmer",
"Ian Reid"
] | null | null | Data augmentation is an essential part of the training process applied to deep learning models. The motivation is that a robust training process for deep learning models depends on large annotated datasets, which are expensive to be acquired, stored and processed. Therefore a reasonable alternative is to be able to aut... | [] | null | 27 | 1710.10564 | title_snapshot |
0768281a05da9f27df178b5c39a51263 | Deep Hyperalignment | https://proceedings.neurips.cc/paper_files/paper/2017/hash/0768281a05da9f27df178b5c39a51263-Abstract.html | [
"Muhammad Yousefnezhad",
"Daoqiang Zhang"
] | null | null | This paper proposes Deep Hyperalignment (DHA) as a regularized, deep extension, scalable Hyperalignment (HA) method, which is well-suited for applying functional alignment to fMRI datasets with nonlinearity, high-dimensionality (broad ROI), and a large number of subjects. Unlink previous methods, DHA is not limited by ... | [] | null | 28 | 1710.03923 | title_snapshot |
077e29b11be80ab57e1a2ecabb7da330 | Best of Both Worlds: Transferring Knowledge from Discriminative Learning to a Generative Visual Dialog Model | https://proceedings.neurips.cc/paper_files/paper/2017/hash/077e29b11be80ab57e1a2ecabb7da330-Abstract.html | [
"Jiasen Lu",
"Anitha Kannan",
"Jianwei Yang",
"Devi Parikh",
"Dhruv Batra"
] | null | null | We present a novel training framework for neural sequence models, particularly for grounded dialog generation. The standard training paradigm for these models is maximum likelihood estimation (MLE), or minimizing the cross-entropy of the human responses. Across a variety of domains, a recurring problem with MLE trained... | [] | null | 29 | 1706.01554 | title_snapshot |
07811dc6c422334ce36a09ff5cd6fe71 | PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference | https://proceedings.neurips.cc/paper_files/paper/2017/hash/07811dc6c422334ce36a09ff5cd6fe71-Abstract.html | [
"Jonathan Huggins",
"Ryan P. Adams",
"Tamara Broderick"
] | null | null | Generalized linear models (GLMs)---such as logistic regression, Poisson regression, and robust regression---provide interpretable models for diverse data types. Probabilistic approaches, particularly Bayesian ones, allow coherent estimates of uncertainty, incorporation of prior information, and sharing of power across ... | [] | null | 30 | 1709.09216 | title_snapshot |
08b255a5d42b89b0585260b6f2360bdd | Online multiclass boosting | https://proceedings.neurips.cc/paper_files/paper/2017/hash/08b255a5d42b89b0585260b6f2360bdd-Abstract.html | [
"Young Hun Jung",
"Jack Goetz",
"Ambuj Tewari"
] | null | null | Recent work has extended the theoretical analysis of boosting algorithms to multiclass problems and to online settings. However, the multiclass extension is in the batch setting and the online extensions only consider binary classification. We fill this gap in the literature by defining, and justifying, a weak learning... | [] | null | 31 | 1702.07305 | title_snapshot |
08e6bea8e90ba87af3c9554d94db6579 | State Aware Imitation Learning | https://proceedings.neurips.cc/paper_files/paper/2017/hash/08e6bea8e90ba87af3c9554d94db6579-Abstract.html | [
"Yannick Schroecker",
"Charles L Isbell"
] | null | null | Imitation learning is the study of learning how to act given a set of demonstrations provided by a human expert. It is intuitively apparent that learning to take optimal actions is a simpler undertaking in situations that are similar to the ones shown by the teacher. However, imitation learning approaches do not tend t... | [] | null | 32 | null | null |
09fb05dd477d4ae6479985ca56c5a12d | Adaptive SVRG Methods under Error Bound Conditions with Unknown Growth Parameter | https://proceedings.neurips.cc/paper_files/paper/2017/hash/09fb05dd477d4ae6479985ca56c5a12d-Abstract.html | [
"Yi Xu",
"Qihang Lin",
"Tianbao Yang"
] | null | null | Error bound, an inherent property of an optimization problem, has recently revived in the development of algorithms with improved global convergence without strong convexity. The most studied error bound is the quadratic error bound, which generalizes strong convexity and is satisfied by a large family of machine learn... | [] | null | 33 | null | null |
0a0a0c8aaa00ade50f74a3f0ca981ed7 | Unsupervised Learning of Disentangled and Interpretable Representations from Sequential Data | https://proceedings.neurips.cc/paper_files/paper/2017/hash/0a0a0c8aaa00ade50f74a3f0ca981ed7-Abstract.html | [
"Wei-Ning Hsu",
"Yu Zhang",
"James Glass"
] | null | null | We present a factorized hierarchical variational autoencoder, which learns disentangled and interpretable representations from sequential data without supervision. Specifically, we exploit the multi-scale nature of information in sequential data by formulating it explicitly within a factorized hierarchical graphical mo... | [] | null | 34 | 1709.07902 | title_snapshot |
0a5c79b1eaf15445da252ada718857e9 | Recurrent Ladder Networks | https://proceedings.neurips.cc/paper_files/paper/2017/hash/0a5c79b1eaf15445da252ada718857e9-Abstract.html | [
"Isabeau Prémont-Schwarz",
"Alexander Ilin",
"Tele Hao",
"Antti Rasmus",
"Rinu Boney",
"Harri Valpola"
] | null | null | We propose a recurrent extension of the Ladder networks whose structure is motivated by the inference required in hierarchical latent variable models. We demonstrate that the recurrent Ladder is able to handle a wide variety of complex learning tasks that benefit from iterative inference and temporal modeling. The arch... | [] | null | 35 | 1707.09219 | title_snapshot |
0abdc563a06105aee3c6136871c9f4d1 | Distral: Robust multitask reinforcement learning | https://proceedings.neurips.cc/paper_files/paper/2017/hash/0abdc563a06105aee3c6136871c9f4d1-Abstract.html | [
"Yee Teh",
"Victor Bapst",
"Wojciech M. Czarnecki",
"John Quan",
"James Kirkpatrick",
"Raia Hadsell",
"Nicolas Heess",
"Razvan Pascanu"
