ArtificialIntelligenceArticles
International evaluation of an AI system for breast cancer screening https://www.nature.com/articles/s41586-019-1799-6
Chest Radiograph Interpretation with deep learning ✔️
https://pubs.rsna.org/doi/10.1148/radiol.2019191293
Evaluation of an AI system for breast cancer screening ✔️
https://nature.com/articles/s41586-019-1799-6
impressive work https://health.google
https://pubs.rsna.org/doi/10.1148/radiol.2019191293
Evaluation of an AI system for breast cancer screening ✔️
https://nature.com/articles/s41586-019-1799-6
impressive work https://health.google
Radiology
Chest Radiograph Interpretation with Deep Learning Models: Assessment with Radiologist-adjudicated Reference Standards and Population…
Background Deep learning has the potential to augment the use of chest radiography in clinical radiology, but challenges include poor generalizability, spectrum bias, and difficulty comparing across studies. Purpose To develop and evaluate deep learning models…
Learning Symbolic Physics with Graph Networks
Miles D. Cranmer, Rui Xu, Peter Battaglia, Shirley Ho : https://arxiv.org/abs/1909.05862 #GraphNetworks
#MachineLearning #Physics
Miles D. Cranmer, Rui Xu, Peter Battaglia, Shirley Ho : https://arxiv.org/abs/1909.05862 #GraphNetworks
#MachineLearning #Physics
The UNC School of Medicine lab of Zoe McElligott, PhD, found that alcohol consumption is regulated by the activity of a particular set of neurons in a specific brain region, a discovery that could lead to a better understanding of why some casual drinkers. https://www.eurekalert.org/pub_releases/2019-12/uonc-sdk121219.php
EurekAlert!
Scientists discover key neural circuit regulating alcohol consumption
Published in the Journal of Neuroscience, UNC-Chapel Hill research pinpoints a specific neural circuit that when altered caused animal models to drink less alcohol.
From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction
Tanaka et al.: https://papers.nips.cc/paper/9060-from-deep-learning-to-mechanistic-understanding-in-neuroscience-the-structure-of-retinal-prediction
#DeepLearning #Neuroscience #NeurIPS2019
Tanaka et al.: https://papers.nips.cc/paper/9060-from-deep-learning-to-mechanistic-understanding-in-neuroscience-the-structure-of-retinal-prediction
#DeepLearning #Neuroscience #NeurIPS2019
papers.nips.cc
From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction
Electronic Proceedings of Neural Information Processing Systems
The Decade of Deep Learning
https://leogao.dev/2019/12/31/The-Decade-of-Deep-Learning/
https://leogao.dev/2019/12/31/The-Decade-of-Deep-Learning/
Leo Gao
The Decade of Deep Learning
As the 2010’s draw to a close, it’s worth taking a look back at the monumental progress that has been made in Deep Learning in this decade.[1] Driven by the development of ever-more powerful comput
Selective Brain Damage: Measuring the Disparate Impact of Model Compression
Sara Hooker, Aaron Courville, Yann Dauphin, Andrea Frome
https://weightpruningdamage.github.io/
SLIDES
https://drive.google.com/file/d/1VIeV7l9x-KXdT_UdZB54GQxhGHRRQ79T/view
Sara Hooker, Aaron Courville, Yann Dauphin, Andrea Frome
https://weightpruningdamage.github.io/
SLIDES
https://drive.google.com/file/d/1VIeV7l9x-KXdT_UdZB54GQxhGHRRQ79T/view
Deep Neural Network Pruning
Selective Brain Damage
What do pruned deep neural networks forget?
2019’s Top Open Source Machine Learning Projects
https://heartbeat.fritz.ai/2019s-top-open-source-machine-learning-projects-3cd082a02f78
https://heartbeat.fritz.ai/2019s-top-open-source-machine-learning-projects-3cd082a02f78
Fritz ai
2019’s Top Open Source Machine Learning Projects - Fritz ai
In this piece, we’ll look at some of the top open source machine learning projects in 2019, as ranked by MyBridge. Real-Time-Voice-Cloning (13.7K ⭐️) This project is an implementation of the SV2TTS paper with a vocoder that works in real-time.… Continue reading…
keras-ocr
A packaged and flexible version of the CRAFT text detector and Keras CRNN recognition model
GitHub, by Fausto Morales : https://github.com/faustomorales/keras-ocr
#ArtificialIntelligence #DeepLearning #MachineLearning
A packaged and flexible version of the CRAFT text detector and Keras CRNN recognition model
GitHub, by Fausto Morales : https://github.com/faustomorales/keras-ocr
#ArtificialIntelligence #DeepLearning #MachineLearning
GitHub
GitHub - faustomorales/keras-ocr: A packaged and flexible version of the CRAFT text detector and Keras CRNN recognition model.
