Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. They are proceedings from the conference, "Neural Information Processing Systems MitchellDan RothEric P. Simon S. DuWei HuJason D. WangXiang LiCharles X. Risk bounds for classification and regression rules that interpolate Mikhail BelkinDaniel J.
WarmuthDaniel J. ChenXuechen LiRoger B. GrosseDavid K. Duvenaud Contextual bandits with surrogate losses: Margin bounds and efficient algorithms Dylan J.
CrowleyGavin GrayAmos J. Irene ChenFredrik D. FriesenPedro M. LeeR. BuiShengjia ZhaoMykel J.
LiuRong GeMichael I. DiCarloDaniel L. DattaJonathan W. HalloranDavid M. LedsamKlaus Maier-HeinS. BartlettMichael I. GordonByron BootsJ. KakadeJason D. TemmeJonas RauberHeiko H. WeinbergerDavid BindelAndrew G. GomesBart SelmanKilian Q. Fei-FeiDaniel L. MouraJoao P. CosteiraGeoffrey J. VetrovAndrew G. CohenRuss R.We invite submissions for the Thirty-Fourth Annual Conference on Neural Information Processing Systems NeurIPSa multi-track, interdisciplinary conference that brings together researchers in machine learning, computational neuroscience, and their applications.
Subject areas are listed below in brief, and in full here. Social Aspects of ML: AI Safety; Fairness and Accountability; Privacy Significant changes to the reviewing process follow; please read carefully and watch the accompanying video: There is a mandatory abstract submission deadline on May 05,one week before full submissions are due. It will not be possible to modify the author list nor the author order after the abstract submission deadline.
All authors are required to register in CMT and fill out a reviewer profile form by May 14, Because of the rapid growth of NeurIPS, all authors and co-authors are expected to be available to review papers, if asked to do so. If all co-authors do not register and enter their information, their submission may be desk rejected.
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Authors need to declare if a previous version of their submission was rejected at any peer-reviewed venue within the past 12 months, and, if so, summarize the changes to the current version.
This information should be entered into CMT during the submission process. In order to provide a balanced perspective, authors are required to include a statement of the potential broader impact of their work, including its ethical aspects and future societal consequences. Authors should take care to discuss both positive and negative outcomes.
Authors are required to provide an explicit disclosure of funding financial activities supporting the submitted work and competing interests related financial activities outside the submitted work that could result in conflicts of interest. This section should be added to the camera-ready version of accepted papers. More information can be found here.
We strongly encourage but do not require accompanying code and data to be submitted with accepted papers that contribute and present experiments with a new algorithm or new dataset.
Moreover, we encourage authors to upload their code as part of their supplementary material at submission time in order to help reviewers assess the quality of the work. See the policy here. As an additional step to make NeurIPS content accessible to those unable to attend the conference, authors of accepted submissions will be required to provide a link to all the following accompanying materials by the camera ready deadline: 3-minute video summarizing the paper PDF of slides summarizing the paper PDF of the poster used at the conference Authors will be asked to confirm that their submissions accord with the NeurIPS code of conduct.
Formatting instructions: All submissions must be in PDF format. Submissions are limited to eight content pages, including all figures and tables; additional pages containing a statement of broader impact, acknowledgements and funding disclosures, and references are allowed. The maximum file size for submissions is 50MB. Submissions that violate the NeurIPS style e.
If your submission is accepted, you will be allowed a ninth content page for the camera-ready version. Supplementary material: Authors may submit up to MB of supplementary material, such as proofs, derivations, data, or source code; all supplementary materials must be in PDF or ZIP format.
Supplementary material should be material, created by the authors, that directly supports the submission content. Like submissions, supplementary material must be anonymized. To submit supplementary material, first upload your submission.NeurIPS with participants, accepted papers out of submissions58 workshops, and 16K pages of proceedings, was the most overwhelming yet one of the most fruitful conferences I ever attended. Considering the size of the conference, it was practically impossible to cover all the tracks, talks, and posters.
In this post and the follow-upsI will provide some featured highlights of the conference from my personal point of view.
Yoshua provided a fantastic insight into the current state of deep learning and its future. He discussed Consciousness and Attention as its key ingredient as the basics of System 2with sparse factor graphs as consciousness priorand meta-learning as a theoretical framework to expand deep learning from System 1 to System 2.
