ML Papers Explained | A.I. Socratic Circles

@AISC_TO

A community of intellectually curious individuals, centred around technical review and discussion of advances in machine learning.

Toronto & Online
Vrijeme pridruživanja: rujan 2018.

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  1. Prikvačeni tweet
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  2. [Use neural network potentials in drug-protein binding simulations] Simulating protein–ligand binding with neural network potentials (submitted by Amir Feizpour)

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  3. Join us tomorrow for our AISC Abstract and Social Night:

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  4. Top 10 AI, Machine Learning Research Articles to know

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  5. ‘Federated Learning’ ‘Differential Privacy’ ‘Homomorphic Encryption’. Heard all these buzzwords and want to put it all in context? Join us on February 8 & Feb 15 for two sessions on privacy-preserving ML to build privacy-preserving ML applications!

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  6. Wondering how the privacy of training/production data is maintained by ML models? Join us on February 8 & Feb 15 on privacy-preserving NLP, where we take a journey through the state-of-the-art in this field, with a hands-on exercises.

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  7. A Deep Structural Model for Analyzing Correlated Multivariate Time Series (submitted by Jiri Stodulka)

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  8. When fraudulent activity is a fraction of your transaction data, you need to be sure your algorithm can find it Register for our workshop to learn more:

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  9. Google Introduces Flax: A Neural Network Library for JAX (submitted by Rouzbeh Afrasiabi & Willy Rempel)

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  10. Ludwig is a toolbox built on top of TensorFlow that allows to train and test deep learning models without the need to write code. (submitted by Rouzbeh Afrasiabi)

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  11. Adversarial networks can help your cyber analytics team train better models. Last day to register at 20% off for our workshop:

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  12. Alink is the Machine Learning algorithm platform based on Flink, developed by the PAI team of Alibaba computing platform.

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  13. Angle, A Flexible and Powerful Parameter Server for large-scale machine learning (submitted by Rouzbeh Afrasiabi)

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  14. DDSP: Differentiable Digital Signal Processing (submitted by Yenson Lau)

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  15. An Intrusion detection system can be trained using imaginary intruders. We can show you how Watch the workshop overview:

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  16. Reformer: The Efficient Transformer (submitted by Amir Feizpour)

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  17. Join us tomorrow online:

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  18. TinyBERT for Search: 10x faster and 20x smaller than BERT (submitted by Amir Feizpour)

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  19. if you have been following in any capacity, could you please take 3 minutes to fill out this survey? it'll help us make AISC better immensely

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  20. Augment your network data with intrusion events that seem so real, they fit like a glove. Watch the workshop overview:

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  21. A DoS classifier trained on university data isn't going to perform well on an enterprise network. Find out how to improve it. Watch the workshop overview:

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