ICML 2019 videos have been posted https://www.youtube.com/channel/UCvqEpkx-HQ2nDMT-ob-AADg/videos https://t.iss.one/ArtificialIntelligenceArticles
Tutorial: "Meta-Learning: from Few-Shot Learning to Rapid Reinforcement Learning" https://sites.google.com/view/icml19metalearning #ICML2019 https://t.iss.one/ArtificialIntelligenceArticles
ArtificialIntelligenceArticles
Tutorial: "Meta-Learning: from Few-Shot Learning to Rapid Reinforcement Learning" https://sites.google.com/view/icml19metalearning #ICML2019 https://t.iss.one/ArtificialIntelligenceArticles
https://www.youtube.com/watch?v=DijI4XrhqNo&list=UUvqEpkx-HQ2nDMT-ob-AADg&index=5
https://t.iss.one/ArtificialIntelligenceArticles
https://t.iss.one/ArtificialIntelligenceArticles
YouTube
ICML 2019 Tutorial: Meta-Learning: from Few-Shot Learning to Rapid Reinforcement Learning (Part 1)
Welcome to the ICML 2019 Tutorial session: Active Hypothesis Testing: An Information Theoretic (re)View
Presented by Tara Javidi
https://www.youtube.com/watch?v=tuu56-8BhwM
Presented by Tara Javidi
https://www.youtube.com/watch?v=tuu56-8BhwM
YouTube
ICML 2019 Active Hypothesis Testing An Information Theoretic reView
Welcome to ICML 2019 Tutorial session: Never-Ending Learning
Presented by Tom Mitchell and Partha Talukdar
https://www.youtube.com/watch?v=0TADiY7iPAc
https://t.iss.one/ArtificialIntelligenceArticles
Presented by Tom Mitchell and Partha Talukdar
https://www.youtube.com/watch?v=0TADiY7iPAc
https://t.iss.one/ArtificialIntelligenceArticles
YouTube
ICML 2019 Tutorial Session on Active Learning from Theory to Practice
Welcome to the ICML 2019 Tutorial session: A Tutorial on Attention in Deep Learning
Presented by Alex Smola and Aston Zhang
https://www.youtube.com/watch?v=nS1Lse2B48w
Presented by Alex Smola and Aston Zhang
https://www.youtube.com/watch?v=nS1Lse2B48w
YouTube
ICML 2019 A Tutorial on Attention in Deep Learning
New DeepMind Unsupervised Image Model Challenges AlexNet
https://medium.com/syncedreview/new-deepmind-unsupervised-image-model-challenges-alexnet-d658ef92ab1e
https://medium.com/syncedreview/new-deepmind-unsupervised-image-model-challenges-alexnet-d658ef92ab1e
Medium
New DeepMind Unsupervised Image Model Challenges AlexNet
While supervised learning has tremendously improved AI performance in image classification, a major drawback is its reliance on…
ICML 2019 Best Paper Award
https://proceedings.mlr.press/v97/locatello19a.html https://t.iss.one/ArtificialIntelligenceArticles
https://proceedings.mlr.press/v97/locatello19a.html https://t.iss.one/ArtificialIntelligenceArticles
Rates of Convergence for Sparse Variational Gaussian Process Regression
arxiv.org/abs/1903.03571 https://t.iss.one/ArtificialIntelligenceArticles
arxiv.org/abs/1903.03571 https://t.iss.one/ArtificialIntelligenceArticles
CVPR’19 paper on speech-to-gesture prediction. Given raw speech audio, predict arm/hand motion to go along with it. Check out video, or download 128 hours of video for 10 speakers
Learning Individual Styles of Conversational Gesture
https://people.eecs.berkeley.edu/~shiry/speech2gesture/
Learning Individual Styles of Conversational Gesture
https://people.eecs.berkeley.edu/~shiry/speech2gesture/
Congratulations to the Best Papers at the ongoing #ICML2019
The Thirty-sixth International Conference on Machine Learning, Long Beach, USA
(1)Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations https://arxiv.org/pdf/1811.12359.pdf
Congratulations to the GoogleAI team of Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly,Bernhard Schölkopf, Olivier Bachem
(2)Rates of Convergence for Sparse Variational Gaussian Process Regression
https://arxiv.org/pdf/1903.03571.pdf
