---
title: "NeurIPS 2016 — accepted papers — Elamur Research"
description: "NeurIPS 2016 — accepted papers: 1 paper in the Elamur Research library, with our summaries, ranked by citations."
canonical: "https://research.elamur.ai/free/en/venues/neural-information-processing-systems/2016/"
---
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# NeurIPS 2016 — accepted papers

NeurIPS 2016 is the 2016 edition of the Conference on Neural Information Processing Systems (NeurIPS). OpenAlex lists 177 papers of it; our library holds 1 with our summaries. Below: the most cited papers, every accepted paper, and ours with their topics, authors and institutions.

[**177** papers in the proceedings](#all) · [**1** work in our library](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/2016/#works) · [**1,801** citations of these works](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/2016/#works)

## Most cited papers

By their OpenAlex citations; a paper with our summary links to its page here, the others to the original.

1. **[Improved deep metric learning with multi-class N-pair loss objective ↗](https://papers.nips.cc/paper/2016/file/6b180037abbebea991d8b1232f8a8ca9-Paper.pdf)**Kihyuk Sohn*1,240 citations*
2. **[Deep ADMM-Net for compressive sensing MRI ↗](https://papers.nips.cc/paper/6406-deep-admm-net-for-compressive-sensing-mri.pdf)**Yan Yang, Jian Zhong Sun, Huibin Li et al.*871 citations*
3. **[Improved Variational Inference with Inverse Autoregressive Flow ↗](https://papers.nips.cc/paper/2016/file/ddeebdeefdb7e7e7a697e1c3e3d8ef54-Paper.pdf)**Durk Kingma, Tim Salimans, Rafał Józefowicz et al.*724 citations*
4. **[Weight normalization: a simple reparameterization to accelerate training of deep neural networks ↗](https://papers.nips.cc/paper/6114-weight-normalization-a-simple-reparameterization-to-accelerate-training-of-deep-neural-networks.pdf)**Tim Salimans, Diederik P. Kingma*617 citations*
5. **[Unifying count-based exploration and intrinsic motivation ↗](https://proceedings.neurips.cc/paper/2016/file/afda332245e2af431fb7b672a68b659d-Paper.pdf)**Marc G. Bellemare, Sriram Srinivasan, Georg Ostrovski et al.*612 citations*
6. **[Diffusion-Convolutional Neural Networks ↗](https://papers.nips.cc/paper/2016/file/390e982518a50e280d8e2b535462ec1f-Paper.pdf)**James R. Atwood, Don Towsley*517 citations*
7. **[Examples are not enough, learn to criticize! Criticism for Interpretability ↗](https://papers.nips.cc/paper/6300-examples-are-not-enough-learn-to-criticize-criticism-for-interpretability.pdf)**Been Kim, Rajiv Khanna, Sanmi Koyejo*499 citations*
8. **[MS MARCO: A Human Generated MAchine Reading COmprehension Dataset. ↗](http://ceur-ws.org/Vol-1773/CoCoNIPS_2016_paper9.pdf)**Tri Gia Nguyen, Mir Rosenberg, Song Xia et al.*419 citations*
9. **[Dynamic Filter Networks ↗](https://papers.nips.cc/paper/6578-dynamic-filter-networks.pdf)**Xu Jia, Bert De Brabandere, Tinne Tuytelaars et al.*315 citations*
10. **[Temporal regularized matrix factorization for high-dimensional time series prediction ↗](https://papers.nips.cc/paper/6160-temporal-regularized-matrix-factorization-for-high-dimensional-time-series-prediction.pdf)**Hsiang‐Fu Yu, Nikhil Rao, Inderjit S. Dhillon*306 citations*

## All accepted papers (177)

The most cited first; 1 with our summary link to their page here, the others to the original.

