Forwarded from بینام
Practical Computer Vision Applications (en).pdf
9.6 MB
Forwarded from Computer Science and Programming
YOLACT (You Only Look At CoefficienTs) - Real-time Instance Segmentation
Results are impressive, above 30 FPS on COCO test-dev
Results are impressive, above 30 FPS on COCO test-dev
AI & Art
@Machine_learn
some artist use the large collections of #data & #ML #algorithms to create mesmerizing & dynamic #installations
watch the video —> https://youtu.be/I-EIVlHvHRM
@Machine_learn
some artist use the large collections of #data & #ML #algorithms to create mesmerizing & dynamic #installations
watch the video —> https://youtu.be/I-EIVlHvHRM
YouTube
How This Guy Uses A.I. to Create Art | Obsessed | WIRED
Artist Refik Anadol doesn't work with paintbrushes or clay. Instead, he uses large collections of data and machine learning algorithms to create mesmerizing and dynamic installations.
Machine Hallucination at Artechouse NYC: https://www.artechouse.com/nyc…
Machine Hallucination at Artechouse NYC: https://www.artechouse.com/nyc…
Forwarded from بینام
Machine Learning and Security (en).pdf
6.4 MB
Forwarded from بینام
[Jojo_John_Moolayil]_Learn_Keras_for_Deep_Neural_N.pdf
2.7 MB
Learn Keras for Deep Neural Networks: A Fast-Track Approach to Modern Deep Learning with Python
@Machine_learn
@Machine_learn
@Machine_learn
Learning to See Transparent Objects
ClearGrasp uses 3 neural networks: a network to estimate surface normals, one for occlusion boundaries (depth discontinuities), and one that masks transparent objects
Google research: https://ai.googleblog.com/2020/02/learning-to-see-transparent-objects.html
Code: https://github.com/Shreeyak/cleargrasp
Dataset: https://sites.google.com/view/transparent-objects
3D Shape Estimation of Transparent Objects for Manipulation: https://sites.google.com/view/cleargrasp
Learning to See Transparent Objects
ClearGrasp uses 3 neural networks: a network to estimate surface normals, one for occlusion boundaries (depth discontinuities), and one that masks transparent objects
Google research: https://ai.googleblog.com/2020/02/learning-to-see-transparent-objects.html
Code: https://github.com/Shreeyak/cleargrasp
Dataset: https://sites.google.com/view/transparent-objects
3D Shape Estimation of Transparent Objects for Manipulation: https://sites.google.com/view/cleargrasp
Machine learning books and papers pinned «https://t.iss.one/Machine_learn»
GANILLA: Generative Adversarial Networks for Image to Illustration Translation.
Github: https://github.com/giddyyupp/ganilla
Dataset: https://github.com/giddyyupp/ganilla/blob/master/docs/datasets.md
Paper: https://arxiv.org/abs/2002.05638v1
Github: https://github.com/giddyyupp/ganilla
Dataset: https://github.com/giddyyupp/ganilla/blob/master/docs/datasets.md
Paper: https://arxiv.org/abs/2002.05638v1
Deep learning of dynamical attractors from time series measurements
Embed complex time series using autoencoders and a loss function based on penalizing false-nearest-neighbors.
Code: https://github.com/williamgilpin/fnn
Paper: https://arxiv.org/abs/2002.05909
Embed complex time series using autoencoders and a loss function based on penalizing false-nearest-neighbors.
Code: https://github.com/williamgilpin/fnn
Paper: https://arxiv.org/abs/2002.05909
GitHub
GitHub - williamgilpin/fnn: Embed strange attractors using a regularizer for autoencoders
Embed strange attractors using a regularizer for autoencoders - williamgilpin/fnn
Forwarded from بینام
Deep Reinforcement Learning with Guar Perf.pdf
11.2 MB
@Machine_learn
IBM Data Science and AI Programs Free for 30 Days
https://onlinecoursesgalore.com/ibm-data-science-ai-coursera/
Coursera: https://www.coursera.org/promo/ibmdscommunity?ranMID=40328&ranEAID
IBM Data Science and AI Programs Free for 30 Days
https://onlinecoursesgalore.com/ibm-data-science-ai-coursera/
Coursera: https://www.coursera.org/promo/ibmdscommunity?ranMID=40328&ranEAID
Online Courses Galore
IBM Data Science and AI Programs on Coursera Free for 30 Days
Coursera 30 days of free access to IBM data science and artificial intelligence specialization & professional cert programs until June 2022
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@Machine_learn
Exploring Transfer Learning with T5: the Text-To-Text Transfer Transformer
https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html
Code: https://colab.research.google.com/github/google-research/text-to-text-transfer-transformer/blob/master/notebooks/t5-trivia.ipynb
Github: https://github.com/google-research/text-to-text-transfer-transformer
Exploring Transfer Learning with T5: the Text-To-Text Transfer Transformer
https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html
Code: https://colab.research.google.com/github/google-research/text-to-text-transfer-transformer/blob/master/notebooks/t5-trivia.ipynb
Github: https://github.com/google-research/text-to-text-transfer-transformer
@Machine_learn
Graph ML Surveys
A good way to start in this domain is to read what people already have done.
