Audio signals generated by the human body (e.g., sighs, breathing, heart, digestion, vibration sounds) have routinely been used by clinicians as indicators to diagnose disease or assess disease progression. Until recently, such signals were usually ...

The escalating crisis of COVID-19 has put people all over the world in danger. Owing to the high contagion rate of the virus, COVID-19 cases continue to increase globally. To further suppress the threat of the COVID-19 pandemic and minimize its damage, ...

The rapid spread of the Coronavirus SARS-2 is a major challenge that led almost all governments worldwide to take drastic measures to respond to the tragedy. Chief among those measures is the massive lockdown of entire countries and cities, which beyond ...

Realistic simulation of a massive amount of human mobility data is of great use in epidemic spreading modeling and related health policy-making. Existing solutions for mobility simulation can be classified into two categories: model-based methods and ...

People increasingly search online for answers to their medical questions but the rate at which medical questions are asked online significantly exceeds the capacity of qualified people to answer them. This leaves many questions unanswered or ...

Understanding human language in real world scenarios involves not just natural language processing but also speech, vision, knowledge graphs, user modeling, and other AI techniques. Doing this at Google scale involves planetary-scale cloud computing as ...

In this talk, we will first discuss multimodal ML for video content analysis. Videos typically have data in multiple modalities like audio, video, and text (captions). Understanding and modeling the interaction between different modalities is key for ...

In the midst of COVID-19, a global economic crisis is threatening the livelihoods of small business owners everywhere. In ordinary times, 50 percent of small businesses go out of business in the first 5 years. In today's extraordinary times, nearly 7.5 ...

Coinbase's mission is "to create an open financial system for the world". This presentation serves as an overview of our efforts in building the state-of-the-art machine learning technology for the fast-evolving crypto economy, which follows a prototype,...

There are many opportunities to pursue AI and ML in the financial domain. In this talk, I will overview several research directions we are pursuing in engagement with the lines of business, ranging from data and knowledge, learning from experience, ...

In this tutorial we show how to build deep learning recommendation systems and resolve the associated interpretability, integrity and privacy challenges. We start with an overview of the PyTorch framework, features that it offers and a brief review of ...

Tackling issues of bias and fairness when building and deploying data science systems has received increased attention from the research community in recent years, yet a lot of the research has focused on theoretical aspects and very limited set of ...

Automated machine learning (AutoML) offers the promise of translating raw data into accurate predictions with just a few lines of code. Rather than relying on human time/effort and manual experimentation, models can be improved by simply letting the ...

Anomaly detection is an important problem that has been well-studied within diverse research areas and application domains. A robust anomaly detection system identifies rare events and patterns in the absence of labelled data. The identified patterns ...

Explore new techniques in Microsoft's open source library called DeepSpeed, which advances large model training by improving scale, speed, cost, and usability, unlocking the ability to train 100-billion-parameter models. DeepSpeed is compatible with ...

The lines between data science (DS), machine learning (ML), deep learning (DL), and data mining continue to be blurred and removed. This is great as it ushers in vast amounts of capabilities, but it brings increased complexity and a vast number of tools/...

We present Neural Structured Learning (NSL) in TensorFlow [2], a new learning paradigm to train neural networks by leveraging structured signals in addition to feature inputs. Structure can be explicit as represented by a graph, or implicit, either ...

Research in Machine Learning (ML) for classification tasks has been primarily guided by metrics that derive from a confusion matrix (e.g. accuracy, precision and recall). Several works have highlighted that this has lead to training practices that ...

Automated ML is an emerging field in Machine Learning that helps developers and new data scientists with little data science knowledge build Machine Learning models and solutions without understanding the complexity of Learning Algorithm selection, and ...

This tutorial focuses on how to build Question Answering (QA) syetems based on the Never-Ending Learning (NEL) approach. NEL systems can be roughly described as computer systems that learn over time to become better in solving a specific task. Different ...