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Author: torontoai

[D] Looking for a specific figure demonstrating the importance of good datasets

I’m looking for a figure to cite in one of my projects. I have seen it once before, but didn’t save the source, and have no luck finding it again. The effect of the figure is to show that on average, successful theories were invented very early, but good results only follow the release of good datasets.

This is achieved by listing a number of tasks (e.g. image classification). For each task, the figure lists which technique has been used to successfully tackle the problem (e.g. CNNs) and the year it was first proposed. Additionally, it lists the year that a significant dataset for this problem (e.g. ImageNet) was released. Finally, the last column displays the year that some performance threshold was reached on the given technique.

I hope my description is clear. It would be great if someone could find the actual figure!

In general, do you think the claim made here is valid? Or is it simplistically aggregating too much information, and missing the point?

submitted by /u/Drimage
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TORONTO AI MEETUP – TRADE REV

TORONTO AI MEETUP – ML solution architecture at TradeRev

150 John St Toronto

Topic
——————–
Amit Jain, TradeRev’s R&D Lead will give background into the company’s product and solution, while discussing its microservice architecture, serverless scalable ML solutions and the continuous integration and deployments that keep TradeRev on the cutting edge of innovation.

Agenda:
——————–
6:00p – arrive & socialize
6:30p – talk begins
7:00p – Q&A
7:20p – AI news
7:25p – 20 second open mic rounds
7:30p – wrap up

About TradeRev
——————–
An auto tech company that changed the way car dealers buy and sell vehicles through their revolutionary app, TradeRev is constantly pushing the boundaries when it comes to its tech.

Discord & Slack
——————–
Join us on Discord: JOIN DISCORD
Join us on Slack: JOIN 

JOIN MEETUP

Trade Rev

150 John Street

43.6505,-79.3914

Vector Researchers Prepare for 33rd Annual Conference on Neural Information Processing Systems (NeurIPS)

Vector researchers are preparing for the world’s premier machine learning conference, the 33rd annual conference on Neural Information Processing Systems (NeurIPS). A multi-track machine learning and computational neuroscience conference that includes invited talks, demonstrations, symposia and oral and poster presentations of refereed papers, NeurIPS 2019 runs December 8-14 at the Vancouver Convention Center, Vancouver, BC.

This year, Vector researchers had an impressive 23 papers accepted to the conference. Additionally, they are organizing four workshops.

At the 2018 NeurIPS conference, Vector Faculty Members and students collaborated to win two of four Best Paper awards and a Best Student Paper Award for their research. Read more about Vector’s accomplishments at last year’s conference here.

 

Accepted Papers by Vector researchers:

Efficient Graph Generation with Graph Recurrent Attention Networks
Renjie Liao (University of Toronto) · Yujia Li (DeepMind) · Yang Song (Stanford University) · Shenlong Wang (University of Toronto) · Will Hamilton (McGill) · David Duvenaud (University of Toronto) · Raquel Urtasun (Uber ATG) · Richard Zemel (Vector Institute/University of Toronto)

 

Incremental Few-Shot Learning with Attention Attractor Networks
Mengye Ren (University of Toronto / Uber ATG) · Renjie Liao (University of Toronto) · Ethan Fetaya (University of Toronto) · Richard Zemel (Vector Institute/University of Toronto)

 

SMILe: Scalable Meta Inverse Reinforcement Learning through Context-Conditional Policies
Seyed Kamyar Seyed Ghasemipour (University of Toronto, Vector Institute) · Shixiang (Shane) Gu (Google Brain) · Richard Zemel (Vector Institute/University of Toronto)

 

Lookahead Optimizer: k steps forward, 1 step back
Michael Zhang (University of Toronto) · James Lucas (University of Toronto) · Jimmy Ba (University of Toronto / Vector Institute) · Geoffrey Hinton (Google)

Graph Normalizing Flows
Jenny Liu (Vector Institute, University of Toronto) · Aviral Kumar (UC Berkeley) · Jimmy Ba (University of Toronto / Vector Institute) · Jamie Kiros (Google Inc.) · Kevin Swersky (Google)