Videos
* Learning on Non-Euclidean Domains
* Stanford Course CS 224w
@Machine_learn
GNN
* Graph Neural Networks: A Review of Methods and Applications 2018
* A Comprehensive Survey on Graph Neural Networks 2019
* A Gentle Introduction to Deep Learning for Graphs 2019
* Deep Learning on Graphs: A Survey 2018
* Relational inductive biases, deep learning, and graph networks 2018
* Geometric deep learning: going beyond Euclidean data 2016
* Graph Neural Networks for Small Graph and Giant Network Representation Learning: An Overview 2019
@Machine_learn
Graph kernels
* A Survey on Graph Kernels 2019
* Graph Kernels: A Survey 2019
@Machine_learn
Adversarial Attacks
* Adversarial Attack and Defense on Graph Data: A Survey 2018
@Machine_learn
Representation Learning
* Learning Representations of Graph Data -- A Survey 2019
* Representation Learning on Graphs: Methods and Applications 2017
@Machine_learn
Graph ML Surveys
A good way to start in this domain is to read what people already have done.
Videos
* Learning on Non-Euclidean Domains
* Stanford Course CS 224w
@Machine_learn
GNN
* Graph Neural Networks: A Review of Methods and Applications 2018
* A Comprehensive Survey on Graph Neural Networks 2019
* A Gentle Introduction to Deep Learning for Graphs 2019
* Deep Learning on Graphs: A Survey 2018
* Relational inductive biases, deep learning, and graph networks 2018
* Geometric deep learning: going beyond Euclidean data 2016
* Graph Neural Networks for Small Graph and Giant Network Representation Learning: An Overview 2019
@Machine_learn
Graph kernels
* A Survey on Graph Kernels 2019
* Graph Kernels: A Survey 2019
@Machine_learn
Adversarial Attacks
* Adversarial Attack and Defense on Graph Data: A Survey 2018
@Machine_learn
Representation Learning
* Learning Representations of Graph Data -- A Survey 2019
* Representation Learning on Graphs: Methods and Applications 2017
@Machine_learn
CS236605: Deep Learning
Lecture 11: Learning on Non-Euclidean Domains
Toeplitz operators, graphs, fields, gradients, divergence, Laplace-Beltramioperator, non-euclidean convolution, spectral and spatial CNN for graphs.
Machine learning books and papers pinned «@Machine_learn Graph ML Surveys A good way to start in this domain is to read what people already have done. Videos * Learning on Non-Euclidean Domains * Stanford Course CS 224w @Machine_learn GNN * Graph Neural Networks: A Review of Methods and Applications…»
@Machine_learn
Fresh picks from ArXiv
ICML 20 submissions, AISTATS 20, graphs in math, and Stephen Hawking 👨🔬
ICML 2020 submissions
Fast Detection of Maximum Common Subgraph via Deep Q-Learning (https://arxiv.org/abs/2002.03129)
Random Features Strengthen Graph Neural Networks (https://arxiv.org/abs/2002.03155)
Hierarchical Generation of Molecular Graphs using Structural Motifs (https://arxiv.org/pdf/2002.03230.pdf)
Graph Neural Distance Metric Learning with Graph-Bert (https://arxiv.org/abs/2002.03427)
Segmented Graph-Bert for Graph Instance Modeling (https://arxiv.org/abs/2002.03283)
Haar Graph Pooling (https://arxiv.org/abs/1909.11580)
Constant Time Graph Neural Networks (https://arxiv.org/abs/1901.07868)
@Machine_learn
AISTATS 20
Laplacian-Regularized Graph Bandits: Algorithms and Theoretical Analysis (https://arxiv.org/abs/1907.05632)
@Machine_learn
Math
Some arithmetical problems that are obtained by analyzing proofs and infinite graphs (https://arxiv.org/abs/2002.03075)
Extra pearls in graph theory (https://arxiv.org/abs/1812.06627)
Distance Metric Learning for Graph Structured Data (https://arxiv.org/abs/2002.00727)
@Machine_learn
Surveys
Generalized metric spaces. Relations with graphs, ordered sets and automata : A survey (https://arxiv.org/abs/2002.03019)
@Machine_learn
Stephen Hawking 👨🔬
Stephen William Hawking: A Biographical Memoir (https://arxiv.org/abs/2002.03185)
Fresh picks from ArXiv
ICML 20 submissions, AISTATS 20, graphs in math, and Stephen Hawking 👨🔬
ICML 2020 submissions
Fast Detection of Maximum Common Subgraph via Deep Q-Learning (https://arxiv.org/abs/2002.03129)
Random Features Strengthen Graph Neural Networks (https://arxiv.org/abs/2002.03155)
Hierarchical Generation of Molecular Graphs using Structural Motifs (https://arxiv.org/pdf/2002.03230.pdf)
Graph Neural Distance Metric Learning with Graph-Bert (https://arxiv.org/abs/2002.03427)
Segmented Graph-Bert for Graph Instance Modeling (https://arxiv.org/abs/2002.03283)
Haar Graph Pooling (https://arxiv.org/abs/1909.11580)
Constant Time Graph Neural Networks (https://arxiv.org/abs/1901.07868)
@Machine_learn
AISTATS 20
Laplacian-Regularized Graph Bandits: Algorithms and Theoretical Analysis (https://arxiv.org/abs/1907.05632)
@Machine_learn
Math
Some arithmetical problems that are obtained by analyzing proofs and infinite graphs (https://arxiv.org/abs/2002.03075)
Extra pearls in graph theory (https://arxiv.org/abs/1812.06627)
Distance Metric Learning for Graph Structured Data (https://arxiv.org/abs/2002.00727)
@Machine_learn
Surveys
Generalized metric spaces. Relations with graphs, ordered sets and automata : A survey (https://arxiv.org/abs/2002.03019)
@Machine_learn
Stephen Hawking 👨🔬
Stephen William Hawking: A Biographical Memoir (https://arxiv.org/abs/2002.03185)