MLconf - The Machine Learning Conference

MLconf gathers the machine learning community to discuss recent trends, innovations, research and the application of algorithms, tools, best practices, and platforms within AI/ML.

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The MLconf Team

MLconf SF 2019 Speaker Resources

MLconf SF 2019 Speaker Resources:

Franziska Bell, Director of Data Science, Head of Platform Data Science, Uber

Uber’s Intelligent Insights Assistant

Mihajlo Grbovic, Principal Machine Learning Scientist, Airbnb

Machine Learning-Powered Search Ranking of Airbnb Experiences

Vito Ostuni, Pandora

‘The Voice’: New Challenges in a Zero UI world

Anoop Deoras, Researcher, Netflix

Building an Incrementally Trained, Local Taste Aware, Global Deep Learned Recommender System Model

Sneha Rajana, Software Development Engineer, Amazon

Deep Learning Architectures for Semantic Relation Detection Tasks

  • https://www.aclweb.org/anthology/S17-1002.pdf
  • https://github.com/srajana/AntNET
  • https://github.com/vered1986/LexNET
  • https://arxiv.org/pdf/1608.05014.pdf
  • https://arxiv.org/abs/1610.08694
  • https://arxiv.org/pdf/1906.05612.pdf
  • https://arxiv.org/pdf/1906.04706.pdf
  • https://arxiv.org/abs/1810.04805
  • https://arxiv.org/abs/1706.03762
  • https://www.aclweb.org/anthology/P19-1279.pdf
  • https://arxiv.org/pdf/1905.08284.pdf
  • https://arxiv.org/abs/1901.08163
  • https://www.aclweb.org/anthology/P16-1123/

June Andrews, AI Instruments, Stitch Fix

The Uncanny Valley of ML

Noam Finkelstein, PhD student, Johns Hopkins University

The Importance of Modeling Data Collection

Meghana Ravikumar, Platform Engineering Lead, SigOpt

Optimized Image Classification on the Cheap

Martin Isaksson, CEO and Co-founder, PerceptiLabs

How to enable warp speed for Machine Learning modeling

Ted Willke, Director of the Brain-Inspired Computing Lab, Intel

The Brain’s Guide to Dealing with Context in Language Understanding

Anitha Kannan & Xavier Amatriain, Founding Member & CTO, Curai

AI for healthcare: Scaling Access and Quality of Care for Everyone

Jekaterina Novikova, Director of Machine Learning, Winterlight Labs

Machine Learning Methods in Detecting Alzheimer’s Disease from Speech and Language

  • Z. Zhu, J. Novikova, and F. Rudzicz. Detecting cognitive impairments by agreeing on interpretations of linguistic features. In Proceedings of NAACL, 2019
  • J. Novikova, A. Balagopalan, K. Shkaruta and F. Rudzicz. Lexical Features Are More Vulnerable, Syntactic Features Have More Predictive Power. In: The 5th Workshop on Noisy User-generated Text at EMNLP, Hong Kong, 2019
  • Z. Zhu, J. Novikova, and F. Rudzicz. Semi-supervised classification by reaching consensus among modalities. In: NeurIPS Workshop on Interpretability and Robustness in Audio, Speech, and Language IRASL, Montreal, 2018

Vinay Prabhu, Chief Scientist, UnifyID Inc

Project GaitNet: Ushering in the ImageNet moment for human Gait kinematics

Papers:

  • [1] Stephanie Tietz, Vinay Uday Prabhu, ‘GaitID-2-SquatID: Deep transfer learning for human kinematics’, Time Series workshop, ICML-2019, June-2019, Long Beach, California, USA 
  • [2] Vinay Prabhu, Stephanie Tietz and Anh Ta, ‘Classifying humans using Deep time-series transfer learning : accelerometric gait-cycles to gyroscopic squats , Proceedings, 5th SIGKDD Workshop on Mining and Learning from Time Series (MiLeTS), KDD-2019, Anchorage, Aug-2019, Alaska
  • [3] Vinay Prabhu, John Whaley and Mihail D, ‘MODHILL: A framework for debugging gait in multi-factor authentication systems’, Workshop on Human In the Loop Learning (HILL)-ICLR 2019, May 2019, New Orleans, USA
  • [4] Vinay Prabhu, John Whaley ,”Vulnerability of deep learning-based gait biometric recognition to adversarial perturbations”, In Proceedings of the CVPR 2017 CV-COPS workshop, 2017,Honolulu, Hawaii — July 21, 2017.

Code and Datasets:

Further reading list of 3 other notable Gait-classification projects: 

Josh Wills, Software Engineer, Slack

Data Labeling as Religious Experience

Igor Markov, Professor, University of Michigan

Quantum Computing: a Treasure Hunt, not a Gold Rush

Jamila Smith-Loud, User Researcher, Google

Justin Armstrong, Senior Backend Engineer – Applied ML, Compology

Applying Computer Vision to Reduce Contamination in the Recycling Stream

MLconf NYC 2019 Speaker Resources

MLconf NYC 2019 Speaker Resources

Emily Pitler, Software Engineer, Google AI

Representations from Natural Language Data: Successes and Challenges

Papers

Code and Pretrained Models

Datasets:

Tutorials:

View the slides for this presentation.

Watch this presentation on YouTube.

Anna Choromanska, Assistant Professor, NYU Tandon School of Engineering

Data-driven challenges in AI: scale, information selection, and safety

Neel Sundaresan, Partner Director, Microsoft

Teaching a Machine to Code

Esperanz López Aguilera, Machine Learning Team, Kx

Using a Bayesian Neural Network in the Detection of Exoplanets

Soumith Chintala, Researcher, Facebook

Increasing the Impact of AI Through Better Software

Amir Sadoughi, Senior Software Development Engineer, Amazon Web Services

Developing large-scale machine learning algorithms on Amazon SageMaker

Renaud Bourassa, Staff Software Engineer, Slack

Building Machine Learning Models with Strict Privacy Boundaries

Reference- Michael Bendersky, Xuanhui Wang, Donald Metzler, and Marc Najork. 2017. Learning from User Interactions in Personal Search via Attribute Parameterization. In Proceedings of the Tenth ACM International Conference on Web Search and Data Mining (WSDM ’17). ACM, New York, NY, USA, 791-799. DOI: https://doi.org/10.1145/3018661.3018712

 

Rishabh Mehrotra, Research Scientist, Spotify Research

Recommendations in a Marketplace: Personalizing Explainable Recommendations with Multi-objective Contextual Bandits

Madalina Fiterau , Assistant Professor, University of Massachusetts Amherst

Hybrid Machine Learning Methods for the Interpretation and Integration of Heterogeneous Multimodal Data

UMass Profile page: https://www.cics.umass.edu/people/fiterau-brostean-madalina

Webpage for PhD/Postdoc projects: http://www.cs.cmu.edu/~mfiterau/

Lab and project pages:

References:
[1] Madalina Fiterau and Artur Dubrawski. Projection Retrieval for Classification. In
Advances in Neural Information Processing Systems 25, pages 3032–3040,
NIPS 2012. Poster.
[2] Fiterau M, Dubrawski A, Chen L, Hravnak M, Clermont G, Pinsky MR. Automatic
identification of artifacts in monitoring critically ill patients. Annual Congress of
the European Society of Intensive Care Medicine 2014; 39 Suppl 2: S470.
Electronic Poster.
[3] Donghan Wang, Madalina Fiterau and Artur Dubrawski, VIPR: An Interactive
Tool for Meaningful Visualization of High-Dimensional Data, Demonstration at the
International Joint Conference in Artificial Intelligence IJCAI 2016.
[4] Fiterau M, Wang J, Dubrawski A, Clermont G, Hravnak M, Pinsky MR. Using
expert review to calibrate semi-automated adjudication of vital sign alerts in step-
down units. Society of Critical Care Medicine Annual Congress 2016. Abstract
and slides. Star Research Award.
[5] Peter Kontschieder, Madalina Fiterau, Antonio Criminisi and Samuel Rota-Bulo.
Deep Neural Decision Forests, International Conference in Computer Vision,
ICCV 2015. Spotlight and poster. Marr Prize for Best Paper.
[6] Madalina Fiterau, Suvrat Bhooshan, Jason Fries, Charles Bournhonesque,
Jennifer Hicks, Eni Halilaj, Christopher Ré and Scott Delp. ShortFuse:
Biomedical Time Series Representations in the Presence of Structured
Information. 3rd Conference on Machine Learning for Healthcare, MLHC 2017.
Spotlight.
[7] Jason A Fries, Paroma Varma, Vincent S Chen, Ke Xiao, Heliodoro Tejeda,
Priyanka Saha, Jared Dunnmon, Henry Chubb, Shiraz Maskatia, Madalina
Fiterau, Scott Delp, Euan Ashley, Christopher Ré, James Priest, Weakly
supervised classification of rare aortic valve malformations using unlabeled
cardiac MRI sequences. Accepted to Nature Communications.

 

Sandhya Prabhakaran, Research Fellow, Memorial Sloan Kettering Cancer Centre

A Bayesian Approach To Model Overlapping Objects Available As Distance Data

 

Liliana Cruz Lopez, Graduate Student, Columbia University

Deep Reinforcement Learning based Insulin Controller for Effective Type-1 Diabetic Care

Kerry Weinberg, Head of Data Science, Amgen Digital Health

Putting the Tech in Biotech: Challenges and Opportunities in Application of AI in Healthcare

Nitin Sharma, Research Scientist, PayPal

Deep Learning Applications to Online Payment Fraud Detection

Janani Kalyanam, Sr. Data Scientist, Intuit

Machine Learning to Detect Illegal Online Sales of Prescription Opioids

Leanna Kent, Sr. Data Scientist, Elder Research

Using Network Analysis to Detect Kickback Schemes Among Medical Providers

 

Niels Bantilan, Machine Learning Engineer, Talkspace

Augmenting Mental Health Care in the Digital Age: Machine Learning as a Therapist Assistant

Related Papers

  • Kshirsagar, Rohan, Robert Morris, and Sam Bowman. “Detecting and explaining crisis.” arXiv preprint arXiv:1705.09585 (2017).
  • Al Hanai, Tuka, Mohammad Ghassemi, and James Glass. “Detecting Depression with Audio/Text Sequence Modeling of Interviews.” Proc. Interspeech. 2018.
  • Fast, Ethan, Binbin Chen, and Michael S. Bernstein. “Empath: Understanding topic signals in large-scale text.” Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. ACM, 2016.

Github Repos

Tweet for a chance to win FREE books at MLconf NYC this Friday!

We’d like to thank the participating publishers who have created custom discount codes for online order and are sending books for the Book Display at MLconf NYC 2019. At the close of the event, we’ll host a book giveaway from a small collection of various ML books and textbooks across several publishers. Attendees who tweet the most interesting and unique tweets, using the @mlconf or #mlcny. Book winners will be tweeted at by the @mlconf handle on 3/29/2019. Must be present to win.

The 40% off discount code on the following books (for all attendees):  ctwmlconfny19

**Email books@mlconf.com for a chance to win a free eBook Grokking Deep Learning for Computer Vision by Mohamed Elgendy  Full Catalog, here.

 

The books are 20% off and the discount is good until April 30, 2019. Check out classics such as:

For a full list of books, check out:  https://www.cambridge.org/wm-ecommerce-web/academic/landingPage/MLCONF19

Interview with Jacob Borodovsky

CB)  Tell us briefly about yourself and your work.

JB)  I’m an epidemiologist with a background in psychoactive substance use and policy. I’m interested understanding how the regulation of drugs affects patterns of drug use in the population. Most of the opioid-related work that I have done so far concerns a medication called buprenorphine – a treatment for opioid use disorder.

Link: https://www.cdc.gov/drugoverdose/epidemic/index.html

CB) You received a Ph.D. from Dartmouth and currently work as a postdoctoral researcher in the Department of Psychiatry at Washington University School of Medicine in St. Louis. You have experience with advanced statistical methods and devote much of your research towards understanding the opioid epidemic and other drug policy related research. What brought you to this path?

JB)  I don’t know why, but when I was a kid I loved movies, shows, and documentaries about drug addiction and drug cartels. It was an interesting way to learn about law/politics, economics, neurobiology etc. Also, the disease of addiction raised interesting questions at the intersection of free will and criminal justice. I guess I just followed these interests in college and here I am. I should be clear though. I am not a machine learning expert, nor do I consider myself a data scientist. I only have a basic understanding of the principles of these techniques. My first foray into the ML area was as a pre-doc at the Dartmouth Center for Technology and Behavioral Health. Benjamin Crosier (postdoc at the time) and I used Random Forest to try and predict opioid overdose. We trained it on a massive dataset from individuals with opioid use disorder that had been collected by some of our colleagues in NYC. We presented the data as a poster but never got around to publishing a manuscript.

CB) Walk us through your typical workday.

JB)  It’s actually pretty fun. I just spend the day running analyses, reading and writing papers, and thinking about drugs.

CB) What tools do you use in your research?

JB)  I do all my modeling on STATA. I can already hear the criticism that I should be using R or Python…

CB) Where does your data come from and what are some of the challenges you experience working with this data set?

JB)  My primary data sources are federally funded datasets like the National Survey on Drug Use and Health, medical records within healthcare systems, and web surveys using Facebook ads for recruitment. The challenge is usually making decisions on methodological trade-offs. For example, federal datasets allow you to produce nationally representative estimates, but you have no control over the types of questions asked.

CB) What are some of the critical areas of the opioid epidemic which could benefit from advanced Machine Learning techniques? / Where do you see things heading in the next 5-10 years?

JB) Again, I know more about opioids than I do about ML so I see my contribution here as pointing towards the types of questions that real ML experts might think about tackling.

Broadly speaking, in medical research in general, there seems to be is a lot of working going on with deep learning and diagnostic image analysis (I’m told areas like Radiology are dying because eventually MDs will be replaced by AI). With regard to opioids, I can’t see how this type of research applies (hopefully someone can prove me wrong here). The problem is that when it comes to opioid addiction, the variables that we work with are behavioral in nature (frequency of heroin purchases, interaction with law enforcement, injection status etc). There are few if any diagnostic images that need to be analyzed. However, outside of medicine, there might be some interesting opportunities. I keep wondering if it would be possible to do something like use images from Google street view to predict opioid-related events in different neighborhoods.

I would guess that in the coming years we will see automated ML embedded into the electronic medical record systems used in healthcare (including pharmacies). To me that seems like a promising area. Just brainstorming here quickly, it would be great if we could leverage those data to predict things like:

  • Retention in addiction treatment
  • Abstinence during treatment
  • Diversion of medications
  • Identifying aberrant opioid consumption patterns
  • Identifying aberrant opioid prescribing patterns
  • Who is going to come back to the emergency with another overdose? How soon?
  • Holding dose constant, who is most likely to develop an opioid addiction within a population of patients who have chronic pain and are prescribed opioids?
  • Clinical decision-making such as:
    • Finding the right dosing of opioid agonist medications (for meds related to chronic pain and meds for treating opioid addiction
    • Deciding whether buprenorphine or methadone is the more appropriate

Outside of EHRs, it would be great if we could use publicly available data (including social media) to predict “outbreaks” of overdoses within a particular geographic location.

One thing that you guys might want to think about when starting a project is whether downstream, the clinical application will require that we know the variables involved. In some situations I could see us not really needing to know how a prediction is made. In other scenarios it would make sense to know the most important inputs. I think that end goal is important to keep in mind before starting a project.

Another thing that comes to mind is network analysis. I am not entirely sure how those types of analyses are conducted, or how they overlap with ML, but I imagine they’re linked in some way. Understanding how drug use spreads within communities/social networks seems to be another interesting area worth looking at. It would be interesting to see if some of those dynamics change depending on things like urban/rural environment and the nature/source of the supply.

