NeurIPS
Collection
Accepted papers for NeurIPS (Conference on Neural Information Processing Systems), one dataset per year. • 13 items • Updated
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 | embedding list |
|---|---|---|---|---|---|---|---|---|---|---|---|
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 | [
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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 | [
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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 | [
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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 | [
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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 | [
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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 | [
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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 | [
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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 | [
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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 | [
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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 | [
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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 | [
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0... |
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