1. **[Improved deep metric learning with multi-class N-pair loss objective ↗](https://papers.nips.cc/paper/2016/file/6b180037abbebea991d8b1232f8a8ca9-Paper.pdf)**Kihyuk Sohn*1,240 citations*
2. **[Deep ADMM-Net for compressive sensing MRI ↗](https://papers.nips.cc/paper/6406-deep-admm-net-for-compressive-sensing-mri.pdf)**Yan Yang, Jian Zhong Sun, Huibin Li et al.*871 citations*
3. **[Improved Variational Inference with Inverse Autoregressive Flow ↗](https://papers.nips.cc/paper/2016/file/ddeebdeefdb7e7e7a697e1c3e3d8ef54-Paper.pdf)**Durk Kingma, Tim Salimans, Rafał Józefowicz et al.*724 citations*
4. **[Weight normalization: a simple reparameterization to accelerate training of deep neural networks ↗](https://papers.nips.cc/paper/6114-weight-normalization-a-simple-reparameterization-to-accelerate-training-of-deep-neural-networks.pdf)**Tim Salimans, Diederik P. Kingma*617 citations*
5. **[Unifying count-based exploration and intrinsic motivation ↗](https://proceedings.neurips.cc/paper/2016/file/afda332245e2af431fb7b672a68b659d-Paper.pdf)**Marc G. Bellemare, Sriram Srinivasan, Georg Ostrovski et al.*612 citations*
6. **[Diffusion-Convolutional Neural Networks ↗](https://papers.nips.cc/paper/2016/file/390e982518a50e280d8e2b535462ec1f-Paper.pdf)**James R. Atwood, Don Towsley*517 citations*
7. **[Examples are not enough, learn to criticize! Criticism for Interpretability ↗](https://papers.nips.cc/paper/6300-examples-are-not-enough-learn-to-criticize-criticism-for-interpretability.pdf)**Been Kim, Rajiv Khanna, Sanmi Koyejo*499 citations*
8. **[MS MARCO: A Human Generated MAchine Reading COmprehension Dataset. ↗](http://ceur-ws.org/Vol-1773/CoCoNIPS_2016_paper9.pdf)**Tri Gia Nguyen, Mir Rosenberg, Song Xia et al.*419 citations*
9. **[Dynamic Filter Networks ↗](https://papers.nips.cc/paper/6578-dynamic-filter-networks.pdf)**Xu Jia, Bert De Brabandere, Tinne Tuytelaars et al.*315 citations*
10. **[Temporal regularized matrix factorization for high-dimensional time series prediction ↗](https://papers.nips.cc/paper/6160-temporal-regularized-matrix-factorization-for-high-dimensional-time-series-prediction.pdf)**Hsiang‐Fu Yu, Nikhil Rao, Inderjit S. Dhillon*306 citations*
11. **[Residual networks behave like ensembles of relatively shallow networks ↗](https://papers.nips.cc/paper/6556-residual-networks-behave-like-ensembles-of-relatively-shallow-networks.pdf)**Andreas Veit, Michael J. Wilber, Serge Belongie*300 citations*
12. **[Regularization with stochastic transformations and perturbations for deep semi-supervised learning ↗](https://papers.nips.cc/paper/2016/file/30ef30b64204a3088a26bc2e6ecf7602-Paper.pdf)**Mehdi S. M. Sajjadi, Mehran Javanmardi, Tolga Taşdizen*262 citations*
13. **[Learning to poke by poking: experiential learning of intuitive physics ↗](https://proceedings.neurips.cc/paper/2016/file/c203d8a151612acf12457e4d67635a95-Paper.pdf)**Pulkit Agrawal, Ashvin Nair, Pieter Abbeel et al.*254 citations*
14. **[Bayesian optimization with robust Bayesian neural networks ↗](https://papers.nips.cc/paper/2016/file/a96d3afec184766bfeca7a9f989fc7e7-Paper.pdf)**Jost Tobias Springenberg, Aaron Klein, Stefan Falkner et al.*252 citations*
15. **[Learning Transferrable Representations for Unsupervised Domain Adaptation ↗](https://papers.nips.cc/paper/6360-learning-transferrable-representations-for-unsupervised-domain-adaptation.pdf)**Ozan Şener, Hyun Oh Song, Ashutosh Saxena et al.*226 citations*
16. **[Supervised Learning with Tensor Networks ↗](https://papers.nips.cc/paper/6211-supervised-learning-with-tensor-networks.pdf)**Edwin Miles Stoudenmire, David J. Schwab*188 citations*
