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Andrew M. Dai proslijedio/la je Tweet
Another talk from our team
@GoogleHealth at#NeurIPS2019 My teammates Dale Webster and@lhpeng talking about the challenges in building#ML4H for real-world use, sharing experience beyond model build/evaluation, into integration in real products, real workflows, for real peoplepic.twitter.com/t6ni0lOrJY
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Come see our team's work on medical records: doctor's notes, uncertainty, EHR graphical structure, clinical forecasting, federated and differentially private learning and multivariate timeseries at the ML4H workshop at
#NeurIPS2019 today. Code also athttps://github.com/Google-Health/records-research …Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Unexpected NeurIPS moments: main conference poster sessions reaching capacity.pic.twitter.com/zr5vDsXuHE
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Come to the Google booth at NeurIPS today to hear about some of our recent research and open-sourcing on medical records!https://twitter.com/GoogleAI/status/1204495419754995712 …
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Come see our work on medical record notes modeling with Jonas Kemp and also on learning adaptive learning rates with Zhen Xu and
@Luke_Metz at Baylearn today!pic.twitter.com/GksfPfu48Y
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Come work with us! Google is now accepting applications for the 2020 AI Residency Program, Healthcare! Head to http://g.co/airesidency/healthcare … for more details about the program. Applications close on Sept 17th, 2019! Questions? Go to http://g.co/airesidency/healthcarefaqs …
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Andrew M. Dai proslijedio/la je Tweet
I'm very excited about our work on the use of machine learning and technology for healthcare at
@GoogleAI, & that we've hired David Feinberg (@dtfeinberg) to lead our efforts in this space. Learn more about David and what we're doing in this interview. https://www.blog.google/technology/health/david-feinberg-google-health/amp/ …https://twitter.com/Google/status/1140721535986130945 …
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See our work on learning graphical structure in medical records: Learning Graphical Structure of Electronic Health Records with Transformer for Predictive Healthcare https://arxiv.org/abs/1906.04716 at the Learning and Reasoning with Graph-Structured Representations workshop 3:30-4:30pm.
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Check out our work on medical records at the ICML workshops today! Analyzing the Role of Model Uncertainty for Electronic Health Records https://arxiv.org/abs/1906.03842 at the Uncertainty workshop and Time series modelling by restricting feature interaction at the Time series workshop.
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Come to the Google ICML booth to hear about some of our recent medical records research.https://twitter.com/GoogleAI/status/1138495419292348416 …
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Come see Anna Huang give a talk on our music generation with transformer work at the
#ICLR2019 poster session this morning!pic.twitter.com/Bpwkn8AL7U
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Almost as many hands went up at
#iclrdebate#ICLR2019 for 'not everything can be learnt' vs. 'everything can be learnt'. Surprising for this audience!Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Andrew M. Dai proslijedio/la je Tweet
TPU Pods are the hardware we use at
@GoogleAI for much of our research and production ML models for things like BERT, large-scale image classification, etc. They are now in beta on@GCPCloud. Now you can get your own AI supercomputer by the hour! https://cloud.google.com/tpu/ https://twitter.com/googlecloud/status/1125857086787682305 …
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Andrew M. Dai proslijedio/la je Tweet
At 4:15 at the
#ICLR2019 Google booth,@iamandrewdai will talk about some of Google's health-related research efforts, including fairness in#MachineLearning for health equity, scalable and accurate#DeepLearning with electronic health records and more. We hope you'll join us!Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
In fact we showed that you don't even need additional data to get a gain from pretraining.https://twitter.com/quocleix/status/1098417363639136257 …
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Andrew M. Dai proslijedio/la je Tweet
Since our work on "Semi-supervised sequence learning", ELMo, BERT and others have shown changes in the algorithm give big accuracy gains. But now given these nice results with a vanilla language model, it's possible that a big factor for gains can come from scale. Exciting!https://twitter.com/OpenAI/status/1096092704709070851 …
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Andrew M. Dai proslijedio/la je Tweet
Nice. But the idea of using pretrained language models AFAIK was first proposed by this paper https://arxiv.org/abs/1511.01432 (Also mentioned in Jacob's slides for the history around BERT: https://nlp.stanford.edu/seminar/details/jdevlin.pdf … )
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Andrew M. Dai proslijedio/la je Tweet
Great advice from Olivier Bousquet about being bold and creative in research. From test of time award talk at
#NeurIPS2018pic.twitter.com/Nn7cp3abuz
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If only we had transformer models back then to do bigger LM pretraining!https://twitter.com/quocleix/status/1050825516247179264 …
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The product of our Brain/Calico collaboration is now out!https://twitter.com/sschoenholz/status/1031971463585099781 …
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