] | null | null | Most deep reinforcement learning algorithms are data inefficient in complex and rich environments, limiting their applicability to many scenarios. One direction for improving data efficiency is multitask learning with shared neural network parameters, where efficiency may be improved through transfer across related tas... | [] | null | 36 | 1707.04175 | title_snapshot |
0bed45bd5774ffddc95ffe500024f628 | Real-Time Bidding with Side Information | https://proceedings.neurips.cc/paper_files/paper/2017/hash/0bed45bd5774ffddc95ffe500024f628-Abstract.html | [
"arthur flajolet",
"Patrick Jaillet"
] | null | null | We consider the problem of repeated bidding in online advertising auctions when some side information (e.g. browser cookies) is available ahead of submitting a bid in the form of a $d$-dimensional vector. The goal for the advertiser is to maximize the total utility (e.g. the total number of clicks) derived from display... | [] | null | 37 | null | null |
0c74b7f78409a4022a2c4c5a5ca3ee19 | Learning Spherical Convolution for Fast Features from 360° Imagery | https://proceedings.neurips.cc/paper_files/paper/2017/hash/0c74b7f78409a4022a2c4c5a5ca3ee19-Abstract.html | [
"Yu-Chuan Su",
"Kristen Grauman"
] | null | null | While 360° cameras offer tremendous new possibilities in vision, graphics, and augmented reality, the spherical images they produce make core feature extraction non-trivial. Convolutional neural networks (CNNs) trained on images from perspective cameras yield “flat" filters, yet 360° images cannot be projected to a sin... | [] | null | 38 | 1708.00919 | title_snapshot |
0d9095b0d6bbe98ea0c9c02b11b59ee3 | Approximate Supermodularity Bounds for Experimental Design | https://proceedings.neurips.cc/paper_files/paper/2017/hash/0d9095b0d6bbe98ea0c9c02b11b59ee3-Abstract.html | [
"Luiz Chamon",
"Alejandro Ribeiro"
] | null | null | This work provides performance guarantees for the greedy solution of experimental design problems. In particular, it focuses on A- and E-optimal designs, for which typical guarantees do not apply since the mean-square error and the maximum eigenvalue of the estimation error covariance matrix are not supermodular. To do... | [] | null | 39 | 1711.01501 | title_snapshot |
0e55666a4ad822e0e34299df3591d979 | Differentiable Learning of Logical Rules for Knowledge Base Reasoning | https://proceedings.neurips.cc/paper_files/paper/2017/hash/0e55666a4ad822e0e34299df3591d979-Abstract.html | [
"Fan Yang",
"Zhilin Yang",
"William W. Cohen"
] | null | null | We study the problem of learning probabilistic first-order logical rules for knowledge base reasoning. This learning problem is difficult because it requires learning the parameters in a continuous space as well as the structure in a discrete space. We propose a framework, Neural Logic Programming, that combines the pa... | [] | null | 40 | 1702.08367 | title_snapshot |
0e7c7d6c41c76b9ee6445ae01cc0181d | When Cyclic Coordinate Descent Outperforms Randomized Coordinate Descent | https://proceedings.neurips.cc/paper_files/paper/2017/hash/0e7c7d6c41c76b9ee6445ae01cc0181d-Abstract.html | [
"Mert Gurbuzbalaban",
"Asuman Ozdaglar",
"Pablo A Parrilo",
"Nuri Vanli"
] | null | null | The coordinate descent (CD) method is a classical optimization algorithm that has seen a revival of interest because of its competitive performance in machine learning applications. A number of recent papers provided convergence rate estimates for their deterministic (cyclic) and randomized variants that differ in the ... | [] | null | 41 | null | null |
0ebcc77dc72360d0eb8e9504c78d38bd | Principles of Riemannian Geometry in Neural Networks | https://proceedings.neurips.cc/paper_files/paper/2017/hash/0ebcc77dc72360d0eb8e9504c78d38bd-Abstract.html | [
"Michael Hauser",
"Asok Ray"
] | null | null | This study deals with neural networks in the sense of geometric transformations acting on the coordinate representation of the underlying data manifold which the data is sampled from. It forms part of an attempt to construct a formalized general theory of neural networks in the setting of Riemannian geometry. From this... | [] | null | 42 | null | null |
0efbe98067c6c73dba1250d2beaa81f9 | Continual Learning with Deep Generative Replay | https://proceedings.neurips.cc/paper_files/paper/2017/hash/0efbe98067c6c73dba1250d2beaa81f9-Abstract.html | [
"Hanul Shin",
"Jung Kwon Lee",
"Jaehong Kim",
"Jiwon Kim"
] | null | null | Attempts to train a comprehensive artificial intelligence capable of solving multiple tasks have been impeded by a chronic problem called catastrophic forgetting. Although simply replaying all previous data alleviates the problem, it requires large memory and even worse, often infeasible in real world applications wher... | [] | null | 43 | 1705.08690 | title_snapshot |
0f3d014eead934bbdbacb62a01dc4831 | Nonlinear random matrix theory for deep learning | https://proceedings.neurips.cc/paper_files/paper/2017/hash/0f3d014eead934bbdbacb62a01dc4831-Abstract.html | [
"Jeffrey Pennington",
"Pratik Worah"
] | null | null | Neural network configurations with random weights play an important role in the analysis of deep learning. They define the initial loss landscape and are closely related to kernel and random feature methods. Despite the fact that these networks are built out of random matrices, the vast and powerful machinery of random... | [] | null | 44 | null | null |
1006ff12c465532f8c574aeaa4461b16 | Identification of Gaussian Process State Space Models | https://proceedings.neurips.cc/paper_files/paper/2017/hash/1006ff12c465532f8c574aeaa4461b16-Abstract.html | [
"Stefanos Eleftheriadis",
"Tom Nicholson",
"Marc Deisenroth",
"James Hensman"
] | null | null | The Gaussian process state space model (GPSSM) is a non-linear dynamical system, where unknown transition and/or measurement mappings are described by GPs. Most research in GPSSMs has focussed on the state estimation problem, i.e., computing a posterior of the latent state given the model. However, the key challenge in... | [] | null | 45 | 1705.10888 | title_snapshot |
