From Facebook AI:

ReAgent, the most comprehensive and modular open source platform for creating AI-based reasoning systems, and the first to include policy evaluation to incorporate offline feedback to improve models.
https://ai.facebook.com/blog/open-sourcing-reagent-a-platform-for-reasoning-systems/

Quantized Reinforcement Learning (QUARL)
Srivatsan Krishnan, Sharad Chitlangia, Maximilian Lam, Zishen Wan, Aleksandra Faust, Vijay Janapa Reddi : https://arxiv.org/abs/1910.01055
Code: https://github.com/harvard-edge/quarl
#DeepLearning #ReinforcementLearning #Quantization #Robotics

Minimal implementation of a Neural Architecture Search system
https://github.com/nicklashansen/minimal-nas

Active Learning for Graph Neural Networks via Node Feature Propagation
Wu et al.: https://arxiv.org/abs/1910.07567
#ArtificialIntelligence #GraphNeuralNetworks #MachineLearning

Deep Reinforcement Learning meets Graph Neural Networks: An optical network routing use case
Almasan et al.: https://arxiv.org/abs/1910.07421
#DeepLearning #ReinforcementLearning #GraphNeuralNetworks

Machine learning has the potential to improve different steps of the radiology workflow including order scheduling and triage, clinical decision support systems, detection and interpretation of findings, postprocessing and dose estimation, examination quality control, and radiology reporting.
https://pubs.rsna.org/doi/10.1148/radiol.2018171820

Bias-Resilient Neural Network
Adeli et al.: https://arxiv.org/abs/1910.03676
#ArtificialIntelligence #DeepLearning #NeuralNetworks

Precise measurement of quantum observables with neural-network estimators
Torlai et al.: https://arxiv.org/abs/1910.07596
#ArtificialIntelligence #QuantumPhysics #NeuralNetworks

"Restoring ancient text using deep learning: a case study on Greek epigraphy"
Assael et al.: https://arxiv.org/abs/1910.06262
Code: https://github.com/sommerschield/ancient-text-restoration
#ArtificialIntelligence #DeepLearning #NeuralNetworks

DeepGCNs: Making GCNs Go as Deep as CNNs

https://deepai.org/publication/deepgcns-making-gcns-go-as-deep-as-cnns?fbclid=IwAR2edqmWo5uKSGybcgRWW43ov-03resk_as2EoJ52nzeaF_3jSnnV3bxH1o

#DeepAI #neuralnetworks #CNNs ##GCNs

Deep networks work by learning complex, often hierarchical internal representations of input data. These form a kind of functional language the network uses to describe the data.

Language can emerge from tasks like object recognition: has pointy ears, whiskers, tail => cat.

This relates to Wittgenstein’s "language-game" in Philosophical Investigations, where a functional language emerge from simple tasks before defining a vocabulary.

The visual vocabulary of a convolutional neural network seems to emerge from low level features such as edges and orientations, and builds up textures, patterns and composites, … and builds up even further into complete objects: houses, dogs, etc.

Source: NeurIPS 2018“Unsupervised Deep Learning” Tutorial – Part 1 by Alex Graves - https://media.neurips.cc/Conferences/NIPS2018/Slides/Deep_Unsupervised_Learning.pdf

#artificialintelligence #deeplearning #language #machinelearning

Yann LeCun : Interview on France-5 with Ali Baddou.
https://www.youtube.com/watch?v=AnQBeLJspVQ&feature=youtu.be&fbclid=IwAR0x-Dwekjb_PsmLDyi1QDWo5BREfK0KyxpypaL_3pQ2G_T6dOfr6Tand0E

Use jupyter notebook like feature in Visual Studio code
https://towardsdatascience.com/jupyter-notebook-in-visual-studio-code-3fc21a36fe43

Machine Learning and Data Science Applications in Industry
https://github.com/firmai/industry-machine-learning/blob/master/README.md

"Deep Neural Networks as Scientific Models" by Radoslaw Cichy & Daniel Kaiser in Trends in CogSci argues that deep learning should be used as models of human cognition.

"First, given the current level of theory development and the need to trade-off model desiderata, we should embrace DNNs as one of many diverse kinds of useful models. Second, through their predictive power DNNs have rich potential as tools for scientific research and application. Third, we should use DNNs' explanatory power for theorisation, but make explicit what type of explanation is at stake to allow fair assessment and criticism. Finally, the exploratory power of DNNs deserves our heightened attention."




https://www.cell.com/trends/cognitive-sciences/fulltext/S1364-6613(19)30034-8#%20

Hair-GANs: Recovering 3D Hair Structure from a Single Image
Meng Zhang Youyi Zheng : https://arxiv.org/pdf/1811.06229.pdf
#Hair #DeepLearning #GenerativeAdversarialNetworks

"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

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

Securing machine learning models against adversarial attacks
https://www.elementai.com/news/2019/securing-machine-learning-models-against-adversarial-attacks