Towards AutoML in the presence of Drift: first results. arxiv.org/abs/1907.10772
Andrew Ng: How to Choose Your First AI Project
https://hbr.org/2019/02/how-to-choose-your-first-ai-project
https://hbr.org/2019/02/how-to-choose-your-first-ai-project
Harvard Business Review
How to Choose Your First AI Project
Tapping the power of AI technologies requires customizing them to your business context. The purpose of your first 1-2 pilot projects is only partly to create value; more importantly, the success of these projects will help convince stakeholders to invest…
Beautifully drawn notes on the deeplearning.ai specialization on Coursera, by Tess Ferrandez.
https://www.slideshare.net/TessFerrandez/notes-from-coursera-deep-learning-courses-by-andrew-ng
https://www.slideshare.net/TessFerrandez/notes-from-coursera-deep-learning-courses-by-andrew-ng
SlideShare
Notes from Coursera Deep Learning courses by Andrew Ng
Notes from Coursera Deep Learning courses by Andrew Ng - Download as a PDF or view online for free
How Facebook is Using Machine Learning to Map the World Population
https://www.visualcapitalist.com/facebook-machine-learning-world-population-map/
https://www.visualcapitalist.com/facebook-machine-learning-world-population-map/
Visual Capitalist
How Facebook is Using Machine Learning to Map the World Population
Machine learning technology is allowing researchers at Facebook to map the world population in unprecedented detail.
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"Neural Point-Based Graphics" - new paper about realistic neural rendering from our lab (Samsung AI Center, Moscow)! We only use pointclouds as proxy for free-viewpoint rendering and do not reconstruct meshes.
youtu.be/7s3BYGok7wU
dmitryulyanov.github.io/neural_point_based_graphics
arxiv.org/abs/1906.08240
youtu.be/7s3BYGok7wU
dmitryulyanov.github.io/neural_point_based_graphics
arxiv.org/abs/1906.08240
Top 8 trends from ICLR 2019
Overview of trends on #ICLR2019:
1. Inclusivity
2. Unsupervised representation learning & transfer learning
3. Retro ML
4. RNN is losing its luster with researchers
5. GANs are still going on strong
6. The lack of biologically inspired deep learning
7. Reinforcement learning is still the most popular topic by submissions
8. Most accepted papers will be quickly forgotten
Link: https://huyenchip.com/2019/05/12/top-8-trends-from-iclr-2019.html
Overview of trends on #ICLR2019:
1. Inclusivity
2. Unsupervised representation learning & transfer learning
3. Retro ML
4. RNN is losing its luster with researchers
5. GANs are still going on strong
6. The lack of biologically inspired deep learning
7. Reinforcement learning is still the most popular topic by submissions
8. Most accepted papers will be quickly forgotten
Link: https://huyenchip.com/2019/05/12/top-8-trends-from-iclr-2019.html
Huyenchip
Top 8 trends from ICLR 2019
[Twitter thread] Disclaimer: This post doesn’t reflect the view of any of the organizations I’m associated with and is probably peppered with my personal and...
Paper on RoBERTa, the current holder of the pole position on the GLUE leaderboard ( https://gluebenchmark.com/leaderboard/ )
https://arxiv.org/abs/1907.11692
https://arxiv.org/abs/1907.11692
Gluebenchmark
GLUE Benchmark
The General Language Understanding Evaluation (GLUE) benchmark is a collection of resources for training, evaluating, and analyzing natural language understanding systems
RoBERTa: A Robustly Optimized BERT Pretraining Approach
Liu et al.: https://arxiv.org/abs/1907.11692
#bert #naturallanguageprocessing #unsupervisedlearning
Liu et al.: https://arxiv.org/abs/1907.11692
#bert #naturallanguageprocessing #unsupervisedlearning
arXiv.org
RoBERTa: A Robustly Optimized BERT Pretraining Approach
Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging. Training is computationally expensive, often done on private...
Now AI can also be used to identify fake text.
These researchers have released a tool called has GLTR - Giant Language Model Test Room
1) Test yourself here GLTR tool page: https://gltr.io/
2) GitHub link: https://github.com/HendrikStrobelt/detecting-fake-text
3) Paper link: https://arxiv.org/pdf/1906.04043.pdf
https://t.iss.one/ArtificialIntelligenceArticles
These researchers have released a tool called has GLTR - Giant Language Model Test Room
1) Test yourself here GLTR tool page: https://gltr.io/
2) GitHub link: https://github.com/HendrikStrobelt/detecting-fake-text
3) Paper link: https://arxiv.org/pdf/1906.04043.pdf
https://t.iss.one/ArtificialIntelligenceArticles
Deep Learning with MATLAB: Transfer Learning in 10 Lines of MATLAB Code
https://nl.mathworks.com/videos/deep-learning-with-matlab-transfer-learning-in-10-lines-of-matlab-code-1487714838381.html
https://nl.mathworks.com/videos/deep-learning-with-matlab-transfer-learning-in-10-lines-of-matlab-code-1487714838381.html
Mathworks
Deep Learning with MATLAB: Transfer Learning in 10 Lines of MATLAB Code Video
"Learn how to use transfer learning in MATLAB to re-train deep learning networks created by experts for your own data or task. "
Machine Learning Top 10 Articles for the Past Month (v.July 2019)
https://medium.com/@Mybridge/machine-learning-top-10-articles-for-the-past-month-v-july-2019-178436f99201 https://t.iss.one/ArtificialIntelligenceArticles
https://medium.com/@Mybridge/machine-learning-top-10-articles-for-the-past-month-v-july-2019-178436f99201 https://t.iss.one/ArtificialIntelligenceArticles
Low-Rank Matrix Completion: A Contemporary Survey. arxiv.org/abs/1907.11705
DEAM: Accumulated Momentum with Discriminative Weight for Stochastic Optimization. arxiv.org/abs/1907.11307
Recurrent Aggregation Learning for Multi-View Echocardiographic Sequences Segmentation. arxiv.org/abs/1907.11292