10c272d06794d3e5785d5e7c5356e9ff | Estimation of the covariance structure of heavy-tailed distributions | https://proceedings.neurips.cc/paper_files/paper/2017/hash/10c272d06794d3e5785d5e7c5356e9ff-Abstract.html | [
"Xiaohan Wei",
"Stanislav Minsker"
] | null | null | We propose and analyze a new estimator of the covariance matrix that admits strong theoretical guarantees under weak assumptions on the underlying distribution, such as existence of moments of only low order. While estimation of covariance matrices corresponding to sub-Gaussian distributions is well-understood, much le... | [] | null | 46 | 1708.00502 | title_snapshot |
10c66082c124f8afe3df4886f5e516e0 | Robust Optimization for Non-Convex Objectives | https://proceedings.neurips.cc/paper_files/paper/2017/hash/10c66082c124f8afe3df4886f5e516e0-Abstract.html | [
"Robert S. Chen",
"Brendan Lucier",
"Yaron Singer",
"Vasilis Syrgkanis"
] | null | null | We consider robust optimization problems, where the goal is to optimize in the worst case over a class of objective functions. We develop a reduction from robust improper optimization to stochastic optimization: given an oracle that returns $\alpha$-approximate solutions for distributions over objectives, we compute a ... | [] | null | 47 | 1707.01047 | title_snapshot |
10ce03a1ed01077e3e289f3e53c72813 | Exploring Generalization in Deep Learning | https://proceedings.neurips.cc/paper_files/paper/2017/hash/10ce03a1ed01077e3e289f3e53c72813-Abstract.html | [
"Behnam Neyshabur",
"Srinadh Bhojanapalli",
"David Mcallester",
"Nati Srebro"
] | null | null | With a goal of understanding what drives generalization in deep networks, we consider several recently suggested explanations, including norm-based control, sharpness and robustness. We study how these measures can ensure generalization, highlighting the importance of scale normalization, and making a connection betwee... | [] | null | 48 | 1706.08947 | title_snapshot |
1113d7a76ffceca1bb350bfe145467c6 | Spherical convolutions and their application in molecular modelling | https://proceedings.neurips.cc/paper_files/paper/2017/hash/1113d7a76ffceca1bb350bfe145467c6-Abstract.html | [
"Wouter Boomsma",
"Jes Frellsen"
] | null | null | Convolutional neural networks are increasingly used outside the domain of image analysis, in particular in various areas of the natural sciences concerned with spatial data. Such networks often work out-of-the box, and in some cases entire model architectures from image analysis can be carried over to other problem dom... | [] | null | 49 | null | null |
1177967c7957072da3dc1db4ceb30e7a | Safe Adaptive Importance Sampling | https://proceedings.neurips.cc/paper_files/paper/2017/hash/1177967c7957072da3dc1db4ceb30e7a-Abstract.html | [
"Sebastian U Stich",
"Anant Raj",
"Martin Jaggi"
] | null | null | Importance sampling has become an indispensable strategy to speed up optimization algorithms for large-scale applications. Improved adaptive variants -- using importance values defined by the complete gradient information which changes during optimization -- enjoy favorable theoretical properties, but are typically com... | [] | null | 50 | 1711.02637 | title_snapshot |
11b921ef080f7736089c757404650e40 | Introspective Classification with Convolutional Nets | https://proceedings.neurips.cc/paper_files/paper/2017/hash/11b921ef080f7736089c757404650e40-Abstract.html | [
"Long Jin",
"Justin Lazarow",
"Zhuowen Tu"
] | null | null | We propose introspective convolutional networks (ICN) that emphasize the importance of having convolutional neural networks empowered with generative capabilities. We employ a reclassification-by-synthesis algorithm to perform training using a formulation stemmed from the Bayes theory. Our ICN tries to iteratively: (1)... | [] | null | 51 | 1704.07816 | title_snapshot |
1264a061d82a2edae1574b07249800d6 | Hybrid Reward Architecture for Reinforcement Learning | https://proceedings.neurips.cc/paper_files/paper/2017/hash/1264a061d82a2edae1574b07249800d6-Abstract.html | [
"Harm Van Seijen",
"Mehdi Fatemi",
"Joshua Romoff",
"Romain Laroche",
"Tavian Barnes",
"Jeffrey Tsang"
] | null | null | One of the main challenges in reinforcement learning (RL) is generalisation. In typical deep RL methods this is achieved by approximating the optimal value function with a low-dimensional representation using a deep network. While this approach works well in many domains, in domains where the optimal value function can... | [] | null | 52 | 1706.04208 | title_snapshot |
1271a7029c9df08643b631b02cf9e116 | When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness | https://proceedings.neurips.cc/paper_files/paper/2017/hash/1271a7029c9df08643b631b02cf9e116-Abstract.html | [
"Chris Russell",
"Matt J Kusner",
"Joshua Loftus",
"Ricardo Silva"
] | null | null | Machine learning is now being used to make crucial decisions about people's lives. For nearly all of these decisions there is a risk that individuals of a certain race, gender, sexual orientation, or any other subpopulation are unfairly discriminated against. Our recent method has demonstrated how to use techniques fro... | [] | null | 53 | null | null |
12a1d073d5ed3fa12169c67c4e2ce415 | Dualing GANs | https://proceedings.neurips.cc/paper_files/paper/2017/hash/12a1d073d5ed3fa12169c67c4e2ce415-Abstract.html | [
"Yujia Li",
"Alexander Schwing",
"Kuan-Chieh Wang",
"Richard Zemel"
] | null | null | Generative adversarial nets (GANs) are a promising technique for modeling a distribution from samples. It is however well known that GAN training suffers from instability due to the nature of its saddle point formulation. In this paper, we explore ways to tackle the instability problem by dualizing the discriminator. W... | [] | null | 54 | 1706.06216 | title_snapshot |