Deep Learning with Bayesian Principles Slides : Deep learning and Bayesian learning are considered two entirely different fields often used in complementary settings. Emtiyaz Khan in his talk introduces modern Bayesian principles to bridge this gap to solve challenging real-world problems by combining their strengths. We can use Bayesian principles as general principles to design, improve, and generalize a range of learning algorithms by computing posterior approximations.
We can derive existing algorithms e. Adam optimizer as special cases, or design new deep learning algorithms for uncertainty estimation, generalization on small datasets, and life-long learning. Training Bayesian neural networks in particular posterior approximation is still a challenging and computationally expensive problem. Hence approximation methods such as Variational Inference VI can be used. A PyTorch implementation is also available as a plug-and-play optimizer.
The following figure shows a word cloud of most frequent tokens words for the titles of all accepted paper to NeurIPS after some pre-processing e.
The code to obtain the titles and token frequencies can be found on the Github. I ran some further analysis on the following keywords: Optimization, Reinforcement Learning, Adversarial, Graph, Generative, and Bayesian Neural Network code can be found hereand found some interesting results:. From my point of view based on the number of attendees to the sessions and the above datathe following topics were trending in the conference:.
A good list of GNN papers is also collected in this repo. Graphs not only can be used as a data structure, but also can represent outputs of NNs, e. The outstanding new directions paper award went to Uniform convergence may be unable to explain generalization in deep learning by Vaishnavh Nagarajan and J. Zico Kolter slidesblogcode. One of the biggest open challenges in deep learning theory is the generalization puzzle, as deep network models in contrary to classical learning theory generalize very well in spite of heavy overparameterization.
This work takes a step back and argues that pursuing uniform convergence-based bounds may not actually lead us to the complete solution of this puzzle. In particular, it shows 1 Theoretical generalization bounds grow with training set size while the empirical gap decreasesand 2 any kind of uniform convergence bounds will provably fail to explain generalization in certain situations in deep learning.
Amongst 54 workshops during 3 days, these were the most popular ones among participants:. NeurIPS had an emphasis on diversity and inclusion. There were 15 official social meetups to bring people with common interests together. For example, there were more than meetups organize by and almost a countless number of topics discussed by participants!!
It was super exciting to see and have a conversation with some of the AI celebrities, as they were very welcoming and humble :. Sign in. NeurIPS Highlights. A summary of what I learned in the biggest machine learning conference. Alireza Dirafzoon Follow. Towards Data Science A Medium publication sharing concepts, ideas, and codes.
Towards Data Science Follow. A Medium publication sharing concepts, ideas, and codes. Write the first response. More From Medium. More from Towards Data Science.Toggle navigation. All submissions must be in PDF format. The maximum file size for submissions is 50MB.
Submissions that violate the NeurIPS style e. Please make sure that your paper prints well. Note that display math in bare TeX commands will not create correct line numbers for submission. You are encouraged to validate the formatting of your submission using the NeurIPS paper checker; please check that your submission validates well in advance of the deadline to avoid server congestion. Authors may submit up to MB of supplementary material, such as proofs, derivations, data, or source code; all supplementary material must be in PDF or ZIP format.
The reviewing process will be double blind at the level of reviewers and area chairs i. As an author, you are responsible for anonymizing your submission. In particular, you should not include author names, author affiliations, or acknowledgements in your submission and you should avoid providing any other identifying information even in the supplementary material.
If you need to cite one of your own papers, you should do so with adequate anonymization to preserve double-blind reviewing e. If you need to cite one of your own papers that is in submission to NeurIPS or elsewhere please do so with adequate anonymization and make sure the cited submission is available for reviewers to read e. Non-anonymous preprints on arXiv, social media, websites, etc.
All author responses must be in PDF format. Author responses are limited to one page, including all figures, tables, and references. Author responses must not contain external links. Toggle navigation Toggle navigation Login. Year Non-anonymous preprints Non-anonymous preprints on arXiv, social media, websites, etc.
Do not remove: This comment is monitored to verify that the site is working properly.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.
If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. A curated list of gradient and adaptive boosting papers with implementations from the following conferences:. How to Make AdaBoost. Analysis of the Performance of AdaBoost. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.
Sign up. A curated list of gradient boosting research papers with implementations. Python Branch: master. Find file. Sign in Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again. Latest commit Fetching latest commit…. Awesome Gradient Boosting Research Papers.
