Grammarly AI: The sweet spot of deep learning and natural language processing
https://bdtechtalks.com/2019/10/17/grammarly-ai-assistant-grammar-checker/
https://bdtechtalks.com/2019/10/17/grammarly-ai-assistant-grammar-checker/
TechTalks
Grammarly AI: The sweet spot of deep learning and natural language processing
Grammarly has found a niche suitable for the narrow capabilities of deep learning, and grown itself from a small app into the leading AI-based grammar checker.
16. Appendix: Mathematics for Deep Learning¶
https://d2l.ai/chapter_appendix_math/index.html
https://d2l.ai/chapter_appendix_math/index.html
Q8BERT: Quantized 8Bit BERT
Zafrir et al.: https://arxiv.org/abs/1910.06188
#NaturalLanguageProcessing #NLP #Transformer
Zafrir et al.: https://arxiv.org/abs/1910.06188
#NaturalLanguageProcessing #NLP #Transformer
arXiv.org
Q8BERT: Quantized 8Bit BERT
Recently, pre-trained Transformer based language models such as BERT and GPT, have shown great improvement in many Natural Language Processing (NLP) tasks. However, these models contain a large...
How to Program UMAP from Scratch
And how to improve UMAP
By Nikolay Oskolkov : https://towardsdatascience.com/how-to-program-umap-from-scratch-e6eff67f55fe
#MachineLearning #DataScience #Bioinformatics
And how to improve UMAP
By Nikolay Oskolkov : https://towardsdatascience.com/how-to-program-umap-from-scratch-e6eff67f55fe
#MachineLearning #DataScience #Bioinformatics
Medium
How to Program UMAP from Scratch
And how to improve UMAP
Debate between Facebook's head of AI, Yann LeCun and Prof. Gary Marcus at New York University
What a great debate! https://youtu.be/aCCotxqxFsk
@ArtificialIntelligenceArticles
#ArtificialIntelligence #DeepLearning
What a great debate! https://youtu.be/aCCotxqxFsk
@ArtificialIntelligenceArticles
#ArtificialIntelligence #DeepLearning
YouTube
Artificial Intelligence Debate - Yann LeCun vs. Gary Marcus - Does AI Need More Innate Machinery?
Debate between Facebook's head of AI, Yann LeCun and Prof. Gary Marcus at New York University.The debate was moderated by Prof. David Chalmers. Recorded: Oct...
The schedule is almost complete for NeurIPS 2019
https://neurips.cc/Conferences/2019/ScheduleMultitrack
https://neurips.cc/Conferences/2019/ScheduleMultitrack
neurips.cc
NeurIPS 2019
NeurIPS Website
SocialIQA: Commonsense Reasoning about Social Interactions
Sap et al.: https://arxiv.org/abs/1904.09728
#Commonsense #MachineLearning #Reasoning
Sap et al.: https://arxiv.org/abs/1904.09728
#Commonsense #MachineLearning #Reasoning
arXiv.org
SocialIQA: Commonsense Reasoning about Social Interactions
We introduce Social IQa, the first largescale benchmark for commonsense reasoning about social situations. Social IQa contains 38,000 multiple choice questions for probing emotional and social...
ICCV19 Best Paper Award
SinGAN: Learning a Generative Model from a Single Natural Image
"We introduce SinGAN, an unconditional generative model that can be learned from a single natural image. Our model is trained to capture the internal distribution of patches within the image, and is then able to generate high quality, diverse samples that carry the same visual content as the image"
Download Project Paper & Code Here
https://bit.ly/SinGAN
SinGAN: Learning a Generative Model from a Single Natural Image
"We introduce SinGAN, an unconditional generative model that can be learned from a single natural image. Our model is trained to capture the internal distribution of patches within the image, and is then able to generate high quality, diverse samples that carry the same visual content as the image"
Download Project Paper & Code Here
https://bit.ly/SinGAN
How Not to Fail Your Machine Learning Interview
https://medium.com/ai%C2%B3-theory-practice-business/how-not-to-fail-your-machine-learning-interview-9545a67b35bc
https://medium.com/ai%C2%B3-theory-practice-business/how-not-to-fail-your-machine-learning-interview-9545a67b35bc
ICCV 2019 and CoRL 2019 Announce Best Papers; DeepMind AlphaStar Reaches ‘Grandmaster Level’
https://www.google.com/amp/s/syncedreview.com/2019/11/03/iccv-2019-and-corl-2019-announce-best-papers-deepmind-alphastar-reaches-grandmaster-level/amp/
https://www.google.com/amp/s/syncedreview.com/2019/11/03/iccv-2019-and-corl-2019-announce-best-papers-deepmind-alphastar-reaches-grandmaster-level/amp/
Synced
ICCV 2019 and CoRL 2019 Announce Best Papers; DeepMind AlphaStar Reaches ‘Grandmaster Level’
Synced Global AI Weekly November 3rd
Mixed Pooling Multi-View Attention Autoencoder for Representation Learning in Healthcare. https://arxiv.org/abs/1910.06456
Supercomputer analyzes web traffic across entire internet
https://news.mit.edu/2019/supercomputer-analyzes-web-traffic-across-entire-internet-1028
https://news.mit.edu/2019/supercomputer-analyzes-web-traffic-across-entire-internet-1028
MIT News | Massachusetts Institute of Technology
Supercomputer analyzes web traffic across entire internet
Using the MIT SuperCloud and the MIT Lincoln Laboratory Supercomputing Center, researchers have developed a model that captures what web traffic looks like around the world on a given day, to be used as a measurement tool for internet and network research.
Generalization through Memorization: Nearest Neighbor Language Models
Khandelwal et al.: https://arxiv.org/abs/1911.00172
#ArtificialIntelligence #DeepLearning #NeuralNetworks
Khandelwal et al.: https://arxiv.org/abs/1911.00172
#ArtificialIntelligence #DeepLearning #NeuralNetworks
arXiv.org
Generalization through Memorization: Nearest Neighbor Language Models
We introduce $k$NN-LMs, which extend a pre-trained neural language model (LM) by linearly interpolating it with a $k$-nearest neighbors ($k$NN) model. The nearest neighbors are computed according...
Learning Neural Networks with Adaptive Regularization
Han Zhao, Yao-Hung Hubert Tsai, Ruslan Salakhutdinov, Geoffrey J. Gordon : https://arxiv.org/abs/1907.06288
Code: https://github.com/yaohungt/Adaptive-Regularization-Neural-Network
#ArtificialIntelligence #NeuralNetworks #NeurIPS2019
Han Zhao, Yao-Hung Hubert Tsai, Ruslan Salakhutdinov, Geoffrey J. Gordon : https://arxiv.org/abs/1907.06288
Code: https://github.com/yaohungt/Adaptive-Regularization-Neural-Network
#ArtificialIntelligence #NeuralNetworks #NeurIPS2019
arXiv.org
Learning Neural Networks with Adaptive Regularization
Feed-forward neural networks can be understood as a combination of an
intermediate representation and a linear hypothesis. While most previous works
aim to diversify the representations, we...
intermediate representation and a linear hypothesis. While most previous works
aim to diversify the representations, we...
Seeing What a GAN Cannot Generate
Bau et al.: https://arxiv.org/abs/1910.11626
#ArtificialIntelligence #DeepLearning #GenerativeAdversarialNetworks
Bau et al.: https://arxiv.org/abs/1910.11626
#ArtificialIntelligence #DeepLearning #GenerativeAdversarialNetworks