 

Latent Ordinary Differential Equations for Irregularly-Sampled Time Series
Yulia Rubanova (University of Toronto) · Tian Qi Chen (U of Toronto) · David Duvenaud (University of Toronto)

 

Residual Flows for Invertible Generative Modeling
Tian Qi Chen (U of Toronto) · Jens Behrmann (University of Bremen) · David Duvenaud (University of Toronto) · Joern-Henrik Jacobsen (Vector Institute)

 

Neural Networks with Cheap Differential Operators
Tian Qi Chen (U of Toronto) · David Duvenaud (University of Toronto)

 

Stochastic Runge-Kutta Accelerates Langevin Monte Carlo and Beyond
Xuechen Li (Google) · Yi Wu (University of Toronto & Vector Institute) · Lester Mackey (Microsoft Research) · Murat Erdogdu (University of Toronto)

Value Function in Frequency Domain and Characteristic Value Iteration
Amir-massoud Farahmand (Vector Institute)

 

Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderer
Wenzheng Chen (University of Toronto) · Huan Ling (University of Toronto, NVIDIA) · Jun Gao (University of Toronto) · Edward Smith (McGill University) · Jaakko Lehtinen (NVIDIA Research; Aalto University) · Alec Jacobson (University of Toronto) · Sanja Fidler (University of Toronto)

 

Fast Convergence of Natural Gradient Descent for Over-Parameterized Neural Networks
Guodong Zhang (University of Toronto) · James Martens (DeepMind) · Roger Grosse (University of Toronto)

 

Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model
Guodong Zhang (University of Toronto) · Lala Li (Google) · Zachary Nado (Google Inc.) · James Martens (DeepMind) · Sushant Sachdeva (University of Toronto) · George Dahl (Google Brain) · Chris Shallue (Google Brain) · Roger Grosse (University of Toronto)

 

Understanding Posterior Collapse in Variational Autoencoders
James Lucas (University of Toronto) · George Tucker (Google Brain) · Roger Grosse (University of Toronto) · Mohammad Norouzi (Google Brain)

 

Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks
Qiyang Li (University of Toronto) · Saminul Haque (University of Toronto) · Cem Anil (University of Toronto; Vector Institute) · James Lucas (University of Toronto) · Roger Grosse (University of Toronto) · Joern-Henrik Jacobsen (Vector Institute)

 

MixMatch: A Holistic Approach to Semi-Supervised Learning
David Berthelot (Google Brain) · Nicholas Carlini (Google) · Ian Goodfellow (Google Brain) · Nicolas Papernot (University of Toronto) · Avital Oliver (Google Brain) · Colin A Raffel (Google Brain)

 

Fast PAC-Bayes via Shifted Rademacher Complexity
Jun Yang (University of Toronto) · Shengyang Sun (University of Toronto) · Daniel Roy (Univ of Toronto & Vector)

 

Information-Theoretic Generalization Bounds for SGLD via Data-Dependent Estimates
Gintare Karolina Dziugaite (Element AI) · Mahdi Haghifam (University of Toronto) · Jeffrey Negrea (University of Toronto) · Ashish Khisti (University of Toronto) · Daniel Roy (Univ of Toronto & Vector)

 

Understanding attention in graph neural networks
Boris Knyazev (University of Guelph) · Graham W Taylor (University of Guelph) · Mohamed R. Amer (Robust.AI)

 

The Cells Out of Sample (COOS) dataset and benchmarks for measuring out-of-sample generalization of image classifiers
Alex Lu (University of Toronto) · Amy Lu (University of Toronto/Vector Institute) · Wiebke Schormann (Sunnybrook Research Institute) · David Andrews (Sunnybrook Research Institute) · Alan Moses (University of Toronto)

 

Learning Reward Machines for Partially Observable Reinforcement Learning
Rodrigo Toro Icarte (University of Toronto and Vector Institute) · Ethan Waldie (University of Toronto) · Toryn Klassen (University of Toronto) · Rick Valenzano (Element AI) · Margarita Castro (University of Toronto) · Sheila McIlraith (University of Toronto)

