Note: I'm updating this gist as I encounter new reviews, so make sure you're reading the latest revision!
Just as the previous year I collected (and keep doing so) links to various summaries and takeaways from this year's NIPS.
- NIPS 2016 Symposium on People and machines: Public views on machine learning, and what this means for machine learning researchers. (Notes and panel discussion) by /u/gcr
- NIPS 2016 summary, wrap up, and links to slides by /u/beamsearch
- Post NIPS Reflections by Neil Lawrence
- Some general take aways from #NIPS2016 by Igor Carron
- NIPS 2016 experience and highlights by Sergey Korolev
- NIPS 2016 Reflections by Paul Mineiro
- Some general takeaways from #NIPS2016 by Arturo Slim
- Ross Fadely and Jeremy Karnowski:
- NIPS 2016 — Day 1 Highlights
- NIPS 2016 — Day 2 Highlights: Platform wars, RL and RNNs
- NIPS 2016 — Day 3 Highlights: Robots that know, Cars that see, and more!
- NIPS 2016 — Final Highlights Days 4–6: Likelihood-free inference, Dessert analogies, and much more.
- Key deep learning takeaways from NIPS2016 for applied data scientist by Avkash Chauhan
- Brad Neuberg’s NIPS 2016 Notes by Brad Neuberg
You might also be interested in:
- All Code Implementations for NIPS 2016 papers
- On RocketAI: [1] + [2] + [3]
UPD [14 Dec]:
- 50 things I learned at NIPS 2016
- Lab 41 by Karl Ni:
- NIPS 2016 Review, Days 0 & 1
- NIPS 2016 Review, Day 2
- NIPS 2016 Review, Day 3
- WiML 2016 (Women in Machine Learning) videos:
- Designing Algorithms for Practical Machine Learning by Maya Gupta
- On the Expressive Power of Deep Neural Networks by Maithra Raghu
- Ancestral Causal Inference by Sara Magliacane
- Towards a Reasoning Engine for Individualizing Healthcare by Suchi Saria
- Learning Representations from Time Series Data through Contextualized LSTMs by Madalina Fiterau
- Towards Conversational Recommender Systems by Konstantina Christakopoulou
- Large-Scale Machine Learning through Spectral Methods: Theory & Practice by Anima Anandkumar
- WiML Updates by Tamara Broderick
- Using Convolutional Neural Networks to Estimate Population Density from High Resolution Satellite Images by Amy Zhang
- Graphons and Machine Learning by @JenniferChayes
- NIPS 2016: cake, Rocket AI, GANs and the style transfer debate by Luba Elliott
- Summary of NIPS 2016 Adversarial Training Workshop: More Theory, Exciting Progress by /u/fhuszar
- NIPS 2016 Notes by /u/lehinevych
UPD [16 Dec]:
- NIPS 2016 by Stephanie Hyland
- Nuts and Bolts of Building Deep Learning Applications: Ng @ NIPS2016 by Tomasz Malisiewicz
- NIPS 2016 by Roman Shapovalov
- Magenta wins "Best Demo" at NIPS 2016!, checkout the demo here
- Dialogue Workshop Recap by Paul Mineiro
- NIPS 2016: Towards an end to end Dynamic Dialogue System by Vishal Bhalla
- NIPS 2016: Deep Reinforcement Learning by Leighton Turner
- NIPS 2016 Highlights by Sebastian Ruder
- NIPS 2016 Notes by Valentine Svensson
- Some Interesting Talks at NIPS 2016 by Yen-Huan Li
UPD [21 Dec]:
- Deezer R&D goes to NIPS 2016
- Robots Learning About Human Values, Emotion, and Intent by Malika Cantor
- Notes on NIPS 2016 by Ilya Kuzovkin
- NIPS 2016 Trip Report by Mike Lanzetta
- Highlights of NIPS 2016: Adversarial learning, Meta-learning, and more by Sebastian Ruder
UPD [29 Dec]:
- Garbled highlights from NIPS 2016 by Dan Mackinlay
- AI Safety Highlights from NIPS 2016 by Victoria Krakovna
UPD [3 Jan]: