"Adversarial Examples Are Not Bugs, They Are Features"
Ilyas et al.: https://arxiv.org/pdf/1905.02175.pdf
#artificialintelligence #deeplearning #machinelearning
Ilyas et al.: https://arxiv.org/pdf/1905.02175.pdf
#artificialintelligence #deeplearning #machinelearning
The Hitchhiker’s Guide to AI Ethics
Blog by B. Nalini: https://medium.com/@naliniwrites/ethics-of-ai-a-comprehensive-primer-1bfd039124b0
#artificialintelligence #aiethics #ai4good #deeplearning #technology
Blog by B. Nalini: https://medium.com/@naliniwrites/ethics-of-ai-a-comprehensive-primer-1bfd039124b0
#artificialintelligence #aiethics #ai4good #deeplearning #technology
Medium
The Hitchhiker’s Guide to AI Ethics
A 3-part series exploring ethics issues in Artificial Intelligence
The Effect of Network Width on Stochastic Gradient Descent and Generalization: an Empirical Study
Park et al.: https://arxiv.org/abs/1905.03776
#MachineLearning #ArtificialIntelligence #NeuralNetworks
Park et al.: https://arxiv.org/abs/1905.03776
#MachineLearning #ArtificialIntelligence #NeuralNetworks
2.8M images with precise segmentation masks from Google https://ai.googleblog.com/2019/05/announcing-open-images-v5-and-iccv-2019.html
research.google
Announcing Open Images V5 and the ICCV 2019 Open Images Challenge
Posted by Vittorio Ferrari, Research Scientist, Machine Perception In 2016, we introduced Open Images, a collaborative release of ~9 million imag...
Visualizing the Consequences of Climate Change Using Cycle-Consistent Adversarial Networks
Schmidt et al.: https://arxiv.org/abs/1905.03709
#ComputerVision #PatternRecognition #ArtificialIntelligence
Schmidt et al.: https://arxiv.org/abs/1905.03709
#ComputerVision #PatternRecognition #ArtificialIntelligence
arXiv.org
Visualizing the Consequences of Climate Change Using...
We present a project that aims to generate images that depict accurate, vivid, and personalized outcomes of climate change using Cycle-Consistent Adversarial Networks (CycleGANs). By training our...
Controllable Neural Story Plot Generation via Reward Shaping
Tambwekar et al.: https://arxiv.org/pdf/1809.10736.pdf
#DeepLearning #MachineLearning #NeuralNetworks
Tambwekar et al.: https://arxiv.org/pdf/1809.10736.pdf
#DeepLearning #MachineLearning #NeuralNetworks
Big news today for the AI For Good Summer Lab as we announce a three year partnership with CIFAR
https://www.cifar.ca/cifarnews/2019/05/10/new-partnership-to-create-more-opportunities-for-women-in-artificial-intelligence-for-good
https://www.cifar.ca/cifarnews/2019/05/10/new-partnership-to-create-more-opportunities-for-women-in-artificial-intelligence-for-good
CIFAR
New partnership to create more opportunities for women in Artificial Intelligence for good
OSMO and CIFAR partner to grow the AI For Good Summer Lab program
#Tensorflow_Graphics #GoogleAI
Check out how Computer Graphics meets Deep Learning!
https://medium.com/tensorflow/introducing-tensorflow-graphics-computer-graphics-meets-deep-learning-c8e3877b7668?_branch_match_id=650042266723832991
Check out how Computer Graphics meets Deep Learning!
https://medium.com/tensorflow/introducing-tensorflow-graphics-computer-graphics-meets-deep-learning-c8e3877b7668?_branch_match_id=650042266723832991
Medium
Introducing TensorFlow Graphics: Computer Graphics Meets Deep Learning
Posted by Julien Valentin and Sofien Bouaziz
Video on self supervised learning of speech representations
By Mirco Ravanelli: https://youtu.be/1zjUmY8L5TU
#deeplearning #selfsupervisedlearning #unsupervisedlearning
By Mirco Ravanelli: https://youtu.be/1zjUmY8L5TU
#deeplearning #selfsupervisedlearning #unsupervisedlearning
YouTube
Toward Unsupervised Learning of Speech Representations
In this presentation, I first introduce unsupervised/self-supervised learning. Then, I describe some of my recent works that aim to learn general and robust self-supervised speech representations.