 

When does label smoothing help?
Rafael Müller (Google Brain) · Simon Kornblith (Google Brain) · Geoffrey E Hinton (Google & University of Toronto)

 

Stacked Capsule Autoencoders
Adam Kosiorek (University of Oxford) · Sara Sabour (Google) · Yee Whye Teh (University of Oxford, DeepMind) · Geoffrey E Hinton (Google & University of Toronto)

 

Vector Institute researchers are hosting four workshops:

 

Machine Learning and the Physical Science: Organized by Juan Felipe Carrasquilla, (Canada CIFAR AI Chair, Vector Institute, Faculty Member, Vector Institute and Assistant Professor (Adjunct), Department of Physics and Astronomy, University of Waterloo) and collaborators, this workshop focuses on applying machine learning to outstanding physics problems. | Learn more 

 

Fair ML in Healthcare: Organized by Shalmali Joshi, Post-doctoral Fellow, and Shems Saleh at the Vector Institute, and collaborators this, the goal of this workshop is to investigate issues around fairness in machine learning-based health care. | Learn more

 

Program Transformations for ML: Organized by David Duvenaud (Assistant Professor at the University of Toronto, Co-founder, Invenia, Canada Research Chair in Generative Models and Faculty Member, Vector Institute) and his collaborators.  This workshop aims at viewing program transformations in ML in a unified light, making these capabilities more accessible, and building entirely new ones | Learn more

 

Machine Learning with Guarantees: Organized by Daniel Roy (Assistant Professor at the University of Toronto, Faculty Member, Vector Institute and Canada CIFAR Artificial Intelligence Chair) and his collaborators, this workshop will bring together researchers to discuss the problem of obtaining performance guarantees and algorithms to optimize them.  | Learn more

Learn more:

  • Check out a full list of Vector research publications here.

Microsoft and Nuance join forces in quest to help doctors turn their focus back to patients

Imagine a visit to your doctor’s office in which your physician asks you how you’ve been feeling, whether your medication is working or if the shoulder pain from an old fall is still bothering you — and his or her focus is entirely on you and that conversation.

The doctor is looking at you, not at a computer screen. He or she isn’t moving a mouse around hunting for an old record or pecking on the keyboard to enter a diagnosis code.

This sounds like an ideal scenario, but as most people know from their own visits to the doctor, it’s far from the norm today.

But experts say that in an exam room of the future enhanced by artificial intelligence, the doctor would be able to call up a lab result or prescribe a new medicine with a simple voice command. She or he wouldn’t be distracted by entering symptoms into your electronic health record (EHR). And at the end of the visit, the essential elements of the conversation would have been securely captured and distilled into concise documentation that can be shared with nurses, specialists, insurance companies or anyone else you’ve entrusted with your care.

A new strategic partnership between Microsoft and Nuance Communications Inc. announced today will work to accelerate and deliver this level of ambient clinical intelligence to exam rooms, allowing ambient sensing and conversational AI to take care of some of the more burdensome administrative tasks and to provide clinical documentation that writes itself. That, in turn, will allow doctors to turn their attention fully to taking care of patients.

Of course, there are still immense technical challenges to getting to that ideal scenario of the future. But the companies say they believe that they already have a strong foundation in features from Nuance’s ambient clinical intelligence (ACI) technology unveiled earlier this year and Microsoft’s Project EmpowerMD Intelligent Scribe Service. Both are using AI technologies to learn how to convert doctor-patient conversations into useful clinical documentation, potentially reducing errors, saving doctors’ time and improving the overall physician experience.

“Physicians got into medicine because they wanted to help and heal people, but they are spending a lot of their time today outside of the care process,” said Joe Petro, Nuance executive vice president and chief technology officer. “They’re entering in data to make sure the appropriate bill can be generated. They’re capturing insights for population health and quality measures. And although this data is all important, it’s really outside a physician’s core focus on treating that patient.”

