Make Trump Sing Again
Generated by a Trump TTS model trained based off the paper "Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis", where given a reference audio the model will try to replicate that style.
ArXiV: https://arxiv.org/pdf/1803.09017.pdf
Youtube: https://youtu.be/3rgAVT8b4fw
#tts #song #speech #DL
Generated by a Trump TTS model trained based off the paper "Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis", where given a reference audio the model will try to replicate that style.
ArXiV: https://arxiv.org/pdf/1803.09017.pdf
Youtube: https://youtu.be/3rgAVT8b4fw
#tts #song #speech #DL
DGC-Net: Dense Geometric Correspondence network
Paper addresses the challenge of dense pixel correspondence estimation between two images. Practically, this means that it is about comparing different views of one object, which is very important to make #CV more robust.
ArXiV: https://arxiv.org/abs/1810.08393
Github: https://github.com/AaltoVision/DGC-Net
YouTube: https://www.youtube.com/watch?v=xnQMEr4FbHE
Project page: https://aaltovision.github.io/dgc-net-site/
#CNN #DL
Paper addresses the challenge of dense pixel correspondence estimation between two images. Practically, this means that it is about comparing different views of one object, which is very important to make #CV more robust.
ArXiV: https://arxiv.org/abs/1810.08393
Github: https://github.com/AaltoVision/DGC-Net
YouTube: https://www.youtube.com/watch?v=xnQMEr4FbHE
Project page: https://aaltovision.github.io/dgc-net-site/
#CNN #DL
arXiv.org
DGC-Net: Dense Geometric Correspondence Network
This paper addresses the challenge of dense pixel correspondence estimation
between two images. This problem is closely related to optical flow estimation
task where ConvNets (CNNs) have recently...
between two images. This problem is closely related to optical flow estimation
task where ConvNets (CNNs) have recently...
Forwarded from EarthML
Automatic feature selection:
EGU is still ongoing, but I am burning to share with you some of my findings already!
Research group in University of Lausanne developed a pretty promising algorithm for automatic feature selection based on General Regression Neural Network (GRNN, also known as Nadaraya-Watson Estimator). The idea behind is pretty simple and therefor powerful - why won't we build the simplest network that can train really fast and brute force all possible combination of features to check how they affect accuracy, learning rate etc and than select the best performing once.
Here is Python implementation on GitHub: https://github.com/federhub/pyGRNN
Also, check their poster: https://github.com/federhub/pyGRNN/blob/master/EGU2019_FS_using_simple_and_efficient_ML_models.pdf
Stay tuned, subscribe and share!
xoxo
EGU is still ongoing, but I am burning to share with you some of my findings already!
Research group in University of Lausanne developed a pretty promising algorithm for automatic feature selection based on General Regression Neural Network (GRNN, also known as Nadaraya-Watson Estimator). The idea behind is pretty simple and therefor powerful - why won't we build the simplest network that can train really fast and brute force all possible combination of features to check how they affect accuracy, learning rate etc and than select the best performing once.
Here is Python implementation on GitHub: https://github.com/federhub/pyGRNN
Also, check their poster: https://github.com/federhub/pyGRNN/blob/master/EGU2019_FS_using_simple_and_efficient_ML_models.pdf
Stay tuned, subscribe and share!
xoxo
GitHub
GitHub - federhub/pyGRNN: Python implementation of General Regression Neural Network (Nadaraya-Watson Estimator). A Feature Selectionβ¦
Python implementation of General Regression Neural Network (Nadaraya-Watson Estimator). A Feature Selection module based on GRNN is also provided - federhub/pyGRNN
ββAutodesk claims to use GANs to design a chair.
First-of-its-kind chair from Philippe Starck and Kartell. Imagined by a human and cocreated with intelligent generative design
Link: https://adsknews.autodesk.com/news/starck-intelligent-generative-design
Generative design explanation link: https://www.autodesk.com/solutions/generative-design
#Autodesk #generativedesign
First-of-its-kind chair from Philippe Starck and Kartell. Imagined by a human and cocreated with intelligent generative design
Link: https://adsknews.autodesk.com/news/starck-intelligent-generative-design
Generative design explanation link: https://www.autodesk.com/solutions/generative-design
#Autodesk #generativedesign
ββGoogleβs progress on AutoML
Hint: itβs beating some old competition solutions.
Link: https://cloud.google.com/blog/products/ai-machine-learning/expanding-google-cloud-ai-to-make-it-easier-for-developers-to-build-and-deploy-ai
#AutoML #Kaggle
Hint: itβs beating some old competition solutions.
Link: https://cloud.google.com/blog/products/ai-machine-learning/expanding-google-cloud-ai-to-make-it-easier-for-developers-to-build-and-deploy-ai
#AutoML #Kaggle
ββHow is Uber predicting demand, surge and where will be high demand area.