17. **[Composing graphical models with neural networks for structured representations and fast inference ↗](https://papers.nips.cc/paper/2016/file/7d6044e95a16761171b130dcb476a43e-Paper.pdf)**Matthew Johnson, David Duvenaud, Alexander B. Wiltschko et al.*165 citations*
18. **[30th Conference on Neural Information Processing Systems (NIPS 2016) ↗](https://www.research.ed.ac.uk/portal/files/28416442/nips2016_combined_2.pdf)**George Papamakarios, Iain Murray*164 citations*
19. **[Learning feed-forward one-shot learners ↗](https://papers.nips.cc/paper/2016/file/839ab46820b524afda05122893c2fe8e-Paper.pdf)**Luca Bertinetto, João F. Henriques, Jack Valmadre et al.*163 citations*
20. **[CNNpack: packing convolutional neural networks in the frequency domain ↗](https://papers.nips.cc/paper/6390-cnnpack-packing-convolutional-neural-networks-in-the-frequency-domain.pdf)**Yunhe Wang, Chang Xu, Shan You et al.*144 citations*
21. **[Stochastic Gradient Methods for Distributionally Robust Optimization with f-divergences ↗](https://papers.nips.cc/paper/6040-stochastic-gradient-methods-for-distributionally-robust-optimization-with-f-divergences.pdf)**Hongseok Namkoong, John C. Duchi*131 citations*
22. **[Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation ↗](https://papers.nips.cc/paper/2016/file/4dcf435435894a4d0972046fc566af76-Paper.pdf)**Jianxu Chen, Lin Yang, Yizhe Zhang et al.*113 citations*
23. **[Fairness in learning: classic and contextual bandits ↗](https://papers.nips.cc/paper/6355-fairness-in-learning-classic-and-contextual-bandits.pdf)**Matthew Joseph, Michael J. Kearns, Jamie Morgenstern et al.*95 citations*
24. **[Proximal stochastic methods for nonsmooth nonconvex finite-sum optimization ↗](https://papers.nips.cc/paper/6116-proximal-stochastic-methods-for-nonsmooth-nonconvex-finite-sum-optimization.pdf)**Sashank Jakkam Reddi, Suvrit Sra, Barnabás Póczos et al.*95 citations*
25. **[Towards Conceptual Compression ↗](https://papers.nips.cc/paper/6542-towards-conceptual-compression.pdf)**Karol Gregor, Frederic Besse, Danilo Jimenez Rezende et al.*95 citations*

[See all 177 papers in NeurIPS 2016 — accepted papers →](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/2016/papers/)

## Papers with our summary in our library (1)

The most cited first (OpenAlex citations plus our library's).

1. **[Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://md.research.elamur.ai/free/en/papers/defferrard-2016-convolutional-neural-networks-graphs-fast/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Michael Defferrard, Xavier Bresson, Pierre Vandergheynst*2016 · 1,801 citations*

## Fields

[Computer Science (1)](https://md.research.elamur.ai/free/en/shelves/computer-science/)

## Top authors

Authors with a page who published here most often: their works here, and the citations of those works.

[🧑‍🔬**Xavier Bresson**1 work here · 1,801 citations](https://md.research.elamur.ai/free/en/authors/xavier-bresson/)

## Top institutions

Institutions on the bylines of its works here, by the number of works.

[🎓**École Polytechnique Fédérale de Lausanne** Lausanne · Switzerland 1 work here](https://md.research.elamur.ai/free/en/universities/ecole-polytechnique-federale-de-lausanne/)

works and citations from OpenAlex and our library.

- [← previous NeurIPS 2013](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/2013/)
- [next → NeurIPS 2020](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/2020/)
- [↑ back to NeurIPS](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)