Defending Against Neural Fake News
Zellers et al.: https://arxiv.org/abs/1905.12616
#ArtificialIntelligence #DeepLearning #Society

Very proud and enthusiastic to contribute to this project!


https://www.korbit.ai/machinelearning

Multi-Sample Dropout for Accelerated Training and Better Generalization
Hiroshi Inoue: https://arxiv.org/abs/1905.09788
#ArtificialIntelligence #NeuralComputing #MachineLearning

Deep Scale-spaces: Equivariance Over Scale
Daniel E. Worrall and Max Welling: https://arxiv.org/abs/1905.11697
#ArtificialIntelligence #DeepLearning #MachineLearning

SinGAN: Learning a Generative Model from a Single Natural Image
Shaham et al.: https://arxiv.org/abs/1905.01164v1
#ArtificialIntelligence #DeepLearning #GenerativeAdversarialNetworks

VAE-SBD
PyTorch implementation of the Variational Autoencoder with Spatial Broadcast Decoder.
GitHub by Daniel Daza: https://github.com/dfdazac/vaesbd
#deeplearning #pytorch #technology #innovation

Geoffrey Hinton explains neural networks at Google i/o 2019

https://m.youtube.com/watch?feature=youtu.be&v=lsDf_S1oYOg

Google AI #GeoffreyHinton

How Intelligence Is Transforming The Aviation Industry
https://medium.com/@morejitu/how-artificial-intelligence-is-transforming-the-aviation-industry-c49a1deeddba

#ArtificialIntelligence #AI #aviation #startup #technology

TorchVision 0.3 is out!: segmentation models, detection models, new datasets and more.

Tutorial on customizing/retraining Mask-RCNN for instance segmentation on Collab: https://colab.research.google.com/github/pytorch/vision/blob/temp-tutorial/tutorials/torchvision_finetuning_instance_segmentation.ipynb

https://pytorch.org/blog/torchvision03/

Human-level performance in first-person multiplayer games with population-based deep reinforcement learning
https://arxiv.org/abs/1807.01281
#artificialintelligence #deeplearning #reinforcementlearning

CoqGym
A Learning Environment for Theorem Proving with the Coq proof assistant
By Princeton Vision & Learning Lab: https://github.com/princeton-vl/CoqGym
#Logic #ComputerScience #ArtificialIntelligence #MachineLearning

This paper introduces a new method to play SuperMario Bros. using RL agent and without knowing the scores from the environment (pure exploration). They employ optical flow for evaluating the novelty of states to guide the RL agent.

https://www.profillic.com/paper/arxiv:1905.10071

In 2017, Google announced a Tensor Processing Unit (#TPU) — a custom application-specific integrated circuit (ASIC) built specifically for #machinelearning. A year later, TPUs were moved to the cloud and made open for commercial use.

Following the line of CPUs and GPUs, Tensor Processing Units (TPUs) are Google’s custom-developed application-specific integrated circuits (ASICs) that are supposed to accelerate machine learning workloads. They are designed specifically for Google’s #TensorFlow framework, a symbolic math library that is used for #neuralnetworks.

https://medium.com/sciforce/understanding-tensor-processing-units-10ff41f50e78

SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers
Fedorov et al.: https://arxiv.org/pdf/1905.12107.pdf
#ArtificialIntelligence #DeepLearning #MachineLearning

These Insect-Inspired Robots Don't Need GPS For Orientation
https://www.humanbrainproject.eu/en/follow-hbp/news/these-insect-inspired-robots-don-t-need-gps-for-orientation

Theoretical Physics for Deep Learning
https://sites.google.com/view/icml2019phys4dl https://t.iss.one/ArtificialIntelligenceArticles

Our CVPR'19 paper about neural free-viewpoint rendering of human avatars without reconstructing geometry is on arXiv! Generator predicts UV mapping (not RGB), and texture is learned per-avatar.

🌐 (link: https://saic-violet.github.io/texturedavatar) saic-violet.github.io/texturedavatar
▶️ (link: https://youtu.be/3rrnUX8wWZ8) youtu.be/3rrnUX8wWZ8
📝 (link: https://arxiv.org/abs/1905.08776) arxiv.org/abs/1905.08776

Speech2Face: Learning the Face Behind a Voice #CVPR2019



ArXiv
arxiv.org/abs/1905.09773
Project
speech2face.github.io

Image Alignment in Unseen Domains via Domain Deep Generalization. arxiv.org/abs/1905.12028

Incidence Networks for Geometric Deep Learning. arxiv.org/abs/1905.11460