 

YouTube Video

Primary care doctors spend two hours on administrative tasks for every hour they’re involved in direct patient care, studies have shown. If they don’t capture a patient’s complaint or treatment plan during or shortly after an exam, that documentation burden will snowball as the day goes on. In another recent study, physicians reported one to two hours of after-hours work each night, mostly related to administrative tasks.

This shift to digital medical record keeping and so-called ‘meaningful use’ regulations is well-intentioned and has provided some important benefits, said Dr. Ranjani Ramamurthy, senior director at Microsoft Healthcare who leads the company’s EmpowerMD research.

People no longer have to worry about not being able to read a doctor’s handwriting or information that never makes it into the right paper file. But the unintended consequence has been that doctors are sometimes forced to focus on their computers and administrative tasks instead of their patients, she said.

After starting her career in computer science, Ramamurthy went back to school to get a medical degree and pursue cancer research. But as she walked the halls of the hospital every day, she couldn’t help thinking that she was missing an opportunity to use her background to create tech solutions that could reinvigorate the doctor-patient relationship.

Ramamurthy noted that most physicians got into healthcare because they want to use their skills and expertise to treat patients, not to feel tethered to their keyboards.

“We need to work on building frictionless systems that take care of the doctors so they can do what they do best, which is take care of patients,” she said.

Built on Microsoft Azure — and working in tandem with the EHR — this new technology will marry the two companies’ strengths in developing ambient sensing and conversational AI solutions. Those include ambient listening with patient consent, wake-up word, voice biometrics, signal enhancement, document summarization, natural language understanding, clinical intelligence and text-to-speech.

Nuance is a leading provider of AI-powered clinical documentation and decision-making support for physicians. Leveraging deep strategic partnerships with the major providers of EHRs, the company has spent decades developing medically relevant speech recognition and processing solutions such as its Dragon Medical One platform, which allows doctors to easily and naturally enter a patient’s story and relevant information into an EHR using dictation. Nuance conversational AI technologies are already used by more than 500,000 physicians worldwide, as well as in 90 percent of U.S. hospitals.

Microsoft brings deep research investments in AI and partner-driven healthcare technologies, commercial relationships with nearly 170,000 healthcare organizations, and enterprise-focused cloud and AI services that accelerate and enable scalable commercial solutions. Earlier this month, for instance, Microsoft announced a strategic collaboration to combine its AI technology with Novartis’ deep life sciences expertise to address challenges in developing new drugs.

In other areas, Azure Cognitive Services offers easy-to-deploy AI tools for speech recognition, computer vision and language understanding, and trusted Azure cloud services can support the user’s compliance with privacy and regulatory requirements for healthcare organizations.

As part of the agreement, Nuance will migrate the majority of its current on-site internal infrastructure and hosted products to Microsoft Azure. Nuance already is a Microsoft Office 365 customer for its more than 8,500 employees worldwide, empowering them with the latest in collaboration and communications tools, including Microsoft Teams.


“We need to work on building frictionless systems that take care of the doctors so they can do what they do best, which is take care of patients.”

~ Dr. Ranjani Ramamurthy, senior director at Microsoft Healthcare


“Just capturing a conversation between two people has been a thorny technical problem for a long time, and a lot of companies have attempted to crack it,” Petro said. “This partnership brings two trusted healthcare superpowers together to solve some of the most difficult challenges and also to leverage the most innovative advances we’ve made in AI, speech and natural language processing.”

The companies will expand upon Nuance’s early success with ACI and expect the technology to be introduced to an initial set of physician specialties in early 2020, and then it will be expanded to numerous other medical specialties over the next few years, Petro said. Initially, the ACI output may be checked by a remote reviewer with medical expertise to provide an important quality check and produce additional training data for the AI models. Once the system has proven its accuracy for a given physician, the ACI documentation will go directly to that physician, who can review it, make any necessary revisions and sign off on a treatment plan all in real-time, Petro said.

