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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
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 | [
0.02058214507997036,
-0.006060718093067408,
0.032200686633586884,
0.010825474746525288,
0.007025424856692553,
0.02776307240128517,
0.0046479105949401855,
0.022008640691637993,
-0.042987555265426636,
-0.042293939739465714,
-0.04075806587934494,
0.01748029887676239,
-0.047465428709983826,
0.... |
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 | [
-0.028856877237558365,
-0.011326405219733715,
0.013674633577466011,
0.042164403945207596,
0.052022647112607956,
0.026074083521962166,
0.017294714227318764,
-0.03199140354990959,
0.010032943449914455,
-0.03892436996102333,
-0.029272504150867462,
0.02291705273091793,
-0.08774011582136154,
0.... |
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 | [
-0.001826958847232163,
-0.011842590756714344,
-0.004485223907977343,
0.030436299741268158,
0.03538673371076584,
0.02737569436430931,
0.009838543832302094,
-0.010291877202689648,
0.026403171941637993,
-0.039323385804891586,
0.0021791444160044193,
-0.02387409843504429,
-0.06122225895524025,
... |
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 | [
0.010916806757450104,
-0.03598533570766449,
-0.02625655196607113,
0.06249323859810829,
0.059007178992033005,
0.03687189519405365,
0.015314684249460697,
-0.019576173275709152,
-0.0205245241522789,
-0.06759639084339142,
-0.01577686332166195,
-0.04125043749809265,
-0.030228665098547935,
-0.01... |
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 | [
-0.02760612778365612,
0.026692280545830727,
-0.009921400807797909,
-0.003448270261287689,
0.060710757970809937,
0.032956983894109726,
0.06235614791512489,
0.018245941027998924,
-0.031476058065891266,
-0.03640519082546234,
-0.007354554254561663,
0.018407469615340233,
-0.0627746656537056,
-0... |
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 | [
0.007732246071100235,
-0.03492339327931404,
0.013300403952598572,
0.027704842388629913,
0.06283548474311829,
0.050803545862436295,
0.01922459900379181,
0.016140548512339592,
-0.031283143907785416,
-0.06073293089866638,
0.01325277704745531,
-0.009466341696679592,
-0.06439420580863953,
0.014... |
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 | [
0.003632055828347802,
0.0021275815088301897,
-0.006203006953001022,
0.0540350079536438,
0.05289163440465927,
0.03700743988156319,
0.02541225031018257,
-0.012493223883211613,
-0.020564444363117218,
-0.05335669592022896,
0.009113227017223835,
0.01670939102768898,
-0.0515315867960453,
0.00004... |
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 | [
-0.06705871969461441,
0.0028085007797926664,
0.008429826237261295,
0.018591389060020447,
0.049573514610528946,
0.04172327741980553,
0.00013721513096243143,
-0.023769665509462357,
-0.016287103295326233,
-0.03212722763419151,
-0.01505959127098322,
-0.026420289650559425,
-0.08147238194942474,
... |
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 | [
-0.019581733271479607,
-0.027954932302236557,
-0.01675308123230934,
0.05171036347746849,
0.026097644120454788,
0.0002597525017336011,
0.025832733139395714,
0.005939655005931854,
-0.030651366338133812,
0.005648090969771147,
-0.03137096017599106,
0.0399160273373127,
-0.05389147996902466,
-0.... |
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 | [
-0.029821030795574188,
-0.011500178836286068,
0.007936990819871426,
0.029366087168455124,
0.03135768324136734,
0.02866283990442753,
0.02677057683467865,
0.0225411057472229,
-0.007838202640414238,
-0.0278396625071764,
0.009167024865746498,
-0.0012740076053887606,
-0.07252741605043411,
-0.00... |
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 | [
0.010282007046043873,
0.012724803760647774,
0.0171970184892416,
0.02984268218278885,
0.017643941566348076,
0.013615220785140991,
0.01804278790950775,
0.03517058119177818,
-0.04765848070383072,
-0.044321704655885696,
-0.04889651760458946,
0.01188328955322504,
-0.06448562443256378,
-0.012743... |
End of preview. Expand in Data Studio
README.md exists but content is empty.
- Downloads last month
- 32