136f951362dab62e64eb8e841183c2a9 | A Universal Analysis of Large-Scale Regularized Least Squares Solutions | https://proceedings.neurips.cc/paper_files/paper/2017/hash/136f951362dab62e64eb8e841183c2a9-Abstract.html | [
"Ashkan Panahi",
"Babak Hassibi"
] | null | null | A problem that has been of recent interest in statistical inference, machine learning and signal processing is that of understanding the asymptotic behavior of regularized least squares solutions under random measurement matrices (or dictionaries). The Least Absolute Shrinkage and Selection Operator (LASSO or least-squ... | [] | null | 55 | null | null |
13f3cf8c531952d72e5847c4183e6910 | Diffusion Approximations for Online Principal Component Estimation and Global Convergence | https://proceedings.neurips.cc/paper_files/paper/2017/hash/13f3cf8c531952d72e5847c4183e6910-Abstract.html | [
"Chris Junchi Li",
"Mengdi Wang",
"Han Liu",
"Tong Zhang"
] | null | null | In this paper, we propose to adopt the diffusion approximation tools to study the dynamics of Oja's iteration which is an online stochastic gradient method for the principal component analysis. Oja's iteration maintains a running estimate of the true principal component from streaming data and enjoys less temporal and ... | [] | null | 56 | 1808.09645 | title_snapshot |
13fe9d84310e77f13a6d184dbf1232f3 | k-Support and Ordered Weighted Sparsity for Overlapping Groups: Hardness and Algorithms | https://proceedings.neurips.cc/paper_files/paper/2017/hash/13fe9d84310e77f13a6d184dbf1232f3-Abstract.html | [
"Cong Han Lim",
"Stephen Wright"
] | null | null | The k-support and OWL norms generalize the l1 norm, providing better prediction accuracy and better handling of correlated variables. We study the norms obtained from extending the k-support norm and OWL norms to the setting in which there are overlapping groups. The resulting norms are in general NP-hard to compute, b... | [] | null | 57 | null | null |
147ebe637038ca50a1265abac8dea181 | Learning to Model the Tail | https://proceedings.neurips.cc/paper_files/paper/2017/hash/147ebe637038ca50a1265abac8dea181-Abstract.html | [
"Yu-Xiong Wang",
"Deva Ramanan",
"Martial Hebert"
] | null | null | We describe an approach to learning from long-tailed, imbalanced datasets that are prevalent in real-world settings. Here, the challenge is to learn accurate "few-shot'' models for classes in the tail of the class distribution, for which little data is available. We cast this problem as transfer learning, where knowled... | [] | null | 58 | null | null |
14e422f05b68cc0139988e128ee880df | Neural Variational Inference and Learning in Undirected Graphical Models | https://proceedings.neurips.cc/paper_files/paper/2017/hash/14e422f05b68cc0139988e128ee880df-Abstract.html | [
"Volodymyr Kuleshov",
"Stefano Ermon"
] | null | null | Many problems in machine learning are naturally expressed in the language of undirected graphical models. Here, we propose black-box learning and inference algorithms for undirected models that optimize a variational approximation to the log-likelihood of the model. Central to our approach is an upper bound on the log-... | [] | null | 59 | 1711.02679 | title_snapshot |
14ea0d5b0cf49525d1866cb1e95ada5d | Aggressive Sampling for Multi-class to Binary Reduction with Applications to Text Classification | https://proceedings.neurips.cc/paper_files/paper/2017/hash/14ea0d5b0cf49525d1866cb1e95ada5d-Abstract.html | [
"Bikash Joshi",
"Massih R. Amini",
"Ioannis Partalas",
"Franck Iutzeler",
"Yury Maximov"
] | null | null | We address the problem of multi-class classification in the case where the number of classes is very large. We propose a double sampling strategy on top of a multi-class to binary reduction strategy, which transforms the original multi-class problem into a binary classification problem over pairs of examples. The aim o... | [] | null | 60 | 1701.06511 | title_snapshot |
165a59f7cf3b5c4396ba65953d679f17 | Learning Linear Dynamical Systems via Spectral Filtering | https://proceedings.neurips.cc/paper_files/paper/2017/hash/165a59f7cf3b5c4396ba65953d679f17-Abstract.html | [
"Elad Hazan",
"Karan Singh",
"Cyril Zhang"
] | null | null | We present an efficient and practical algorithm for the online prediction of discrete-time linear dynamical systems with a symmetric transition matrix. We circumvent the non-convex optimization problem using improper learning: carefully overparameterize the class of LDSs by a polylogarithmic factor, in exchange for con... | [] | null | 61 | 1711.00946 | title_snapshot |
1680e9fa7b4dd5d62ece800239bb53bd | Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes | https://proceedings.neurips.cc/paper_files/paper/2017/hash/1680e9fa7b4dd5d62ece800239bb53bd-Abstract.html | [
"Zhenwen Dai",
"Mauricio Álvarez",
"Neil Lawrence"
] | null | null | Often in machine learning, data are collected as a combination of multiple conditions, e.g., the voice recordings of multiple persons, each labeled with an ID. How could we build a model that captures the latent information related to these conditions and generalize to a new one with few data? We present a new model ca... | [] | null | 62 | 1705.09862 | title_snapshot |
16a5cdae362b8d27a1d8f8c7b78b4330 | Semi-Supervised Learning for Optical Flow with Generative Adversarial Networks | https://proceedings.neurips.cc/paper_files/paper/2017/hash/16a5cdae362b8d27a1d8f8c7b78b4330-Abstract.html | [
"Wei-Sheng Lai",
"Jia-Bin Huang",
"Ming-Hsuan Yang"
] | null | null | Convolutional neural networks (CNNs) have recently been applied to the optical flow estimation problem. As training the CNNs requires sufficiently large ground truth training data, existing approaches resort to synthetic, unrealistic datasets. On the other hand, unsupervised methods are capable of leveraging real-world... | [] | null | 63 | null | null |
1700002963a49da13542e0726b7bb758 | Phase Transitions in the Pooled Data Problem | https://proceedings.neurips.cc/paper_files/paper/2017/hash/1700002963a49da13542e0726b7bb758-Abstract.html | [
