NIPS Accepted Papers Stats

Robbie Allen
Dec 5, 2017 · 9 min read
Conference Attendance by the AI Index

NIPS doesn’t make it easy

VAE Learning via Stein Variational Gradient Descent
Yuchen Pu (Duke University) · zhe Gan (duke) · Ricardo Henao (Duke University) · Chunyuan Li (Duke University) · Shaobo Han (Duke University) · Lawrence Carin (Duke University)
Efficient Use of Limited-Memory Resources to Accelerate Linear Learning
Celestine Dünner (IBM Research) · Thomas Parnell (IBM Research) · Martin Jaggi (EPFL)
Temporal Coherency based Criteria for Predicting Video Frames using Deep Multi-stage Generative Adversarial Networks
Prateep Bhattacharjee (Indian Institute of Technology Madras) · Sukhendu Das (IIT Madras)
Snippet of Accepted Papers at NIPS 2017

Changes between September and December

Now for the good stuff

Author stats

Total papers:1. lawrence carin (duke university): 10
2. alexander schwing (university of illinois at urbana-champaign): 6
3. nicolas heess (deepmind): 5
3. michael jordan (university of california, berkeley): 5
3. andreas krause (eth zurich): 5
3. razvan pascanu (deepmind): 5
3. le song (georgia institute of technology): 5
8. 22 tied with 4
Last-author:1. lawrence carin (duke university): 7
2. david blei (columbia university): 4
2. volkan cevher (epfl): 4
2. yoshua bengio (université de montréal): 4
5. 31 tied with 3
First-author:1. arya mazumdar (university of massachusetts amherst): 3
1. eric balkanski (harvard university): 3
1. simon du (carnegie mellon university): 3
23 tied with 2

Institution stats

Total papers:1. google: 60 (8.8%)
2. carnegie mellon university: 48 (7.1%)
3. massachusetts institute of technology: 43 (6.3%)
4. microsoft: 40 (5.9%)
5. stanford university: 39 (5.7%)
6. university of california, berkeley: 35 (5.2%)
7. deepmind: 31 (4.6%)
8. university of oxford: 22 (3.2%)
9. university of illinois at urbana-champaign: 20 (2.9%)
10. georgia institute of technology: 18 (2.7%)
11. princeton: 17 (2.5%)
11. eth zurich: 17 (2.5%)
13. ibm: 16 (2.4%)
14. inria: 15 (2.2%)
14. harvard university: 15 (2.2%)
15. cornell university: 15 (2.2%)
17. duke university: 14 (2.1%)
17. columbia university: 14 (2.1%)
17. university of cambridge: 14 (2.1%)
17. epfl: 14 (2.1%)
21. university of michigan: 13 (1.9%)
22. university of toronto: 12 (1.8%)
22. university of southern california: 12 (1.8%)
22. tsinghua university: 12 (1.8%)
25. facebook: 11 (1.6%)
25. riken: 11 (1.6%)
27. university of washington: 10 (1.5%)
27. university of california, los angeles: 10 (1.5%)
27. university of texas at austin: 10 (1.5%)
30. new york university: 10 (1.5%)
30. university college london: 10 (1.5%)
32. université de montréal: 9 (1.3%)
32. tencent ai lab: 9 (1.3%)
34. openai: 8 (1.2%)
34. adobe: 8 (1.2%)
34. university of california, san diego: 8 (1.2%)
37. university of tokyo: 7 (1.0%)
37. university of pittsburgh: 7 (1.0%)
37. peking university: 7 (1.0%)
37. university of minnesota: 7 (1.0%)
41. university of california, davis: 6 (0.9%)
41. technion: 6 (0.9%)
41. university of pennsylvania: 6 (0.9%)
41. nanjing university: 6 (0.9%)
41. johns hopkins university: 6 (0.9%)
41. university of wisconsin-madison: 6 (0.9%)
47. australian national university: 5 (0.7%)
47. tel aviv university: 5 (0.7%)
47. ohio state university: 5 (0.7%)
57. national university of singapore: 5 (0.7%)
Total first-author papers:1. carnegie mellon university: 36
2. massachusetts institute of technology: 30
3. stanford university: 25
4. google: 24
5. university of california, berkeley: 21
6. duke university: 14
7. deepmind: 14
8. eth zurich: 13
9. microsoft: 12
10. harvard university: 11
Total institution authors:1. carnegie mellon university: 89
2. google: 78
3. massachusetts institute of technology: 69
4. deepmind: 68
5. stanford university: 66
6. university of california, berkeley: 60
7. microsoft: 59
8. eth zurich: 31
9. university of oxford: 29
10. duke university: 28
11. princeton: 28

Summary

A note on institution names

'google brain resident': 'google',
'google brain': 'google',
'google inc': 'google',
'google inc.':'google',
'google research nyc': 'google',
'google research': 'google',
'google, inc.': 'google’,
'deepmind @ google': 'deepmind',
'deepmind technologies': 'deepmind',
'google deepmind': 'deepmind’,
'ibm research - china':'ibm',
'ibm research':'ibm',
'ibm research, ny':'ibm',
'ibm research, usa':'ibm',
'ibm t. j. watson research center':'ibm',
'ibm t. j. watson research':'ibm',
'ibm t.j watson research center':'ibm',
'ibm t.j. watson research center':'ibm',
'ibm t.j.watson research center':'ibm',
'ibm thomas j. watson research center':'ibm',
'ibm tj watson research center':'ibm',
'microsoft research cambridge':'microsoft',
'microsoft research india':'microsoft',
'microsoft research maluuba':'microsoft',
'microsoft research new england':'microsoft',
'microsoft research':'microsoft',
'microsoft research, redmond, w':'microsoft',
'microsoft research, redmond, wa':'microsoft',
'miicrosoft research':'microsoft',
'university of wisconsin - madison': 'university of wisconsin-madison',
'university of wisconsin madison': 'university of wisconsin-madison',
'university of wisconsin': 'university of wisconsin-madison',
'university of wisconsin, madison': 'university of wisconsin-madison',
'university of wisconsion-madison': 'university of wisconsin-madison',
'uw-madison': 'university of wisconsion-madison’,

Machine Learning in Practice

Practical insights for executives, managers, and project managers eager to deploy machine learning inside their company.

Robbie Allen

Written by

CEO @InfiniaML, Exec Chairman @Ainsights, Lecturer at @kenanflagler, Ph.D. Student @UNCCS, Writing a book: http://machinelearninginpractice.com

Machine Learning in Practice

Practical insights for executives, managers, and project managers eager to deploy machine learning inside their company.