One more post from brilliant #Uber engineering team, sharing their approach and general experience about forecasting.
Link: https://eng.uber.com/forecasting-introduction/
#ts #timeseries #arima #demandprediction #ml
One more post from brilliant #Uber engineering team, sharing their approach and general experience about forecasting.
Link: https://eng.uber.com/forecasting-introduction/
#ts #timeseries #arima #demandprediction #ml
General talk on who makes the choice now: machine or human
Discussion based on the book Β«A Humanβs Guide to Machine Intelligence: How Algorithms Are Shaping Our Lives and How We Can Stay in ControlΒ».
Link: https://knowledge.wharton.upenn.edu/article/algorithms-decision-making/
#podcast #general #meta #publicml
Discussion based on the book Β«A Humanβs Guide to Machine Intelligence: How Algorithms Are Shaping Our Lives and How We Can Stay in ControlΒ».
Link: https://knowledge.wharton.upenn.edu/article/algorithms-decision-making/
#podcast #general #meta #publicml
Knowledge at Wharton
Who Made That Decision: You or an Algorithm?
Algorithms now make lots of decisions, but they have their own biases, writes Whartonβs Kartik Hosanagar in his new book. β¦Read More
Text Mining 101: Topic Modeling
General intro into #LDA and #textrank
Link: https://www.kdnuggets.com/2016/07/text-mining-101-topic-modeling.html
#nlp
General intro into #LDA and #textrank
Link: https://www.kdnuggets.com/2016/07/text-mining-101-topic-modeling.html
#nlp
ββNew paper on #osteoarthritis progression prediction
Link: https://arxiv.org/abs/1904.06236
GitHub (model & code): https://github.com/MIPT-Oulu/OAProgression
#biolearning #healthcare #ML
Link: https://arxiv.org/abs/1904.06236
GitHub (model & code): https://github.com/MIPT-Oulu/OAProgression
#biolearning #healthcare #ML
π₯Singing voice conversion system developed at FAIR-Tel Aviv.
This can transform someone's singing voice into someone else's voice.
YouTube: https://www.youtube.com/watch?v=IEpkGenLnjw
Link: https://venturebeat.com/2019/04/16/facebooks-ai-can-convert-one-singers-voice-into-another/
ArXiV: https://arxiv.org/abs/1904.06590
#voiceconversion #audiolearning #DL #Facebook
This can transform someone's singing voice into someone else's voice.
YouTube: https://www.youtube.com/watch?v=IEpkGenLnjw
Link: https://venturebeat.com/2019/04/16/facebooks-ai-can-convert-one-singers-voice-into-another/
ArXiV: https://arxiv.org/abs/1904.06590
#voiceconversion #audiolearning #DL #Facebook
ββTransGaGa: Geometry-Aware Unsupervised Image-to-Image Translation
Paper: https://arxiv.org/pdf/1904.09571v1.pdf
#GAN #cv #dl
Paper: https://arxiv.org/pdf/1904.09571v1.pdf
#GAN #cv #dl
A Recipe for Training Neural Networks by Andrej Karpathy
New article written by Andrej Karpathy distilling a bunch of useful heuristics for training neural nets. The post is full of real-world knowledge and how-to details that are not taught in books and often take endless hours to learn the hard way.
Link: https://karpathy.github.io/2019/04/25/recipe/
#tipsandtricks #karpathy #tutorial #nn #ml #dl
New article written by Andrej Karpathy distilling a bunch of useful heuristics for training neural nets. The post is full of real-world knowledge and how-to details that are not taught in books and often take endless hours to learn the hard way.
Link: https://karpathy.github.io/2019/04/25/recipe/
#tipsandtricks #karpathy #tutorial #nn #ml #dl
karpathy.github.io
A Recipe for Training Neural Networks
Musings of a Computer Scientist.
OpenAIβs MuseNet architecture to generate music.
#MuseNet β neural network which discovered how to generate music from first 5 or so notes, using many different instruments and styles.
Post: https://openai.com/blog/musenet/
MuseNet will play an experimental concert today from 12β3pm PT on livestream: http://twitch.tv/openai
#audiolearning #musicgeneration #OpenAI #soundgeneration
#MuseNet β neural network which discovered how to generate music from first 5 or so notes, using many different instruments and styles.
Post: https://openai.com/blog/musenet/
MuseNet will play an experimental concert today from 12β3pm PT on livestream: http://twitch.tv/openai
#audiolearning #musicgeneration #OpenAI #soundgeneration
Openai
MuseNet
Weβve created MuseNet, a deep neural network that can generate 4-minute musical compositions with 10 different instruments, and can combine styles from country to Mozart to the Beatles. MuseNet was not explicitly programmed with our understanding of musicβ¦
All videos & slides from Scaled Machine Learning Conference 2019
YouTube playlist: https://www.youtube.com/playlist?list=PLRM2gQVaW_wWXoUnSfZTxpgDmNaAS1RtG
#Facebook #dl #ScaledML2019
YouTube playlist: https://www.youtube.com/playlist?list=PLRM2gQVaW_wWXoUnSfZTxpgDmNaAS1RtG
#Facebook #dl #ScaledML2019
YouTube
ScaledML 2019 - YouTube
New interactive annotation approach
Claimed to outperform Polygon-RNN++ and being 10x faster.
