"The Visual Task Adaptation Benchmark"
Zhai et al.: https://arxiv.org/abs/1910.04867
GitHub: https://github.com/google-research/task_adaptation
#ArtificialIntelligence #DeepLearning #MachineLearning
Zhai et al.: https://arxiv.org/abs/1910.04867
GitHub: https://github.com/google-research/task_adaptation
#ArtificialIntelligence #DeepLearning #MachineLearning
arXiv.org
A Large-scale Study of Representation Learning with the Visual...
Representation learning promises to unlock deep learning for the long tail of vision tasks without expensive labelled datasets. Yet, the absence of a unified evaluation for general visual...
Deep Neural Networks Improve Radiologists’ Performance in Breast Cancer Screening
Deep learning #AI of > 1 M mammograms: "a hybrid model, averaging the probability of malignancy predicted by a radiologist with a prediction of our neural network, is more accurate than either of the two separately."
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8861376
Deep learning #AI of > 1 M mammograms: "a hybrid model, averaging the probability of malignancy predicted by a radiologist with a prediction of our neural network, is more accurate than either of the two separately."
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8861376
Securing machine learning models against adversarial attacks
https://www.elementai.com/news/2019/securing-machine-learning-models-against-adversarial-attacks
https://www.elementai.com/news/2019/securing-machine-learning-models-against-adversarial-attacks
Element AI
Securing machine learning models against adversarial attacks
Adversarial defences are techniques used to protect against adversarial attacks. The arms race between adversarial attacks and defences is intensifying.
[Tutorial] Build a Gender Classifier for Live Webcam Stream using Tensorflow and OpenCV
https://hackernoon.com/gender-classifier-with-tensorflow-and-opencv-rg1ye3weq
https://hackernoon.com/gender-classifier-with-tensorflow-and-opencv-rg1ye3weq
Hackernoon
[Tutorial] Build a Gender Classifier for Live Webcam Stream using Tensorflow and OpenCV
Training a Neural Network from scratch suffers two main problems. First, a very large, classified input dataset is needed so that the Neural Network can learn the different features it needs for the classification.
Rachael Tatman - Put down the deep learning: When not to use neural networks and what to do instead
https://www.youtube.com/watch?time_continue=2&v=qw5dBdTXLEs
https://www.youtube.com/watch?time_continue=2&v=qw5dBdTXLEs
YouTube
Rachael Tatman - Put down the deep learning: When not to use neural networks and what to do instead
"Speaker: Rachael Tatman The deep learning hype is real, and the Python ecosystem makes it easier than ever to neural networks to everything from speech reco...
Activation maps for deep learning models in a few lines of code
https://www.kdnuggets.com/2019/10/activation-maps-deep-learning-models-lines-code.html
https://www.kdnuggets.com/2019/10/activation-maps-deep-learning-models-lines-code.html
KDnuggets
Activation maps for deep learning models in a few lines of code - KDnuggets
We illustrate how to show the activation maps of various layers in a deep CNN model with just a couple of lines of code.
Working memory revived in older adults by synchronizing rhythmic brain circuits
https://www.nature.com/articles/s41593-019-0371-x.epdf
https://www.nature.com/articles/s41593-019-0371-x.epdf
Nature Neuroscience
Working memory revived in older adults by synchronizing rhythmic brain circuits
The authors develop a noninvasive stimulation protocol to restore neural synchronization patterns and improve working memory in older humans, contributing to groundwork for future drug-free therapeutics targeting age-related cognitive decline.
