#RP Mariya Toneva, Excited to present our work on understanding catastrophic example forgetting at ICLR on Wednesday from 11-1pm! Poster #44. 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
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.
https://www.humanbrainproject.eu/en/silicon-brains/how-we-work/hardware/
EU Human Brain Project (HBP) aimed at understanding cognition and human brain, budget €1 billion for 2013-2023, 100 universities, 500 scientists.
EU Human Brain Project (HBP) aimed at understanding cognition and human brain, budget €1 billion for 2013-2023, 100 universities, 500 scientists.
Exponential Family Estimation via Adversarial Dynamics Embedding
Dai et al.: https://arxiv.org/abs/1904.12083
#ArtificialIntelligence #DeepLearning #MachineLearning
Dai et al.: https://arxiv.org/abs/1904.12083
#ArtificialIntelligence #DeepLearning #MachineLearning
Digging Into Self-Supervised Monocular Depth Estimation
Godard et al.: https://arxiv.org/abs/1806.01260
GitHub: https://github.com/nianticlabs/monodepth2
#ArtificialIntelligence #DeepLearning #MachineLearning
Godard et al.: https://arxiv.org/abs/1806.01260
GitHub: https://github.com/nianticlabs/monodepth2
#ArtificialIntelligence #DeepLearning #MachineLearning
arXiv.org
Digging Into Self-Supervised Monocular Depth Estimation
Per-pixel ground-truth depth data is challenging to acquire at scale. To overcome this limitation, self-supervised learning has emerged as a promising alternative for training models to perform...
Music Translation
"A Universal Music Translation Network", Mor et al.: https://arxiv.org/abs/1805.07848
PyTorch implementation: https://github.com/facebookresearch/music-translation
#artificialintelligence #machinelearning #pytorch #music
"A Universal Music Translation Network", Mor et al.: https://arxiv.org/abs/1805.07848
PyTorch implementation: https://github.com/facebookresearch/music-translation
#artificialintelligence #machinelearning #pytorch #music
arXiv.org
A Universal Music Translation Network
We present a method for translating music across musical instruments, genres, and styles. This method is based on a multi-domain wavenet autoencoder, with a shared encoder and a disentangled...
Open-sourcing Ax and BoTorch: New AI tools for adaptive experimentation
Written by Eytan Bakshy, Max Balandat, Kostya Kashin: https://ai.facebook.com/blog/open-sourcing-ax-and-botorch-new-ai-tools-for-adaptive-experimentation/
#ArtificialIntelligence #DeepLearning #MachineLearning #PyTorch
Written by Eytan Bakshy, Max Balandat, Kostya Kashin: https://ai.facebook.com/blog/open-sourcing-ax-and-botorch-new-ai-tools-for-adaptive-experimentation/
#ArtificialIntelligence #DeepLearning #MachineLearning #PyTorch
Facebook
Open-sourcing new AI tools for adaptive experimentation
We are open-sourcing BoTorch and Ax, two new tools that leverage adaptive experimentation to efficiently tackle optimization challenges in tuning software configurations, machine learning hyperparameters, ranking systems, and more.
Searching for MobileNetV3
Howard et al.: https://arxiv.org/abs/1905.02244
#ArtificialIntelligence #DeepLearning #MachineLearning
Howard et al.: https://arxiv.org/abs/1905.02244
#ArtificialIntelligence #DeepLearning #MachineLearning
arXiv.org
Searching for MobileNetV3
We present the next generation of MobileNets based on a combination of complementary search techniques as well as a novel architecture design. MobileNetV3 is tuned to mobile phone CPUs through a...
Deep Learning course: lecture slides and lab notebooks
Built and maintained by Olivier Grisel and Charles Ollion: https://m2dsupsdlclass.github.io/lectures-labs/
#ArtificialIntelligence #DeepLearning #GenerativeAdversarialNetworks #MachineLearning
Built and maintained by Olivier Grisel and Charles Ollion: https://m2dsupsdlclass.github.io/lectures-labs/
#ArtificialIntelligence #DeepLearning #GenerativeAdversarialNetworks #MachineLearning
In honor of the last day of ICLR 2019, here are the two papers that won Best Paper Award this year:
MILA’s “Ordered Neurons :
https://arxiv.org/abs/1810.09536
MIT CSAIL’s “The Lottery Ticket Hypothesis :
https://arxiv.org/abs/1803.03635
MILA’s “Ordered Neurons :
https://arxiv.org/abs/1810.09536
MIT CSAIL’s “The Lottery Ticket Hypothesis :
https://arxiv.org/abs/1803.03635
arXiv.org
Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks
Natural language is hierarchically structured: smaller units (e.g., phrases) are nested within larger units (e.g., clauses). When a larger constituent ends, all of the smaller constituents that...
ArtificialIntelligenceArticles
https://twitter.com/rezamahmooudi/status/1126553050498441217
Video, slides and paper of my keynote at ISSCC 2019, in which I speculate on the past, present and future of deep learning hardware.
Video: https://youtu.be/YzD7Z2yRL7Y
Slides: https://drive.google.com/open?id=1OeEULePrBCP17ofTaa2ThzwObHsY9KZg
Paper:https://yann.lecun.com/exdb/publis/pdf/lecun-isscc-19.pdf
Video: https://youtu.be/YzD7Z2yRL7Y
Slides: https://drive.google.com/open?id=1OeEULePrBCP17ofTaa2ThzwObHsY9KZg
Paper:https://yann.lecun.com/exdb/publis/pdf/lecun-isscc-19.pdf
YouTube
ISSCC 2019: Deep Learning Hardware: Past, Present, and Future - Yann LeCun
Yann LeCun, Facebook AI Research & New York University, New York, NY
Deep learning has caused revolutions in computer understanding of images, audio, and text,
enabling new applications such as information search and filtering, autonomous driving,
radiology…
Deep learning has caused revolutions in computer understanding of images, audio, and text,
enabling new applications such as information search and filtering, autonomous driving,
radiology…
"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