I’ll end here on something I think that would be really ambitious. I am interested in drug regulation and more specifically in the possibility of maybe using something like NLP to help us effectively regulate pharmaceutical companies to mitigate misuse of prescription medications in the population (particularly marketing/advertising of medications). For those of you who don’t know, the opioid epidemic started with prescription opioids like oxycodone. A variety of institution-level factors came together in the 90’s to create the groundwork for the epidemic we see today. For example, there were a lot of public conversations about pain which led to it becoming the “5th vital sign” which then meant that the healthcare system needed to check for pain more often. At the same time, pharmaceutical companies created extended release (but easily breakable) versions of opioids and marketed the hell out of the medications (promoted the idea that opioid addiction was extremely rare, that chronic pain was a bigger problem than it actually was, gave promotional materials to MD’s…the list goes on). History will repeat itself. Next time it may not be opioids, it may be some other medication. I keep wondering if, from a governmental/regulatory standpoint, if it would be possible to build something now that would allow us to anticipate this sort of cultural/institution-level activity/conversation so we can prevent something like this from happening again. I’m sure that sounds big brotherish but I believe that it could be done responsibly.

CB) Are you looking for researchers to join your team/mission? What skills are you looking for?  Who are your ideal candidates?

JB)  I’m just a lowly postdoc. I’m always looking for opportunities to collaborate though. My approach to these things is to start small, be pragmatic, and make sure execution is feasible. Then you can build more complex things from there. Please feel free to get in touch if you want to throw ideas around.

About Jacob Borodovsky

Dr. Borodovsky is currently a postdoctoral researcher at the Washington University School of Medicine in St. Louis. He received a BA in Clinical Psychology from Tufts University and a PhD in Health Policy and Clinical Practice from Dartmouth College. His research interests lie at the intersection of addiction, epidemiology, and policy. He is particularly interested in understanding how drug control and regulation efforts affect patterns of drug use in the population. Currently, he is investigating a variety of policy-relevant questions concerning cannabis, benzodiazepine, alcohol, and opioid use.

MLconf SF 2018 Speaker Resources

2018 Speaker Resources


Ilke Demir, Postdoctoral Researcher, Facebook

Open datasets and call for papers:

DeepGlobe Benchmark

Generative Street Addresses

My related references

  • Demir I., Hughes F., Raj A., Dhruv K., Muddala S.M., Garg S., Doo B., Raskar R., 2018. Generative Street Addresses from Satellite Imagery. ISPRS International Journal of Geo-Information (IJGI).
  • Aliaga D., Demir I, Benes B., Wand M., 2016. Inverse Procedural Modeling of 3D Models for Virtual Worlds. ACM SIGGRAPH 2016 Courses. (SIGGRAPH)
  • Demir I., Aliaga D., Benes B., 2016. Proceduralization for Editing 3D Architectural Models. International Conference on 3D Vision 2016 (3DV).
  • Demir I., Aliaga D., Benes B., 2015. Coupled Segmentation and Similarity Detection for Architectural Models. ACM Transactions on Graphics (ToG), also SIGGRAPH 2015.
  • Demir I., Aliaga D., Benes B., 2015. Procedural Editing of 3D Building Point Clouds. IEEE International Conference on Computer Vision 2015 (ICCV).
  • Demir I., Aliaga D., Benes B., 2014. Proceduralization of Buildings at City Scale. International Conference on 3D Vision 2014 (3DV).

Mentioned projects

Mentioned references

  • [1] Stefan Voigt, Fabio Giulio-Tonolo, Josh Lyons, Jan Kucera, Brenda Jones, Tobias Schneiderhan, Gabriel Platzeck, Kazuya Kaku, Manzul Kumar  Hazarika,  Lorant  Czaran, et al. Global trends in  satellite-based emergency mapping. Science, 353(6296):247–252, 2016.
  • [2] Timnit Gebru, Jonathan Krause, Yilun Wang, Duyun Chen, Jia Deng, Erez Lieberman Aiden, Li Fei-Fei. Demography with deep learning and street view. Proceedings of the National Academy of Sciences Dec 2017, 114 (50) 13108-13113
  • [3] Carlos A. Vanegas, Ignacio Garcia-Dorado, Daniel G. Aliaga, Bedrich Benes, and Paul Waddell. 2012. Inverse design of urban procedural models. ACM Trans. Graph. 31, 6, Article 168 (November 2012), 11 pages.

