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Post #5645
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Channel Public Channel
AR ArtificialIntelligenceArticles
@artificialintelligencearticles
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Post #5644
1.24K
State of AI Report 2020
The State of AI Report analyses the most interesting developments in AI. We aim to trigger an informed conversation about the state of AI and its implication for the future.
The Report is produced by AI investors Nathan Benaich and Ian Hogarth.
https://www.stateof.ai/
The State of AI Report analyses the most interesting developments in AI. We aim to trigger an informed conversation about the state of AI and its implication for the future.
The Report is produced by AI investors Nathan Benaich and Ian Hogarth.
https://www.stateof.ai/
Post #5643
1.83K
Deep Reinforcement Learning
CS 285 at UC Berkeley, Fall 2020
Levine et al.: https://www.youtube.com/playlist?list=PL_iWQOsE6TfURIIhCrlt-wj9ByIVpbfGc
#ArtificialIntelligence #DeepLearning #ReinforcementLearning
CS 285 at UC Berkeley, Fall 2020
Levine et al.: https://www.youtube.com/playlist?list=PL_iWQOsE6TfURIIhCrlt-wj9ByIVpbfGc
#ArtificialIntelligence #DeepLearning #ReinforcementLearning
Post #5642
1.51K
Post #5641
1.61K
Papers with Code partners with arXiv
Robert Stojnic : https://medium.com/paperswithcode/papers-with-code-partners-with-arxiv-ecc362883167
@ArtificialIntelligenceArticles
#MachineLearning #arXiv #Code
Robert Stojnic : https://medium.com/paperswithcode/papers-with-code-partners-with-arxiv-ecc362883167
@ArtificialIntelligenceArticles
#MachineLearning #arXiv #Code
Post #5640
1.59K
Post #5639
1.51K
Very nice article by M. Aminian in SIAM News entitled "The Connection Between Applied Mathematics and Deep Learning".
The article dissects and explains in very clear terms the gist of my plenary talk at the recent SIAM Mathematics of Data Science conference entitled "The Deep Learning - Applied Mathematics Connection".
It discusses the specifics of optimization as it pertains to DL (stochastic optimization in high-dimensional spaces), the surprising fact that widely overparamterized DL models work well, the use of DL in traditional applications of scientific computing: PDE solving, lattice quantum computation, graph convolutional nets and the connection with Fourier transform, and the rising topic of self-supervised learning.
The methods of Applied Mathematics could help explain some of the mysteries of deep learning
SIAM News paper: https://sinews.siam.org/Details-Page/the-connection-between-applied-mathematics-and-deep-learning
Video of my talk: https://youtu.be/9sqoe_krQ1E
The article dissects and explains in very clear terms the gist of my plenary talk at the recent SIAM Mathematics of Data Science conference entitled "The Deep Learning - Applied Mathematics Connection".
It discusses the specifics of optimization as it pertains to DL (stochastic optimization in high-dimensional spaces), the surprising fact that widely overparamterized DL models work well, the use of DL in traditional applications of scientific computing: PDE solving, lattice quantum computation, graph convolutional nets and the connection with Fourier transform, and the rising topic of self-supervised learning.
The methods of Applied Mathematics could help explain some of the mysteries of deep learning
SIAM News paper: https://sinews.siam.org/Details-Page/the-connection-between-applied-mathematics-and-deep-learning
Video of my talk: https://youtu.be/9sqoe_krQ1E
Post #5638
1.19K
MiniTorch
Sasha Rush and Ge Gao : https://minitorch.github.io/index.html
#DeepLearning #PyTorch #Python
Sasha Rush and Ge Gao : https://minitorch.github.io/index.html
#DeepLearning #PyTorch #Python
Post #5637
1.45K
Post #5636
1.7K
Post #5635
1.41K
Post #5634
1.23K
Post #5633
1.21K
Post #5632
1.39K
tsBNgen, a Python Library to Generate Synthetic Data From an Arbitrary Bayesian Network.
Paper Summary: https://www.marktechpost.com/2020/09/15/tsbngen-a-python-library-to-generate-synthetic-data-from-an-arbitrary-bayesian-network/
Paper: https://arxiv.org/pdf/2009.04595.pdf
Github: https://github.com/manitadayon/tsBNgen/blob/master/tsbngen.pdf
MarkTechPost tsBNgen, a Python Library to Generate Synthetic Data From an Arbitrary Bayesian Network. tsBNgen, a Python Library to Generate Time Series and Sequential Data Based on an Arbitrary Dynamic Bayesian Network. Paper Summary: https://www.marktechpost.com/2020/09/15/tsbngen-a-python-library-to-generate-synthetic-data-from-an-arbitrary-bayesian-network/
Paper: https://arxiv.org/pdf/2009.04595.pdf
Github: https://github.com/manitadayon/tsBNgen/blob/master/tsbngen.pdf
Post #5631
2.11K
Microsoft releases a new version of DeepSpeed tool to enable the creation of deep learning models with a trillion parameters
Github: https://github.com/microsoft/DeepSpeed
Source: https://www.microsoft.com/en-us/research/project/deepspeed/
Paper: https://arxiv.org/abs/1910.02054
Github: https://github.com/microsoft/DeepSpeed
Source: https://www.microsoft.com/en-us/research/project/deepspeed/
Paper: https://arxiv.org/abs/1910.02054
Post #5630
1.51K
Post #5629
1.2K
ConvNets helping the visually impaired.
Wearable AI devices are going to become ubiquitous over the next decade.
I met the OrCam folks when they were just starting.
They've made a lot of progress in low-power ConvNet chips.
OrCam was founded by Amnon Shashua who also founded MobilEye (now part of Intel).
https://m.youtube.com/watch?feature=youtu.be&v=82oAUwwwG2Q
Post by yan lecun
YouTube OrCam and Messi are Empowering the Blind Community Watch the magical moments when Leo Messi met exceptional individuals from the blind community and gave them the gift of independence, OrCam MyEye.
OrCam MyEye is the world's most advanced assistive wearable device that reads text, recognizes faces, identifies… Wearable AI devices are going to become ubiquitous over the next decade.
I met the OrCam folks when they were just starting.
They've made a lot of progress in low-power ConvNet chips.
OrCam was founded by Amnon Shashua who also founded MobilEye (now part of Intel).
https://m.youtube.com/watch?feature=youtu.be&v=82oAUwwwG2Q
Post by yan lecun
Post #5628
1.27K
Post #5627
1.32K
Post #5626
1.51K