A packaged and flexible version of the CRAFT text detector and Keras CRNN recognition model. - faustomorales/keras-ocr
Deep Learning for 3D Point Clouds: A Survey
Guo et al.: https://arxiv.org/abs/1912.12033
#DeepLearning #MachineLearning #Robotics
Guo et al.: https://arxiv.org/abs/1912.12033
#DeepLearning #MachineLearning #Robotics
What can the brain teach us about building artificial intelligence?
Dileep George : https://arxiv.org/abs/1909.01561
#ArtificialIntelligence #Neurons #Brain
Dileep George : https://arxiv.org/abs/1909.01561
#ArtificialIntelligence #Neurons #Brain
exBERT- A Visual Analysis Tool to Explore Learned Representations in Transformers Models
Benjamin Hoover, Hendrik Strobelt, Sebastian Gehrmann : https://exbert.net
#NLP #BERT #LanguageModel
Benjamin Hoover, Hendrik Strobelt, Sebastian Gehrmann : https://exbert.net
#NLP #BERT #LanguageModel
Using Nucleus and TensorFlow for DNA Sequencing Error Correction
Colab Notebook, by Google : https://colab.research.google.com/github/google/nucleus/blob/master/nucleus/examples/dna_sequencing_error_correction.ipynb
#ArtificialIntelligence #DNA #DeepLearning
Colab Notebook, by Google : https://colab.research.google.com/github/google/nucleus/blob/master/nucleus/examples/dna_sequencing_error_correction.ipynb
#ArtificialIntelligence #DNA #DeepLearning
Google
dna_sequencing_error_correction.ipynb
Run, share, and edit Python notebooks
Linguistic Knowledge and Transferability of Contextual Representations
Liu et al.: https://arxiv.org/abs/1903.08855
#ArtificialIntelligence #DeepLearning #NLP
Liu et al.: https://arxiv.org/abs/1903.08855
#ArtificialIntelligence #DeepLearning #NLP
arXiv.org
Linguistic Knowledge and Transferability of Contextual Representations
Contextual word representations derived from large-scale neural language models are successful across a diverse set of NLP tasks, suggesting that they encode useful and transferable features of...
[Google Brain Object detection] EfficientDet: Scalable and Efficient Object Detection implementation by Signatrix GmbH
Source code: https://github.com/signatrix/efficientdet
Source code: https://github.com/signatrix/efficientdet
GitHub
GitHub - signatrix/efficientdet: (Pretrained weights provided) EfficientDet: Scalable and Efficient Object Detection implementation…
(Pretrained weights provided) EfficientDet: Scalable and Efficient Object Detection implementation by Signatrix GmbH - signatrix/efficientdet
The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision
Mao et al.: https://arxiv.org/abs/1904.12584
#ArtificialIntelligence #NeuroSymbolic #AIDebate
Mao et al.: https://arxiv.org/abs/1904.12584
#ArtificialIntelligence #NeuroSymbolic #AIDebate
Geometric Capsule Autoencoders for 3D Point Clouds
Nitish Srivastava, Hanlin Goh, Ruslan Salakhutdinov : https://arxiv.org/pdf/1912.03310.pdf
#ArtificialIntelligence #DeepLearning #MachineLearning
Nitish Srivastava, Hanlin Goh, Ruslan Salakhutdinov : https://arxiv.org/pdf/1912.03310.pdf
#ArtificialIntelligence #DeepLearning #MachineLearning
Analyzing and Improving the Image Quality of StyleGAN
Karras et al.:https://arxiv.org/abs/1912.04958
Github: https://github.com/NVlabs/stylegan2
#ArtificialIntelligence #DeepLearning #GenerativeAdversarialNetworks
Karras et al.:https://arxiv.org/abs/1912.04958
Github: https://github.com/NVlabs/stylegan2
#ArtificialIntelligence #DeepLearning #GenerativeAdversarialNetworks
GitHub
GitHub - NVlabs/stylegan2: StyleGAN2 - Official TensorFlow Implementation
StyleGAN2 - Official TensorFlow Implementation. Contribute to NVlabs/stylegan2 development by creating an account on GitHub.
10 ML & NLP Research Highlights of 2019
https://ruder.io/research-highlights-2019/
https://ruder.io/research-highlights-2019/