 


Joan Xiao, Lead Machine Learning Scientist, Figure Eight

 


Prasanth Anbalagan, Senior Software Engineer (Q&E Analysis) on the Artificial Intelligence Center of Excellence Team, Red Hat

 


Yi Li, Dr. Yi Li, Machine Learning Research Scientist, Baidu Silicon Valley AI Lab

 


Dr. Leslie Smith, Senior Research Scientist, US Naval Research Laboratory

  • Smith, Leslie N. “Cyclical learning rates for training neural networks.” In Applications of Computer Vision (WACV), 2017 IEEE Winter Conference on, pp. 464-472. IEEE, 2017.
  • Smith, Leslie N., and Nicholay Topin. “Super-Convergence: Very Fast Training of Residual Networks Using Large Learning Rates.” arXiv preprint arXiv:1708.07120 (2017).
  • Smith, Leslie N. “A disciplined approach to neural network hyper-parameters: Part 1–learning rate, batch size, momentum, and weight decay.” arXiv preprint arXiv:1803.09820 (2018).

 

My github page:

 

Large batch papers:

  • Goyal, Priya, Piotr Dollár, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, and Kaiming He. “Accurate, large minibatch SGD: training imagenet in 1 hour.” arXiv preprint arXiv:1706.02677 (2017).
  • You, Yang, Igor Gitman, and Boris Ginsburg. “Scaling sgd batch size to 32k for imagenet training.” arXiv preprint arXiv:1708.03888 (2017).
  • Akiba, Takuya, Shuji Suzuki, and Keisuke Fukuda. “Extremely large minibatch sgd: Training resnet-50 on imagenet in 15 minutes.” arXiv preprint arXiv:1711.04325 (2017).
  • Codreanu, Valeriu, Damian Podareanu, and Vikram Saletore. “Scale out for large minibatch SGD: Residual network training on ImageNet-1K with improved accuracy and reduced time to train.” arXiv preprint arXiv:1711.04291 (2017).
  • Smith, Samuel L., Pieter-Jan Kindermans, Chris Ying, and Quoc V. Le. “Don’t decay the learning rate, increase the batch size.” arXiv preprint arXiv:1711.00489 (2017).
  • Jia, Xianyan, Shutao Song, Wei He, Yangzihao Wang, Haidong Rong, Feihu Zhou, Liqiang Xie et al. “Highly Scalable Deep Learning Training System with Mixed-Precision: Training ImageNet in Four Minutes.” arXiv preprint arXiv:1807.11205 (2018).
  • Yao, Zhewei, Amir Gholami, Kurt Keutzer, and Michael Mahoney. “Large batch size training of neural networks with adversarial training and second-order information.” arXiv preprint arXiv:1810.01021 (2018).

Tweet for a chance to win FREE books!

We’d like to thank the participating publishers who have created custom discount codes for online order and are sending books for the Book Display at MLconf 2018. At the close of the event, we’ll host a book giveaway for the attendees who tweet the most interesting and unique tweets, using the @mlconf or #mlconf18. Book winners will be tweeted at by the @mlconf handle on 11/14/18.

Manning Publications

The 40% off discount code on the following books (for all attendees):  ctwmlconf18

 

**Email books@mlconf.com for a chance to win a free eBook  Deep Reinforcement Learning in Action by Alexander Zai and Brandon Brown! https://www.manning.com/books/exploring-data-with-python?utm_source=mlconf&utm_medium=social&utm_campaign=book_exploringdatawithpython&utm_content=conference_distribution

 

CRC Press Machine Learning Books:

 

The books can be purchased from the CRC website, using the discount code MLC20. The discount is valid until December 31st 2018.

 

Cambridge University Press:

A 20% discount on Cambridge books in Machine Learning and related areas is available using the discount code MLCONF18. Check out classics such as:

For a full list of books, check out:  www.cambridge.org/MLCONF18

 

Packt Publishing

Packt is offering a 60% discount on ML books for the MLconf audience! Here: https://www.packtpub.com/tech/Machine-Learning 

Additional Related Books On Display:

 

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