The paper "Learning Predict-and-Simulate Policies From Unorganized Human Motion Data" is available here:
https://mrl.snu.ac.kr/publications/ProjectICC/ICC.html
https://mrl.snu.ac.kr/publications/ProjectICC/ICC.html
Materials of the Summer school on Deep learning and Bayesian methods 2019
GitHub : https://github.com/bayesgroup/deepbayes-2019
#ArtificialIntelligence #DeepLearning #Bayesian
GitHub : https://github.com/bayesgroup/deepbayes-2019
#ArtificialIntelligence #DeepLearning #Bayesian
Machine Learning for Intelligent Systems: Cornell CS4780/CS5780
Lectures: https://www.youtube.com/playlist?list=PLl8OlHZGYOQ7bkVbuRthEsaLr7bONzbXS
Course Website: https://www.cs.cornell.edu/courses/cs4780/2018fa/syllabus/index.html
Lectures: https://www.youtube.com/playlist?list=PLl8OlHZGYOQ7bkVbuRthEsaLr7bONzbXS
Course Website: https://www.cs.cornell.edu/courses/cs4780/2018fa/syllabus/index.html
Harnessing the Power of Infinitely Wide Deep Nets on Small-data Tasks
Arora et al.: https://arxiv.org/abs/1910.01663
#RandomForests #MachineLearning #DeepLearning
Arora et al.: https://arxiv.org/abs/1910.01663
#RandomForests #MachineLearning #DeepLearning
The Illustrated GPT-2 (Visualizing Transformer Language Models)
Blog by Jay Alammar : https://jalammar.github.io/illustrated-gpt2/
#BERT #Transformer #ArtificialIntelligence
Blog by Jay Alammar : https://jalammar.github.io/illustrated-gpt2/
#BERT #Transformer #ArtificialIntelligence
jalammar.github.io
The Illustrated GPT-2 (Visualizing Transformer Language Models)
Discussions:
Hacker News (64 points, 3 comments), Reddit r/MachineLearning (219 points, 18 comments)
Translations: Simplified Chinese, French, Korean, Russian, Turkish
This year, we saw a dazzling application of machine learning. The OpenAI GPT…
Hacker News (64 points, 3 comments), Reddit r/MachineLearning (219 points, 18 comments)
Translations: Simplified Chinese, French, Korean, Russian, Turkish
This year, we saw a dazzling application of machine learning. The OpenAI GPT…
Critique of Paper by "Deep Learning Conspiracy" (Nature 521 p 436)
https://people.idsia.ch/~juergen/deep-learning-conspiracy.html
#deeplearning #MachineLearning #ArtificialIntelligence
https://people.idsia.ch/~juergen/deep-learning-conspiracy.html
#deeplearning #MachineLearning #ArtificialIntelligence
Meta-Learning Deep Energy-Based Memory Models
Bartunov et al.: https://arxiv.org/abs/1910.02720
#MachineLearning #MetaLearning #EnergyBasedMemoryModels
Bartunov et al.: https://arxiv.org/abs/1910.02720
#MachineLearning #MetaLearning #EnergyBasedMemoryModels
Announcing NeurIPS Meetups!
By Neural Information Processing Systems Conference : https://medium.com/@NeurIPSConf/announcing-neurips-meetups-44b2385c67a2
#NeurIPS #Meetup #NeurIPS2019
By Neural Information Processing Systems Conference : https://medium.com/@NeurIPSConf/announcing-neurips-meetups-44b2385c67a2
#NeurIPS #Meetup #NeurIPS2019
GPyTorch
Gaussian processes for modern machine learning systems.
GitHub: https://github.com/cornellius-gp/gpytorch
Website: https://gpytorch.ai
#PyTorch #NeuralNetworks #MachineLearning
@ArtificialIntelligenceArticles
Gaussian processes for modern machine learning systems.
GitHub: https://github.com/cornellius-gp/gpytorch
Website: https://gpytorch.ai
#PyTorch #NeuralNetworks #MachineLearning
@ArtificialIntelligenceArticles
Deep learning method transforms shapes
https://www.sciencedaily.com/releases/2019/10/191018125516.htm
https://www.sciencedaily.com/releases/2019/10/191018125516.htm
ScienceDaily
Deep learning method transforms shapes
Called LOGAN, the deep neural network, i.e., a machine of sorts, can learn to transform the shapes of two different objects, for example, a chair and a table, in a natural way, without seeing any paired transforms between the shapes.
Information Closure Theory of Consciousness
https://arxiv.org/pdf/1909.13045.pdf
@ArtificialIntelligenceArticles
https://arxiv.org/pdf/1909.13045.pdf
@ArtificialIntelligenceArticles
PhD positions in Deep Learning for Satellite Image Analysis at TU Berlin
The Remote Sensing Image Analysis (RSiM) group at the Faculty of Electrical Engineering and Computer Science, Technische Universität Berlin, Germany is looking for highly motivated PhD candidates. The research of the PhD candidates will aim at developing innovative machine learning techniques (with a special focus on deep learning) for the analysis of big data from space.
The main topics include:
developing deep neural network models that can overcome the data imbalance problems for satellite image classification; and
developing active learning methods that are applicable to the designed deep neural networks.
The successful candidates will begin on January 2020 and will have a duration of 3 years. MSc degree is required in computer engineering or computer science with experience in computer vision, deep learning for image understanding. Very good command of German and English is required.
Interested candidates are requested to email their CVs to Prof. Begum Demir ([email protected]).
The Remote Sensing Image Analysis (RSiM) group at the Faculty of Electrical Engineering and Computer Science, Technische Universität Berlin, Germany is looking for highly motivated PhD candidates. The research of the PhD candidates will aim at developing innovative machine learning techniques (with a special focus on deep learning) for the analysis of big data from space.
The main topics include:
developing deep neural network models that can overcome the data imbalance problems for satellite image classification; and
developing active learning methods that are applicable to the designed deep neural networks.
The successful candidates will begin on January 2020 and will have a duration of 3 years. MSc degree is required in computer engineering or computer science with experience in computer vision, deep learning for image understanding. Very good command of German and English is required.
Interested candidates are requested to email their CVs to Prof. Begum Demir ([email protected]).