ArXiV: https://arxiv.org/pdf/1903.06874.pdf
YouTube: https://www.youtube.com/watch?v=ycD2BtO-QzU
Code: https://github.com/fidler-lab/curve-gcn
#PyTorch #annotation #release
Claimed to outperform Polygon-RNN++ and being 10x faster.
ArXiV: https://arxiv.org/pdf/1903.06874.pdf
YouTube: https://www.youtube.com/watch?v=ycD2BtO-QzU
Code: https://github.com/fidler-lab/curve-gcn
#PyTorch #annotation #release
YouTube
Fast Interactive Object Annotation with Curve-GCN
Paper is accepted by Conference on Computer Vision and Pattern Recognition (CVPR), 2019
Paper link: https://arxiv.org/abs/1903.06874
Code is available at: https://github.com/fidler-lab/curve-gcn
Paper link: https://arxiv.org/abs/1903.06874
Code is available at: https://github.com/fidler-lab/curve-gcn
ββODE DL paper with overview
This paper recieved award at #NeurIPS2018. Main idea: defining a deep residual network as a continuously evolving system & instead of updating the hidden units layer by layer, define their derivative with respect to depth instead.
ArXiV: https://arxiv.org/pdf/1806.07366.pdf
GitHub: https://github.com/rtqichen/torchdiffeq
Overview: https://rkevingibson.github.io/blog/neural-networks-as-ordinary-differential-equations/
#ODE #DL #NeurIPS
This paper recieved award at #NeurIPS2018. Main idea: defining a deep residual network as a continuously evolving system & instead of updating the hidden units layer by layer, define their derivative with respect to depth instead.
ArXiV: https://arxiv.org/pdf/1806.07366.pdf
GitHub: https://github.com/rtqichen/torchdiffeq
Overview: https://rkevingibson.github.io/blog/neural-networks-as-ordinary-differential-equations/
#ODE #DL #NeurIPS
Great visualization of DBSCAN
DBSCAN β is fast and rather reliable #clustering algorithm. It can outperform classical K-means in some cases and icredibly useful in some cases. This interactive demo helps to understand how algorithm really works.
Link: https://www.naftaliharris.com/blog/visualizing-dbscan-clustering/
#ML #dbscan
DBSCAN β is fast and rather reliable #clustering algorithm. It can outperform classical K-means in some cases and icredibly useful in some cases. This interactive demo helps to understand how algorithm really works.
Link: https://www.naftaliharris.com/blog/visualizing-dbscan-clustering/
#ML #dbscan
GAN for retail marketing images generation
Startup that uses GANs to generate retail marketing images, has raised $17M. What a smart use for GANs.
https://venturebeat.com/2019/04/24/vue-ai-raises-17-million-for-ai-driven-retail-products/
#GAN #startup #fundraising
Startup that uses GANs to generate retail marketing images, has raised $17M. What a smart use for GANs.
https://venturebeat.com/2019/04/24/vue-ai-raises-17-million-for-ai-driven-retail-products/
#GAN #startup #fundraising
VentureBeat
Vue.ai raises $17 million for AI-driven retail products
Vue.ai, a subbrand of India-U.S. startup Mad Street Den, has raised $17 million in venture capital, which it says will be used to grow its team.
Machine Learning Workflows
Pretty articel about how machine learning / AI works in the organization.
Link: https://skymind.ai/wiki/machine-learning-workflow
#management #production #workflow
Pretty articel about how machine learning / AI works in the organization.
Link: https://skymind.ai/wiki/machine-learning-workflow
#management #production #workflow
Skymind
Machine Learning Workflows
Workflows for moving Machine Learning to a business production environment.
Modeling User Exposure in Recommendation
On using latent variables for exposure of an user to an item to build a better recommendation systems.
Link: https://arxiv.org/abs/1510.07025
#recommender #RS
On using latent variables for exposure of an user to an item to build a better recommendation systems.
Link: https://arxiv.org/abs/1510.07025
#recommender #RS
arXiv.org
Modeling User Exposure in Recommendation
Collaborative filtering analyzes user preferences for items (e.g., books,
movies, restaurants, academic papers) by exploiting the similarity patterns
across users. In implicit feedback settings,...
movies, restaurants, academic papers) by exploiting the similarity patterns
across users. In implicit feedback settings,...