ArtificialIntelligenceArticles
2.97K subscribers
1.64K photos
9 videos
5 files
3.86K links
for who have a passion for -
1. #ArtificialIntelligence
2. Machine Learning
3. Deep Learning
4. #DataScience
5. #Neuroscience

6. #ResearchPapers

7. Related Courses and Ebooks
Download Telegram
Transformers: State-of-the-art Natural Language Processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Jamie Brew : https://arxiv.org/abs/1910.03771
#Transformers #NaturalLanguageProcessing #PyTorch #TensorFlow
Visual example of loss function space for Object Detection on Pedestrian Detection Database with SSD300 model.

That's why training model can be tough, cause it's almost the same as climbing on the Everest and jumping into the Mariana Trench.

And that's why we are making course on Object Detection, to help understand such moments - subscribe https://upscri.be/vg7ilp
Using CNN (Convolutional Neural Network) to predict about Chest X-ray images that whether a person has Pneumonia or he is Normal. Data set is 1gb in size and available on kaggle to download.
Link to the dataset:
https://lnkd.in/dnxheZU
Python Script of the model:
https://lnkd.in/dJfc_yS
Congrats to Dr. Rahaf Aljundi on receiving her PhD from KULeuven (advised by Prof. Tinne Tuytelaars). I am happy about our fruitful collaboration on continual learning and that it was a part of her well-deserved PhD.

Please see her PhD thesis in the link below; seasoned continual learning research ranging from the use of unlabeled data leveraged by MAS (our ECCV18 collaboration) that is also inspired from Hebbian learning theory, use of language, ACCV18), later her work on task free continual learning and making it more online at CVPR19 and NeurIPS19 (at MILA).

https://arxiv.org/abs/1910.02718
PyTorch 1.3 is now available with iOS / Android support, quantization, named tensors, type promotion, and more: bit.ly/2OCfNpR
Practical Posterior Error Bounds from Variational Objectives
Jonathan H. Huggins, Mikołaj Kasprzak, Trevor Campbell, Tamara Broderick : https://arxiv.org/abs/1910.04102
#MachineLearning #StatisticsTheory #VariationalInference