"Jonathan Scarlett",
"Volkan Cevher"
] | null | null | In this paper, we study the {\em pooled data} problem of identifying the labels associated with a large collection of items, based on a sequence of pooled tests revealing the counts of each label within the pool. In the noiseless setting, we identify an exact asymptotic threshold on the required number of tests with op... | [] | null | 64 | 1710.06766 | title_snapshot |
17d8da815fa21c57af9829fb0a869602 | Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement Learning | https://proceedings.neurips.cc/paper_files/paper/2017/hash/17d8da815fa21c57af9829fb0a869602-Abstract.html | [
"Christoph Dann",
"Tor Lattimore",
"Emma Brunskill"
] | null | null | Statistical performance bounds for reinforcement learning (RL) algorithms can be critical for high-stakes applications like healthcare. This paper introduces a new framework for theoretically measuring the performance of such algorithms called Uniform-PAC, which is a strengthening of the classical Probably Approximatel... | [] | null | 65 | 1703.07710 | title_snapshot |
17ed8abedc255908be746d245e50263a | Stein Variational Gradient Descent as Gradient Flow | https://proceedings.neurips.cc/paper_files/paper/2017/hash/17ed8abedc255908be746d245e50263a-Abstract.html | [
"Qiang Liu"
] | null | null | Stein variational gradient descent (SVGD) is a deterministic sampling algorithm that iteratively transports a set of particles to approximate given distributions, based on a gradient-based update constructed to optimally decrease the KL divergence within a function space. This paper develops the first theoretical analy... | [] | null | 66 | 1704.07520 | title_snapshot |
17fafe5f6ce2f1904eb09d2e80a4cbf6 | Expectation Propagation for t-Exponential Family Using q-Algebra | https://proceedings.neurips.cc/paper_files/paper/2017/hash/17fafe5f6ce2f1904eb09d2e80a4cbf6-Abstract.html | [
"Futoshi Futami",
"Issei Sato",
"Masashi Sugiyama"
] | null | null | Exponential family distributions are highly useful in machine learning since their calculation can be performed efficiently through natural parameters. The exponential family has recently been extended to the t-exponential family, which contains Student-t distributions as family members and thus allows us to handle noi... | [] | null | 67 | 1705.09046 | title_snapshot |
186a157b2992e7daed3677ce8e9fe40f | Collaborative PAC Learning | https://proceedings.neurips.cc/paper_files/paper/2017/hash/186a157b2992e7daed3677ce8e9fe40f-Abstract.html | [
"Avrim Blum",
"Nika Haghtalab",
"Ariel D Procaccia",
"Mingda Qiao"
] | null | null | We introduce a collaborative PAC learning model, in which k players attempt to learn the same underlying concept. We ask how much more information is required to learn an accurate classifier for all players simultaneously. We refer to the ratio between the sample complexity of collaborative PAC learning and its non-col... | [] | null | 68 | null | null |
18bb68e2b38e4a8ce7cf4f6b2625768c | Polynomial time algorithms for dual volume sampling | https://proceedings.neurips.cc/paper_files/paper/2017/hash/18bb68e2b38e4a8ce7cf4f6b2625768c-Abstract.html | [
"Chengtao Li",
"Stefanie Jegelka",
"Suvrit Sra"
] | null | null | We study dual volume sampling, a method for selecting k columns from an n*m short and wide matrix (n <= k <= m) such that the probability of selection is proportional to the volume spanned by the rows of the induced submatrix. This method was proposed by Avron and Boutsidis (2013), who showed it to be a promising metho... | [] | null | 69 | 1703.02674 | title_snapshot |
18d10dc6e666eab6de9215ae5b3d54df | Premise Selection for Theorem Proving by Deep Graph Embedding | https://proceedings.neurips.cc/paper_files/paper/2017/hash/18d10dc6e666eab6de9215ae5b3d54df-Abstract.html | [
"Mingzhe Wang",
"Yihe Tang",
"Jian Wang",
"Jia Deng"
] | null | null | We propose a deep learning-based approach to the problem of premise selection: selecting mathematical statements relevant for proving a given conjecture. We represent a higher-order logic formula as a graph that is invariant to variable renaming but still fully preserves syntactic and semantic information. We then embe... | [] | null | 70 | 1709.09994 | title_snapshot |
192fc044e74dffea144f9ac5dc9f3395 | Differentiable Learning of Submodular Models | https://proceedings.neurips.cc/paper_files/paper/2017/hash/192fc044e74dffea144f9ac5dc9f3395-Abstract.html | [
"Josip Djolonga",
"Andreas Krause"
] | null | null | Can we incorporate discrete optimization algorithms within modern machine learning models? For example, is it possible to use in deep architectures a layer whose output is the minimal cut of a parametrized graph? Given that these models are trained end-to-end by leveraging gradient information, the introduction of such... | [] | null | 71 | null | null |
1943102704f8f8f3302c2b730728e023 | YASS: Yet Another Spike Sorter | https://proceedings.neurips.cc/paper_files/paper/2017/hash/1943102704f8f8f3302c2b730728e023-Abstract.html | [
"Jin Hyung Lee",
"David E Carlson",
"Hooshmand Shokri Razaghi",
"Weichi Yao",
"Georges A Goetz",
"Espen Hagen",
"Eleanor Batty",
"E. J. Chichilnisky",
"Gaute T. Einevoll",
"Liam Paninski"
] | null | null | Spike sorting is a critical first step in extracting neural signals from large-scale electrophysiological data. This manuscript describes an efficient, reliable pipeline for spike sorting on dense multi-electrode arrays (MEAs), where neural signals appear across many electrodes and spike sorting currently represents a ... | [] | null | 72 | null | null |
194cf6c2de8e00c05fcf16c498adc7bf | Variational Laws of Visual Attention for Dynamic Scenes | https://proceedings.neurips.cc/paper_files/paper/2017/hash/194cf6c2de8e00c05fcf16c498adc7bf-Abstract.html | [
"Dario Zanca",
"Marco Gori"
