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/
Adaptive Neural Trees (ANTs) - icml2019 paper
combined benefits of decision trees and deep neural networks.
code https://github.com/rtanno21609/AdaptiveNeuralTrees
paper https://proceedings.mlr.press/v97/tanno19a.html
combined benefits of decision trees and deep neural networks.
code https://github.com/rtanno21609/AdaptiveNeuralTrees
paper https://proceedings.mlr.press/v97/tanno19a.html
GitHub
GitHub - rtanno21609/AdaptiveNeuralTrees: Adaptive Neural Trees
Adaptive Neural Trees . Contribute to rtanno21609/AdaptiveNeuralTrees development by creating an account on GitHub.
CVPR 2019
Papers: https://openaccess.thecvf.com/CVPR2019.py
Workshops: https://openaccess.thecvf.com/CVPR2019_workshops/menu.py
Papers: https://openaccess.thecvf.com/CVPR2019.py
Workshops: https://openaccess.thecvf.com/CVPR2019_workshops/menu.py
Good summary article about GANs
https://machinelearningmastery.com/resources-for-getting-started-with-generative-adversarial-networks/
https://machinelearningmastery.com/resources-for-getting-started-with-generative-adversarial-networks/
MachineLearningMastery.com
Best Resources for Getting Started With GANs - MachineLearningMastery.com
Generative Adversarial Networks, or GANs, are a type of deep learning technique for generative modeling. GANs are the techniques behind the startlingly photorealistic generation of human faces, as well as impressive image translation tasks such as photo colorization…
Nice paper on disentanglement representation(where each dimension of embedding represent one feature)
https://proceedings.mlr.press/v97/mathieu19a.html
https://proceedings.mlr.press/v97/mathieu19a.html
PMLR
Disentangling Disentanglement in Variational Autoencoders
We develop a generalisation of disentanglement in variational autoencoders (VAEs)—decomposition of the latent representation—characterising it as the fulfilm...
Python framework that facilitates the quick development of complex video analysis applications and other series-processing based applications in a multiprocessing environment.
https://github.com/videoflow/videoflow
https://github.com/videoflow/videoflow
GitHub
GitHub - videoflow/videoflow: Python framework that facilitates the quick development of complex video analysis applications and…
Python framework that facilitates the quick development of complex video analysis applications and other series-processing based applications in a multiprocessing environment. - videoflow/videoflow
Generative Adversarial Networks: A Survey and Taxonomy
By @wangvilla @sheqi1991 @tomasward
7 architecture-variant GANs and 9 loss-variant GANs focusing on
(1) High quality image generation
(2) Diverse image generation
(3) Stable training
https://arxiv.org/abs/1906.01529v1
By @wangvilla @sheqi1991 @tomasward
7 architecture-variant GANs and 9 loss-variant GANs focusing on
(1) High quality image generation
(2) Diverse image generation
(3) Stable training
https://arxiv.org/abs/1906.01529v1
arXiv.org
Generative Adversarial Networks: A Survey and Taxonomy
Generative adversarial networks (GANs) have been extensively studied in the
past few years. Arguably the revolutionary techniques are in the area of
computer vision such as plausible image...
past few years. Arguably the revolutionary techniques are in the area of
computer vision such as plausible image...