Austin Corotan

Director of Engineering @Integrated_IP
#AI #MachineLearning #DeepLearning #DataScience

[Austin Corotan @AustinCorotan]

@b_shambam @JoselitoCruz @Siully_ @b_shambam this is our web platform, so all devices are supported

[ODSC (Open Data Science Conference) AI @_odsc]

More businesses, organizations, and startups are talking about machine learning and what it means for business than just about any other technological advance. How do you deploy ML initiatives, then? #DataScience #MachineLearning https://t.co/scnVQdcIFE

[AI at Meta @AIatMeta]

Facebook AI researchers and engineers have developed a new method for using deep learning and weakly supervised training to predict road networks from commercially available high-resolution satellite imagery. https://t.co/HOL1KfdsAB

[AI at Meta @AIatMeta]

Congrats to our AI team for matching the top GLUE benchmark performance! We believe strongly in open & collaborative research and thank @GoogleAI for releasing BERT. It led to RoBERTa, our robustly optimized system that was trained longer, on more data. https://t.co/FAKAGboEHl

[Google AI @GoogleAI]

Check out Evolved Transformer, a new state-of-the-art Transformer architecture for natural language processing, derived using a fresh approach to evolution-based neural architecture search. Learn all about it here → https://t.co/WsBG7cRG9n

[Gerard de Melo @gdm3000]

Amazing!! Deep Learning-based NLP techniques are going to revolutionize the way we write software. Here's Deep TabNine, a GPT-2 model trained on around 2 million files from GitHub. Details at https://t.co/8J2v8Ns7n4 #nlproc

[AI at Meta @AIatMeta]

We recently open-sourced a state-of-the-art deep learning recommendation model (DLRM) implemented using @PyTorch and @caffe2ai: https://t.co/kQL0pD8Akf

[Austin Corotan @AustinCorotan]

“AI’s Growing Role in Musical Composition” by Synced https://t.co/cGX7J5azb5

[Google DeepMind @GoogleDeepMind]

In our new paper https://t.co/Rhm94rOuX5 we show that GANs can be harnessed for unsupervised representation learning, with state-of-the-art results on ImageNet. Reconstructions, as shown below, tend to emphasise high-level semantics over pixel-level details.