] | null | null | Computational models of visual attention are at the crossroad of disciplines like cognitive science, computational neuroscience, and computer vision. This paper proposes a model of attentional scanpath that is based on the principle that there are foundational laws that drive the emergence of visual attention. We devis... | [] | null | 73 | null | null |
1abb1e1ea5f481b589da52303b091cbb | How regularization affects the critical points in linear networks | https://proceedings.neurips.cc/paper_files/paper/2017/hash/1abb1e1ea5f481b589da52303b091cbb-Abstract.html | [
"Amirhossein Taghvaei",
"Jin W Kim",
"Prashant Mehta"
] | null | null | This paper is concerned with the problem of representing and learning a linear transformation using a linear neural network. In recent years, there is a growing interest in the study of such networks, in part due to the successes of deep learning. The main question of this body of research (and also of our paper) is re... | [] | null | 74 | 1709.09625 | title_snapshot |
1b5230e3ea6d7123847ad55a1e06fffd | On Tensor Train Rank Minimization : Statistical Efficiency and Scalable Algorithm | https://proceedings.neurips.cc/paper_files/paper/2017/hash/1b5230e3ea6d7123847ad55a1e06fffd-Abstract.html | [
"Masaaki Imaizumi",
"Takanori Maehara",
"Kohei Hayashi"
] | null | null | Tensor train (TT) decomposition provides a space-efficient representation for higher-order tensors. Despite its advantage, we face two crucial limitations when we apply the TT decomposition to machine learning problems: the lack of statistical theory and of scalable algorithms. In this paper, we address the limitations... | [] | null | 75 | 1708.00132 | title_snapshot |
1baff70e2669e8376347efd3a874a341 | EX2: Exploration with Exemplar Models for Deep Reinforcement Learning | https://proceedings.neurips.cc/paper_files/paper/2017/hash/1baff70e2669e8376347efd3a874a341-Abstract.html | [
"Justin Fu",
"John Co-Reyes",
"Sergey Levine"
] | null | null | Deep reinforcement learning algorithms have been shown to learn complex tasks using highly general policy classes. However, sparse reward problems remain a significant challenge. Exploration methods based on novelty detection have been particularly successful in such settings but typically require generative or predict... | [] | null | 76 | 1703.01260 | title_snapshot |
1c303b0eed3133200cf715285011b4e4 | Training Quantized Nets: A Deeper Understanding | https://proceedings.neurips.cc/paper_files/paper/2017/hash/1c303b0eed3133200cf715285011b4e4-Abstract.html | [
"Hao Li",
"Soham De",
"Zheng Xu",
"Christoph Studer",
"Hanan Samet",
"Tom Goldstein"
] | null | null | Currently, deep neural networks are deployed on low-power portable devices by first training a full-precision model using powerful hardware, and then deriving a corresponding low-precision model for efficient inference on such systems. However, training models directly with coarsely quantized weights is a key step towa... | [] | null | 77 | 1706.02379 | title_snapshot |
1c54985e4f95b7819ca0357c0cb9a09f | Convolutional Gaussian Processes | https://proceedings.neurips.cc/paper_files/paper/2017/hash/1c54985e4f95b7819ca0357c0cb9a09f-Abstract.html | [
"Mark van der Wilk",
"Carl Edward Rasmussen",
"James Hensman"
] | null | null | We present a practical way of introducing convolutional structure into Gaussian processes, making them more suited to high-dimensional inputs like images. The main contribution of our work is the construction of an inter-domain inducing point approximation that is well-tailored to the convolutional kernel. This allows ... | [] | null | 78 | 1709.01894 | title_snapshot |
1ce927f875864094e3906a4a0b5ece68 | Best Response Regression | https://proceedings.neurips.cc/paper_files/paper/2017/hash/1ce927f875864094e3906a4a0b5ece68-Abstract.html | [
"Omer Ben-Porat",
"Moshe Tennenholtz"
] | null | null | In a regression task, a predictor is given a set of instances, along with a real value for each point. Subsequently, she has to identify the value of a new instance as accurately as possible. In this work, we initiate the study of strategic predictions in machine learning. We consider a regression task tackled by two p... | [] | null | 79 | null | null |
1cecc7a77928ca8133fa24680a88d2f9 | Elementary Symmetric Polynomials for Optimal Experimental Design | https://proceedings.neurips.cc/paper_files/paper/2017/hash/1cecc7a77928ca8133fa24680a88d2f9-Abstract.html | [
"Zelda E. Mariet",
"Suvrit Sra"
] | null | null | We revisit the classical problem of optimal experimental design (OED) under a new mathematical model grounded in a geometric motivation. Specifically, we introduce models based on elementary symmetric polynomials; these polynomials capture "partial volumes" and offer a graded interpolation between the widely used A-opt... | [] | null | 80 | 1705.09677 | title_snapshot |
1dba5eed8838571e1c80af145184e515 | Learning from Complementary Labels | https://proceedings.neurips.cc/paper_files/paper/2017/hash/1dba5eed8838571e1c80af145184e515-Abstract.html | [
"Takashi Ishida",
"Gang Niu",
"Weihua Hu",
"Masashi Sugiyama"
] | null | null | Collecting labeled data is costly and thus a critical bottleneck in real-world classification tasks. To mitigate this problem, we propose a novel setting, namely learning from complementary labels for multi-class classification. A complementary label specifies a class that a pattern does not belong to. Collecting compl... | [] | null | 81 | 1705.07541 | title_snapshot |
1f1baa5b8edac74eb4eaa329f14a0361 | Dynamic Importance Sampling for Anytime Bounds of the Partition Function | https://proceedings.neurips.cc/paper_files/paper/2017/hash/1f1baa5b8edac74eb4eaa329f14a0361-Abstract.html | [
"Qi Lou",
"Rina Dechter",
"Alex Ihler"
] | null | null | Computing the partition function is a key inference task in many graphical models. In this paper, we propose a dynamic importance sampling scheme that provides anytime finite-sample bounds for the partition function. Our algorithm balances the advantages of the three major inference strategies, heuristic search, variat... | [] | null | 82 | null | null |