Minkowski Engine
The Minkowski Engine is an auto-diff library for generalized sparse convolutions and high-dimensional sparse tensors https://github.com/StanfordVL/MinkowskiEngine
#artificialintelligence #deeplearning #machinelearning #spacetime
The Minkowski Engine is an auto-diff library for generalized sparse convolutions and high-dimensional sparse tensors https://github.com/StanfordVL/MinkowskiEngine
#artificialintelligence #deeplearning #machinelearning #spacetime
GitHub
GitHub - NVIDIA/MinkowskiEngine: Minkowski Engine is an auto-diff neural network library for high-dimensional sparse tensors
Minkowski Engine is an auto-diff neural network library for high-dimensional sparse tensors - NVIDIA/MinkowskiEngine
"Importance Weighted Hierarchical Variational Inference"
By Artem Sobolev and Dmitry Vetrov: https://arxiv.org/abs/1905.03290
Talk: https://youtu.be/pdSu7XfGhHw
#Bayesian #MachineLearning #VariationalInference
By Artem Sobolev and Dmitry Vetrov: https://arxiv.org/abs/1905.03290
Talk: https://youtu.be/pdSu7XfGhHw
#Bayesian #MachineLearning #VariationalInference
MineRL Competition 2019
Competition Overview: https://minerl.io/competition/
#artificialintelligence #deeplearning #reinforcementlearning #research #technology
Competition Overview: https://minerl.io/competition/
#artificialintelligence #deeplearning #reinforcementlearning #research #technology
Visualizing the Consequences of Climate Change Using Cycle-Consistent Adversarial Networks
Schmidt et al.: https://arxiv.org/abs/1905.03709
#ComputerVision #PatternRecognition #ArtificialIntelligence
Schmidt et al.: https://arxiv.org/abs/1905.03709
#ComputerVision #PatternRecognition #ArtificialIntelligence
arXiv.org
Visualizing the Consequences of Climate Change Using...
We present a project that aims to generate images that depict accurate, vivid, and personalized outcomes of climate change using Cycle-Consistent Adversarial Networks (CycleGANs). By training our...
"Training Neural Nets on Larger Batches: Practical Tips for 1-GPU, Multi-GPU & Distributed setups"
By Thomas Wolf: https://medium.com/huggingface/training-larger-batches-practical-tips-on-1-gpu-multi-gpu-distributed-setups-ec88c3e51255
#ArtificialInteligence #DeepLearning #MachineLearning #NeuralNetworks #Research
By Thomas Wolf: https://medium.com/huggingface/training-larger-batches-practical-tips-on-1-gpu-multi-gpu-distributed-setups-ec88c3e51255
#ArtificialInteligence #DeepLearning #MachineLearning #NeuralNetworks #Research
Medium
💥 Training Neural Nets on Larger Batches: Practical Tips for 1-GPU, Multi-GPU & Distributed setups
Training neural networks with larger batches in PyTorch: gradient accumulation, gradient checkpointing, multi-GPUs and distributed setups…
An Empirical Study of Example Forgetting During Deep Neural Network Learning
Joint work with Alessandro Sordoni, Remi Tachet, Adam Trischler, Yoshua Bengio, and Geoff Gordon
Paper: https://bit.ly/2H8yQUg
Code: https://bit.ly/2vMH6mw
#ICLR #ICLR2019 #MachineLearning
Joint work with Alessandro Sordoni, Remi Tachet, Adam Trischler, Yoshua Bengio, and Geoff Gordon
Paper: https://bit.ly/2H8yQUg
Code: https://bit.ly/2vMH6mw
#ICLR #ICLR2019 #MachineLearning
GitHub
mtoneva/example_forgetting
Contribute to mtoneva/example_forgetting development by creating an account on GitHub.
Learning higher-order sequential structure with cloned HMMs
Dedieu et al.: https://arxiv.org/abs/1905.00507
#ArtificialIntelligence #MachineLearning #Technology
Dedieu et al.: https://arxiv.org/abs/1905.00507
#ArtificialIntelligence #MachineLearning #Technology
arXiv.org
Learning higher-order sequential structure with cloned HMMs
Variable order sequence modeling is an important problem in artificial and natural intelligence. While overcomplete Hidden Markov Models (HMMs), in theory, have the capacity to represent long-term...