With a patient’s consent, ACI is designed to securely ingest and synthesize patient-doctor conversations, integrate that data with information from an EHR, populate a patient’s chart and also help the EHR deliver intelligent recommendations to the doctor.

With innovations in multi-party speech recognition, language understanding and computer vision, these tools can listen to the encounter between the doctor and a patient who grants consent, sense whether they’re pointing to a left knee or right knee when verbally describing a particular pain, extract medically relevant details and translate what just occurred in the exam room into actionable clinical documentation and care suggestions.

“Moving forward, we recognize that reducing the burden of clinical documentation is just the beginning,” said Dr. Greg Moore, Microsoft’s corporate vice president for health technology and alliances. “As the core AI improves and becomes more capable, it will be able to understand much more deeply what is going on by observing doctors and nurses in their day to day work. Ambient clinical intelligence will be able to work in tandem with the EHR to help convert those observations into supportive, augmenting actions.”

For instance, an AI-enabled system can learn to recognize when a doctor is talking to a patient about a new medication, and it can automatically review past conversations as well as the patient’s history to reduce the risk of a drug interaction or allergic reaction. Or it can mine a patient’s complicated medical history with new reported symptoms and offer suggestions for potential diagnoses for the doctor to consider.

In addition, the two companies will open up the ACI platform to an ecosystem of partners than can bring other highly valuable AI innovations to the exam room or at the bedside where the ambient sensing device will be present.

“We want ambient clinical intelligence to assist the EHR in delivering recommendations at the time when it matters — not three days later on your patient portal or when a nurse follows up, but when the doctor and patient are face to face and when that information can actually inform care,” Ramamurthy said.

Related:

The post Microsoft and Nuance join forces in quest to help doctors turn their focus back to patients appeared first on The AI Blog.

[P] MixMatch implementation in PyTorch

I made an implementation of MixMatch (paper) in PyTorch, thought I’d share for those who are interested. Works as an installable package which you can use to create a dataloader that implements the mixmatch algorithm, as well as construct the appropriate loss function.

https://github.com/FelixAbrahamsson/mixmatch-pytorch

Feedback and comments are appreciated!

submitted by /u/Mimsyy
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[N] New AI neural network approach detects heart failure from a single heartbeat with 100% accuracy

Congestive Heart Failure (CHF) is a severe pathophysiological condition associated with high prevalence, high mortality rates, and sustained healthcare costs, therefore demanding efficient methods for its detection. Despite recent research has provided methods focused on advanced signal processing and machine learning, the potential of applying Convolutional Neural Network (CNN) approaches to the automatic detection of CHF has been largely overlooked thus far. This study addresses this important gap by presenting a CNN model that accurately identifies CHF on the basis of one raw electrocardiogram (ECG) heartbeat only, also juxtaposing existing methods typically grounded on Heart Rate Variability. We trained and tested the model on publicly available ECG datasets, comprising a total of 490,505 heartbeats, to achieve 100% CHF detection accuracy. Importantly, the model also identifies those heartbeat sequences and ECG’s morphological characteristics which are class-discriminative and thus prominent for CHF detection. Overall, our contribution substantially advances the current methodology for detecting CHF and caters to clinical practitioners’ needs by providing an accurate and fully transparent tool to support decisions concerning CHF detection.

(emphasis mine)

Press release: https://www.surrey.ac.uk/news/new-ai-neural-network-approach-detects-heart-failure-single-heartbeat-100-accuracy

Paper: https://www.sciencedirect.com/science/article/pii/S1746809419301776

submitted by /u/aiismorethanml
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[D] Deep Learning Software Licences

Hi, I want to use an open-source study for my commercial product. This study closed to commercial usage. (ACADEMIC OR NON-PROFIT ORGANIZATION NONCOMMERCIAL RESEARCH USE ONLY)

I don’t have enough experience with the software licenses. If I change the model a little bit and train it again from scratch, will I have the right to use it commercially? If so How much change do I have to make?

Thanks in advance.

submitted by /u/salihkaragoz
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