Kudos to David R. Burt, Carl E. Rasmussen, Mark van der Wilk of University of Cambridge
The Thirty-sixth International Conference on Machine Learning, Long Beach, USA
(1)Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations https://arxiv.org/pdf/1811.12359.pdf
Congratulations to the GoogleAI team of Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly,Bernhard Schölkopf, Olivier Bachem
(2)Rates of Convergence for Sparse Variational Gaussian Process Regression
https://arxiv.org/pdf/1903.03571.pdf
Kudos to David R. Burt, Carl E. Rasmussen, Mark van der Wilk of University of Cambridge
ICML is one of the premier machine learning conferences. VideoKen is proud to power deep indexing of the entire ICML 2019 video content. Catch up with all of the exciting tutorials here:
https://search.videoken.com/?orgId=133
#MachineLearning #AI #DeepLearning #AIplayer #ICML2019
https://search.videoken.com/?orgId=133
#MachineLearning #AI #DeepLearning #AIplayer #ICML2019
Language, trees, and geometry in neural networks
code https://pair-code.github.io/interpretability/bert-tree/
paper https://arxiv.org/pdf/1906.02715.pdf
code https://pair-code.github.io/interpretability/bert-tree/
paper https://arxiv.org/pdf/1906.02715.pdf
pair-code.github.io
Language, trees, and geometry in neural networks
ICYMI: A project that aims to generate images that depict accurate, vivid, and personalized outcomes of climate change using Cycle-Consistent Adversarial Networks (CycleGANs).
https://www.profillic.com/paper/arxiv:1905.03709
https://www.profillic.com/paper/arxiv:1905.03709
Profillic
Profillic: AI research & source code to supercharge your projects
Explore state-of-the-art in machine learning, AI, and robotics research. Browse papers, source code, models, and more by topics and authors. Connect with researchers and engineers working on related problems in machine learning, deep learning, natural language…
Differentiable Beam search Decoder (DBD): training a speech recognition system by backpropagating through the decoder leads to smaller models and better word error rates.
Paper from FAIRies Ronan Collobert, Awni Hannun and Gabriel Synnaeve here: https://arxiv.org/abs/1902.06022
Awesome stuff. We were doing a simple version of thise to train our check recognition system in the mid-1990s.
https://ai.facebook.com/blog/combining-acoustic-and-language-model-training-for-speech-recognition/
Paper from FAIRies Ronan Collobert, Awni Hannun and Gabriel Synnaeve here: https://arxiv.org/abs/1902.06022
Awesome stuff. We were doing a simple version of thise to train our check recognition system in the mid-1990s.
https://ai.facebook.com/blog/combining-acoustic-and-language-model-training-for-speech-recognition/
Deep Learning Tools
PDF Version: https://drive.google.com/file/d/1XhngKISDpQgwGlvU-hjXWZb_qfyIYjqN/view
Source: Kdnuggets
PDF Version: https://drive.google.com/file/d/1XhngKISDpQgwGlvU-hjXWZb_qfyIYjqN/view
Source: Kdnuggets
ICML 2019 Tutorial Videos
A Neural Approaches to Conversational UI
https://www.youtube.com/watch?v=nr3P1VrFleo
Algorithm configuration: learning in the space of algorithm designs
https://www.youtube.com/watch?v=bIsRNgg20xo
A Neural Approaches to Conversational UI
https://www.youtube.com/watch?v=nr3P1VrFleo
Algorithm configuration: learning in the space of algorithm designs
https://www.youtube.com/watch?v=bIsRNgg20xo
YouTube
A Neural Approaches to Conversational UI
After everything that has happened this year, I'm glad Schmidhuber still has the fire in him. 🔥 https://arxiv.org/abs/1906.04493
Shapes and Context:
In-the-wild Image Synthesis & Manipulation
https://www.cs.cmu.edu/~aayushb/OpenShapes/
In-the-wild Image Synthesis & Manipulation
https://www.cs.cmu.edu/~aayushb/OpenShapes/