Feature selection of neural networks is skewed towards the less abstract cue. arxiv.org/abs/1908.03000
Ten Trending Academic Papers on the Future of Computer Vision
https://hackernoon.com/top-10-papers-you-shouldnt-miss-from-cvpr-2019-deepfake-facial-recognition-reconstruction-and-more-d5ly3q1w
https://hackernoon.com/top-10-papers-you-shouldnt-miss-from-cvpr-2019-deepfake-facial-recognition-reconstruction-and-more-d5ly3q1w
Hackernoon
Ten Trending Academic Papers on the Future of Computer Vision | Hacker Noon
If you couldn’t make it to CVPR 2019, no worries. Below is a list of top 10 papers everyone was talking about, covering DeepFakes, Facial Recognition, Reconstruction, & more.
A Gentle Introduction to the Progressive Growing GAN
https://machinelearningmastery.com/introduction-to-progressive-growing-generative-adversarial-networks/
https://machinelearningmastery.com/introduction-to-progressive-growing-generative-adversarial-networks/
MachineLearningMastery.com
A Gentle Introduction to the Progressive Growing GAN - MachineLearningMastery.com
Progressive Growing GAN is an extension to the GAN training process that allows for the stable training of generator models that can output large high-quality images.
It involves starting with a very small image and incrementally adding blocks of layers…
It involves starting with a very small image and incrementally adding blocks of layers…
Music Transformer ( Huang et al, Google Brain, ICLR2019 ) re-implementation repository
github : https://github.com/jason9693/MusicTransformer-tensorflow2.0
library : Tensorflow2.0 (beta)
training env : v100 x 1GPU
paper : https://arxiv.org/abs/1809.04281
Google Magenta blog : https://magenta.tensorflow.org/music-transformer
generated sample demo from this repo : https://www.youtube.com/playlist?list=PLVopZAnUrGWrbIkLGB3bz5nitWThIueS2
For more details, you can see in README.md
Thank you.
github : https://github.com/jason9693/MusicTransformer-tensorflow2.0
library : Tensorflow2.0 (beta)
training env : v100 x 1GPU
paper : https://arxiv.org/abs/1809.04281
Google Magenta blog : https://magenta.tensorflow.org/music-transformer
generated sample demo from this repo : https://www.youtube.com/playlist?list=PLVopZAnUrGWrbIkLGB3bz5nitWThIueS2
For more details, you can see in README.md
Thank you.
GitHub
GitHub - jason9693/MusicTransformer-tensorflow2.0: implementation of music transformer with tensorflow-2.0 (ICLR2019)
implementation of music transformer with tensorflow-2.0 (ICLR2019) - jason9693/MusicTransformer-tensorflow2.0
ExtremeC3Net: Extreme Lightweight Portrait Segmentation Networks using Advanced C3-modules. arxiv.org/abs/1908.03093
Sim-to-Real Learning for Casualty Detection from Ground Projected Point Cloud Data. arxiv.org/abs/1908.03057
What goes around comes around: Cycle-Consistency-based Short-Term Motion Prediction for A... arxiv.org/abs/1908.03055
CGI faces will soon be indistinguishable from real ones. Here’s how
https://www.digitaltrends.com/cool-tech/cubic-motion-scanning-technology/
https://www.digitaltrends.com/cool-tech/cubic-motion-scanning-technology/
Centre for Computational Statistics and Machine Learning from UCL's Machine Learning Summer School (MLSS'19) video lectures are available here,
https://search.videoken.com/?orgId=198
The topics range from optimization and Bayesian inference to deep learning, reinforcement learning, and Gaussian processes. The lectures are of tutorial style, starts from basics, but then quickly picking up the pace so that after 2-4 hours of teaching, they arrive at the state of the art in the subject area.
#summerschool #machinelearning #tutorials #deeplearning #speechprocessing #reinforcementlearning
https://search.videoken.com/?orgId=198
The topics range from optimization and Bayesian inference to deep learning, reinforcement learning, and Gaussian processes. The lectures are of tutorial style, starts from basics, but then quickly picking up the pace so that after 2-4 hours of teaching, they arrive at the state of the art in the subject area.
#summerschool #machinelearning #tutorials #deeplearning #speechprocessing #reinforcementlearning
Probabilistic Models with Deep Neural Networks
Masegosa et al.: https://arxiv.org/abs/1908.03442
#MachineLearning #NeuralNetworks #StatisticsTheory
Masegosa et al.: https://arxiv.org/abs/1908.03442
#MachineLearning #NeuralNetworks #StatisticsTheory
arXiv.org
Probabilistic Models with Deep Neural Networks
Recent advances in statistical inference have significantly expanded the toolbox of probabilistic modeling. Historically, probabilistic modeling has been constrained to (i) very restricted model...
Must follow Github Repository
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All in one Github Repository
https://github.com/Niraj-Lunavat/Artificial-Intelligence
#Github #artificialIntelligence #ai #ml #machinelearning
⚡️Contains +100 AI Cheatsheets
⚡️List of Free AI Courses
⚡️Free Online Books
⚡️Top 10 Online Books
⚡️Research Papers with codes
⚡️Top Videos&Lecture on AI+ML
⚡️+99 AI Researchers
⚡️Top website which should follow
⚡️+121 Free Datasets
⚡️+53 AI Framework and many more
All in one Github Repository
https://github.com/Niraj-Lunavat/Artificial-Intelligence
#Github #artificialIntelligence #ai #ml #machinelearning
GitHub
GitHub - Niraj-Lunavat/Artificial-Intelligence: Awesome AI Learning with +100 AI Cheat-Sheets, Free online Books, Top Courses,…
Awesome AI Learning with +100 AI Cheat-Sheets, Free online Books, Top Courses, Best Videos and Lectures, Papers, Tutorials, +99 Researchers, Premium Websites, +121 Datasets, Conferences, Frameworks...
Learning Vision-based Flight in Drone Swarms by Imitation
Schilling et al.: https://arxiv.org/abs/1908.02999
#Robotics #MachineLearning #MultiagentSystems
Schilling et al.: https://arxiv.org/abs/1908.02999
#Robotics #MachineLearning #MultiagentSystems
arXiv.org
Learning Vision-based Flight in Drone Swarms by Imitation
Decentralized drone swarms deployed today either rely on sharing of positions
among agents or detecting swarm members with the help of visual markers. This
work proposes an entirely visual...
among agents or detecting swarm members with the help of visual markers. This
work proposes an entirely visual...
NeuPDE: Neural Network Based Ordinary and Partial Differential Equations for Modeling Time-Dependent Data
Sun et al.: https://arxiv.org/abs/1908.03190
#ArtificialIntelligence #NeuralNetwork #PartialDifferentialEquations
Sun et al.: https://arxiv.org/abs/1908.03190
#ArtificialIntelligence #NeuralNetwork #PartialDifferentialEquations
arXiv.org
NeuPDE: Neural Network Based Ordinary and Partial Differential...
We propose a neural network based approach for extracting models from dynamic
data using ordinary and partial differential equations. In particular, given a
time-series or spatio-temporal dataset,...
data using ordinary and partial differential equations. In particular, given a
time-series or spatio-temporal dataset,...