Ash, John Langford, Robert E. Miller, Nicholas J. Foti, Ryan P. Canuto, Thiago Salles, Clebson C. Saberian, David J. Weinberger, Alice X.Accepting papers out of submissions might have made Neural Information Processing Systems Conference overwhelm. But it was the presence of 13, AI researchers at the Vancouver Convention Center which was mind-numbing.
It acts as an interpretable layer while still achieving performance results at par with the SOTA deep learning models. Yes, we have heard this being talked about quite often.
It was visible how the research community and NeurIPS have responded to the claims. Reproducible is being taken seriouslyatleast it has started to. NeurIPS, for the first time, has organized Reproducibility challengeencouraging institutions to use the accepted papers via OpenReview.
ML models are known to be unfair so far. There can be racial biases, gender biases and other such biases percolating into the models leading to disastrous consequences. NeurIPS witnessed lot of research in this domain. Few of interesting ones:. Celeste Kidd, talked about How to know on the opening day. It got a great reception from the audience. While highlighting the issue of sexual harassment in the wake of metoo movement, her keynote speech struck a chord with everyone in the conference.
For the purpose of demonstration, they took paper title as input instead of entire paper. It might give sleepless nights for software developers, engineers and CS folks in general.
It would be interesting to see how it evolves. User could ask questions about contents inside the fridge, number of items, freshness of items via Facebook Messenger interface. Despite being a demo, I was expecting real-world data and richer experience. But the DNN model was trained on specific set of images and the dataset was restrictive. It can be visualized as a point-cloud. It later creates planned paths and executes them. Learning Machines can Curl — Adaptive Deep Reinforcement Learning enables the robot Curly to win against human players in an icy world.
Deep learning is defeating champions not just in games such as Go, Chess but now it makes a foray into Olympic sports. The sporting world is paused for a revolution. Friday and Saturday was a highly parallel problem. Unfortunately my single threaded engine proved to be a bottleneck.
How To Write A Top ML Paper: A Checklist From NeurIPS
Uniform convergence may be unable to explain generalization in deep learning. To make sense of the award-winning papers, I found this article to be helpful. All in all, it was a wonderful action-packed 7 days at Vancouver.
Without getting into the hype cycle, I honestly feel, this is just the beginning of the ride. What an exciting time to be alive! Sign in. NeurIPS Chaitanya Prakash Bapat Follow. Fairness ML models are known to be unfair so far.Thousands of machine learning papers get published every week. It is almost impossible to find the most useful paper in this vast and growing list. Usually, these conferences act as platforms to promote research.
The acceptance guidelines for these top conferences vary, but they all are stringent nevertheless. The reviewers who skim through papers have thumb rules, such as the availability of code, replicability of results, etc. However, every year, few unlucky papers — that are seemingly good — get discarded. This may be because the reviewers burden themselves with papers, which are nothing but a misguided, misleading clutch of text to inflate publication count.
The ML Code Completeness Checklist assesses a code repository based on the scripts and artefacts that have been provided within it. It checks a code repository for:. This renewed interest around replicability of results was kickstarted when the organizers of NeurIPS introduced new policies into their paper submission guidelines to establish an ecosystem that encourages ML researchers to volunteer for reproducibility of claimed results.Unsupervised Deep Learning - Google DeepMind & Facebook Artificial Intelligence NeurIPS 2018
One thing common with all top papers is the availability of complete code, and the goal of the checklist mentioned above is to enhance reproducibility and promote best practices and code repository assessments, so that the future work need not be built from scratch every time.
The above plots are a comparison of reproducibility in papers for the year at NeurIPS. These results again resonate with the idea of promoting reproducibility in the ML community, and the recommendations of papers with code have been spot on. Papers with Code for the last couple of years has been presenting the community with a curated list of papers that have code and beat the benchmark.
It is a free community-driven resource and it has recently joined Facebook AI. There is little doubt now that reproducibility is an essential characteristic for any scientific community.
However, in the case of machine learning, achieving this is not so straightforward because of their black box nature of producing results. Added to this, there is also an overwhelming hype around AI, which can nudge the researchers into inflating the results for various personal reasons.
While the efforts to establish reproducibility have gained traction, PyTorch creator Soumith Chintala urged the community to go one step further and introduce initiatives that would incentivize the researchers to add understandable code to their papers. Research reproducibility is mainly a cultural challenge for our community. A decade ago, people didn't publish code. Now it's embarrassing not to. The next step is to push people to publish understandable code.