1f71e393b3809197ed66df836fe833e5 | Process-constrained batch Bayesian optimisation | https://proceedings.neurips.cc/paper_files/paper/2017/hash/1f71e393b3809197ed66df836fe833e5-Abstract.html | [
"Pratibha Vellanki",
"Santu Rana",
"Sunil Gupta",
"David Rubin",
"Alessandra Sutti",
"Thomas Dorin",
"Murray Height",
"Paul Sanders",
"Svetha Venkatesh"
] | null | null | Abstract Prevailing batch Bayesian optimisation methods allow all control variables to be freely altered at each iteration. Real-world experiments, however, often have physical limitations making it time-consuming to alter all settings for each recommendation in a batch. This gives rise to a unique problem in BO: in a ... | [] | null | 83 | null | null |
1ff8a7b5dc7a7d1f0ed65aaa29c04b1e | Uprooting and Rerooting Higher-Order Graphical Models | https://proceedings.neurips.cc/paper_files/paper/2017/hash/1ff8a7b5dc7a7d1f0ed65aaa29c04b1e-Abstract.html | [
"Mark Rowland",
"Adrian Weller"
] | null | null | The idea of uprooting and rerooting graphical models was introduced specifically for binary pairwise models by Weller (2016) as a way to transform a model to any of a whole equivalence class of related models, such that inference on any one model yields inference results for all others. This is very helpful since infer... | [] | null | 84 | null | null |
20c86a628232a67e7bd46f76fba7ce12 | Learned in Translation: Contextualized Word Vectors | https://proceedings.neurips.cc/paper_files/paper/2017/hash/20c86a628232a67e7bd46f76fba7ce12-Abstract.html | [
"Bryan McCann",
"James Bradbury",
"Caiming Xiong",
"Richard Socher"
] | null | null | Computer vision has benefited from initializing multiple deep layers with weights pretrained on large supervised training sets like ImageNet. Natural language processing (NLP) typically sees initialization of only the lowest layer of deep models with pretrained word vectors. In this paper, we use a deep LSTM encoder fr... | [] | null | 85 | 1708.00107 | title_snapshot |
2131f8ecf18db66a758f718dc729e00e | Semisupervised Clustering, AND-Queries and Locally Encodable Source Coding | https://proceedings.neurips.cc/paper_files/paper/2017/hash/2131f8ecf18db66a758f718dc729e00e-Abstract.html | [
"Arya Mazumdar",
"Soumyabrata Pal"
] | null | null | Source coding is the canonical problem of data compression in information theory. In a locally encodable source coding, each compressed bit depends on only few bits of the input. In this paper, we show that a recently popular model of semisupervised clustering is equivalent to locally encodable source coding. In this m... | [] | null | 86 | 1904.00507 | title_judge |
217e342fc01668b10cb1188d40d3370e | Regularizing Deep Neural Networks by Noise: Its Interpretation and Optimization | https://proceedings.neurips.cc/paper_files/paper/2017/hash/217e342fc01668b10cb1188d40d3370e-Abstract.html | [
"Hyeonwoo Noh",
"Tackgeun You",
"Jonghwan Mun",
"Bohyung Han"
] | null | null | Overfitting is one of the most critical challenges in deep neural networks, and there are various types of regularization methods to improve generalization performance. Injecting noises to hidden units during training, e.g., dropout, is known as a successful regularizer, but it is still not clear enough why such traini... | [] | null | 87 | 1710.05179 | title_snapshot |
21c5bba1dd6aed9ab48c2b34c1a0adde | Few-Shot Adversarial Domain Adaptation | https://proceedings.neurips.cc/paper_files/paper/2017/hash/21c5bba1dd6aed9ab48c2b34c1a0adde-Abstract.html | [
"Saeid Motiian",
"Quinn Jones",
"Seyed Iranmanesh",
"Gianfranco Doretto"
] | null | null | This work provides a framework for addressing the problem of supervised domain adaptation with deep models. The main idea is to exploit adversarial learning to learn an embedded subspace that simultaneously maximizes the confusion between two domains while semantically aligning their embedding. The supervised setting b... | [] | null | 88 | 1711.02536 | title_snapshot |
2227d753dc18505031869d44673728e2 | Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes | https://proceedings.neurips.cc/paper_files/paper/2017/hash/2227d753dc18505031869d44673728e2-Abstract.html | [
"Taylor W Killian",
"Samuel Daulton",
"George Konidaris",
"Finale Doshi-Velez"
] | null | null | We introduce a new formulation of the Hidden Parameter Markov Decision Process (HiP-MDP), a framework for modeling families of related tasks using low-dimensional latent embeddings. Our new framework correctly models the joint uncertainty in the latent parameters and the state space. We also replace the original Gaussi... | [] | null | 89 | 1706.06544 | title_snapshot |
227f6afd3b7f89b96c4bb91f95d50f6d | Multi-View Decision Processes: The Helper-AI Problem | https://proceedings.neurips.cc/paper_files/paper/2017/hash/227f6afd3b7f89b96c4bb91f95d50f6d-Abstract.html | [
"Christos Dimitrakakis",
"David C. Parkes",
"Goran Radanovic",
"Paul Tylkin"
] | null | null | We consider a two-player sequential game in which agents have the same reward function but may disagree on the transition probabilities of an underlying Markovian model of the world. By committing to play a specific policy, the agent with the correct model can steer the behavior of the other agent, and seek to improve ... | [] | null | 90 | null | null |
2288f691b58edecadcc9a8691762b4fd | Maximum Margin Interval Trees | https://proceedings.neurips.cc/paper_files/paper/2017/hash/2288f691b58edecadcc9a8691762b4fd-Abstract.html | [
"Alexandre Drouin",
"Toby Hocking",
"Francois Laviolette"
] | null | null | Learning a regression function using censored or interval-valued output data is an important problem in fields such as genomics and medicine. The goal is to learn a real-valued prediction function, and the training output labels indicate an interval of possible values. Whereas most existing algorithms for this task are... | [] | null | 91 | 1710.04234 | title_snapshot |
22b1f2e0983160db6f7bb9f62f4dbb39 | Online Learning with a Hint | https://proceedings.neurips.cc/paper_files/paper/2017/hash/22b1f2e0983160db6f7bb9f62f4dbb39-Abstract.html | [
"Ofer Dekel",
"arthur flajolet",
"Nika Haghtalab",
"Patrick Jaillet"
] | null | null | We study a variant of online linear optimization where the player receives a hint about the loss function at the beginning of each round. The hint is given in the form of a vector that is weakly correlated with the loss vector on that round. We show that the player can benefit from such a hint if the set of feasible ac... | [] | null | 92 | null | null |
22fb0cee7e1f3bde58293de743871417 | DPSCREEN: Dynamic Personalized Screening | https://proceedings.neurips.cc/paper_files/paper/2017/hash/22fb0cee7e1f3bde58293de743871417-Abstract.html | [
"Kartik Ahuja",
"William Zame",
"Mihaela van der Schaar"
] | null | null | Screening is important for the diagnosis and treatment of a wide variety of diseases. A good screening policy should be personalized to the disease, to the features of the patient and to the dynamic history of the patient (including the history of screening). The growth of electronic health records data has led to the ... | [] | null | 93 | null | null |
23af4b45f1e166141a790d1a3126e77a | Online Learning of Optimal Bidding Strategy in Repeated Multi-Commodity Auctions | https://proceedings.neurips.cc/paper_files/paper/2017/hash/23af4b45f1e166141a790d1a3126e77a-Abstract.html | [
"M. Sevi Baltaoglu",
"Lang Tong",
"Qing Zhao"
] | null | null | We study the online learning problem of a bidder who participates in repeated auctions. With the goal of maximizing his T-period payoff, the bidder determines the optimal allocation of his budget among his bids for $K$ goods at each period. As a bidding strategy, we propose a polynomial-time algorithm, inspired by the ... | [] | null | 94 | 1703.02567 | title_snapshot |
2417dc8af8570f274e6775d4d60496da | A-NICE-MC: Adversarial Training for MCMC | https://proceedings.neurips.cc/paper_files/paper/2017/hash/2417dc8af8570f274e6775d4d60496da-Abstract.html | [
"Jiaming Song",
"Shengjia Zhao",
"Stefano Ermon"
] | null | null | Existing Markov Chain Monte Carlo (MCMC) methods are either based on general-purpose and domain-agnostic schemes, which can lead to slow convergence, or require hand-crafting of problem-specific proposals by an expert. We propose A-NICE-MC, a novel method to train flexible parametric Markov chain kernels to produce sam... | [] | null | 95 | 1706.07561 | title_snapshot |
24681928425f5a9133504de568f5f6df | Question Asking as Program Generation | https://proceedings.neurips.cc/paper_files/paper/2017/hash/24681928425f5a9133504de568f5f6df-Abstract.html | [
"Anselm Rothe",
"Brenden M Lake",
"Todd Gureckis"
] | null | null | A hallmark of human intelligence is the ability to ask rich, creative, and revealing questions. Here we introduce a cognitive model capable of constructing human-like questions. Our approach treats questions as formal programs that, when executed on the state of the world, output an answer. The model specifies a probab... | [] | null | 96 | 1711.06351 | title_snapshot |
24b43fb034a10d78bec71274033b4096 | Gradient Methods for Submodular Maximization | https://proceedings.neurips.cc/paper_files/paper/2017/hash/24b43fb034a10d78bec71274033b4096-Abstract.html | [
"Hamed Hassani",
"Mahdi Soltanolkotabi",
"Amin Karbasi"
] | null | null | In this paper, we study the problem of maximizing continuous submodular functions that naturally arise in many learning applications such as those involving utility functions in active learning and sensing, matrix approximations and network inference. Despite the apparent lack of convexity in such functions, we prove t... | [] | null | 97 | 1708.03949 | title_snapshot |
250cf8b51c773f3f8dc8b4be867a9a02 | Recycling Privileged Learning and Distribution Matching for Fairness | https://proceedings.neurips.cc/paper_files/paper/2017/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html | [
"Novi Quadrianto",
"Viktoriia Sharmanska"
] | null | null | Equipping machine learning models with ethical and legal constraints is a serious issue; without this, the future of machine learning is at risk. This paper takes a step forward in this direction and focuses on ensuring machine learning models deliver fair decisions. In legal scholarships, the notion of fairness itself... | [] | null | 98 | null | null |
253614bbac999b38b5b60cae531c4969 | Collecting Telemetry Data Privately | https://proceedings.neurips.cc/paper_files/paper/2017/hash/253614bbac999b38b5b60cae531c4969-Abstract.html | [
"Bolin Ding",
"Janardhan Kulkarni",
"Sergey Yekhanin"
] | null | null | The collection and analysis of telemetry data from user's devices is routinely performed by many software companies. Telemetry collection leads to improved user experience but poses significant risks to users' privacy. Locally differentially private (LDP) algorithms have recently emerged as the main tool that allows da... | [] | null | 99 | 1712.01524 | title_snapshot |
253f7b5d921338af34da817c00f42753 | Parallel Streaming Wasserstein Barycenters | https://proceedings.neurips.cc/paper_files/paper/2017/hash/253f7b5d921338af34da817c00f42753-Abstract.html | [
"Matthew Staib",
"Sebastian Claici",
"Justin M Solomon",
"Stefanie Jegelka"
] | null | null | Efficiently aggregating data from different sources is a challenging problem, particularly when samples from each source are distributed differently. These differences can be inherent to the inference task or present for other reasons: sensors in a sensor network may be placed far apart, affecting their individual meas... | [] | null | 100 | 1705.07443 | title_snapshot |
End of preview. Expand in Data Studio
README.md exists but content is empty.
- Downloads last month
- 29