Not All Memories are Created Equal: Learning to Expire
Github: https://github.com/lucidrains/learning-to-expire-pytorch
Paper: https://openreview.net/forum?id=ZVBtN6B_6i7
@Machine_learn
Github: https://github.com/lucidrains/learning-to-expire-pytorch
Paper: https://openreview.net/forum?id=ZVBtN6B_6i7
@Machine_learn
🚀 DALL·E is a 12-billion parameter version of GPT-3 trained to generate images from text descriptions
Introduction: https://openai.com/blog/tags/multimodal/
Deepmind Blog: https://openai.com/blog/dall-e/
Github: https://github.com/openai/CLIP
Paper: https://cdn.openai.com/papers/Learning_Transferable_Visual_Models_From_Natural_Language.pdf
Colab: https://colab.research.google.com/github/openai/clip/blob/master/Interacting_with_CLIP.ipynb
@Machine_learn
Introduction: https://openai.com/blog/tags/multimodal/
Deepmind Blog: https://openai.com/blog/dall-e/
Github: https://github.com/openai/CLIP
Paper: https://cdn.openai.com/papers/Learning_Transferable_Visual_Models_From_Natural_Language.pdf
Colab: https://colab.research.google.com/github/openai/clip/blob/master/Interacting_with_CLIP.ipynb
@Machine_learn
🔇 Deep Noise Suppression Challenge – ICASSP 2021
Github: https://github.com/microsoft/DNS-Challenge
Paper: https://arxiv.org/abs/2101.01902v1
Training and test datasets: https://github.com/microsoft/DNS-Challenge/tree/master/datasets
Challenge: https://www.microsoft.com/en-us/research/academic-program/deep-noise-suppression-challenge-icassp-2021/
@Machine_learn
Github: https://github.com/microsoft/DNS-Challenge
Paper: https://arxiv.org/abs/2101.01902v1
Training and test datasets: https://github.com/microsoft/DNS-Challenge/tree/master/datasets
Challenge: https://www.microsoft.com/en-us/research/academic-program/deep-noise-suppression-challenge-icassp-2021/
@Machine_learn
GitHub
GitHub - microsoft/DNS-Challenge: This repo contains the scripts, models, and required files for the Deep Noise Suppression (DNS)…
This repo contains the scripts, models, and required files for the Deep Noise Suppression (DNS) Challenge. - GitHub - microsoft/DNS-Challenge: This repo contains the scripts, models, and required...
👉Lecture Notes for Linear Algebra Featuring Python
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GitHub link : https://github.com/MacroAnalyst/Linear_Algebra_With_Python
@Machine_learn
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GitHub link : https://github.com/MacroAnalyst/Linear_Algebra_With_Python
@Machine_learn
GitHub
GitHub - weijie-chen/Linear-Algebra-With-Python: Lecture Notes for Linear Algebra Featuring Python. This series of lecture notes…
Lecture Notes for Linear Algebra Featuring Python. This series of lecture notes will walk you through all the must-know concepts that set the foundation of data science or advanced quantitative ski...
DENet: a deep architecture for audio surveillance applications
GIthub: https://github.com/MiviaLab/DENet
Paper: https://link.springer.com/article/10.1007/s00521-020-05572-5
@Machine_learn
GIthub: https://github.com/MiviaLab/DENet
Paper: https://link.springer.com/article/10.1007/s00521-020-05572-5
@Machine_learn
GitHub
GitHub - MiviaLab/DENet: This repository contains the code related to the paper 'DENet: a deep architecture for audio surveillance…
This repository contains the code related to the paper 'DENet: a deep architecture for audio surveillance applications'. - MiviaLab/DENet
Gender recognition in the wild: a robustness evaluation over corrupted images
Github: https://github.com/MiviaLab/GenderRecognitionFramework
Paper: https://link.springer.com/article/10.1007/s12652-020-02750-0
@Machine_lear
Github: https://github.com/MiviaLab/GenderRecognitionFramework
Paper: https://link.springer.com/article/10.1007/s12652-020-02750-0
@Machine_lear
GitHub
GitHub - MiviaLab/GenderRecognitionFramework
Contribute to MiviaLab/GenderRecognitionFramework development by creating an account on GitHub.
Superpixel-based Refinement for Object Proposal Generation
Github: https://github.com/chwilms/superpixelRefinement
Paper: https://arxiv.org/abs/2101.04574v1
@Machine_learn
Github: https://github.com/chwilms/superpixelRefinement
Paper: https://arxiv.org/abs/2101.04574v1
@Machine_learn
GitHub
GitHub - chwilms/superpixelRefinement: Superpixel-based Refinement for Object Proposal Generation (ICPR 2020)
Superpixel-based Refinement for Object Proposal Generation (ICPR 2020) - chwilms/superpixelRefinement
🔥 Fast convolutional neural networks on FPGAs with hls4ml
Github: https://github.com/fastmachinelearning/hls4ml
Paper: https://arxiv.org/abs/2101.05108v1
Documentation: https://fastmachinelearning.org/hls4ml/
@Machine_learn
Github: https://github.com/fastmachinelearning/hls4ml
Paper: https://arxiv.org/abs/2101.05108v1
Documentation: https://fastmachinelearning.org/hls4ml/
@Machine_learn
👉Lecture Notes for Linear Algebra Featuring Python
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GitHub link : https://github.com/MacroAnalyst/Linear_Algebra_With_Python
@Machine_learn
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GitHub link : https://github.com/MacroAnalyst/Linear_Algebra_With_Python
@Machine_learn
GitHub
GitHub - weijie-chen/Linear-Algebra-With-Python: Lecture Notes for Linear Algebra Featuring Python. This series of lecture notes…
Lecture Notes for Linear Algebra Featuring Python. This series of lecture notes will walk you through all the must-know concepts that set the foundation of data science or advanced quantitative ski...
A Visual Intro to NumPy and Data Representation
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Link : https://jalammar.github.io/visual-numpy/
@Machine_learn
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Link : https://jalammar.github.io/visual-numpy/
@Machine_learn
jalammar.github.io
A Visual Intro to NumPy and Data Representation
Discussions:
Hacker News (366 points, 21 comments), Reddit r/MachineLearning (256 points, 18 comments)
Translations: Chinese 1, Chinese 2, Japanese, Korean
The NumPy package is the workhorse of data analysis, machine learning, and scientific computing…
Hacker News (366 points, 21 comments), Reddit r/MachineLearning (256 points, 18 comments)
Translations: Chinese 1, Chinese 2, Japanese, Korean
The NumPy package is the workhorse of data analysis, machine learning, and scientific computing…
Self-Adaptive Training
Github: https://github.com/LayneH/self-adaptive-training
Paper: https://arxiv.org/abs/2101.08732v1
@Machine_learn
Github: https://github.com/LayneH/self-adaptive-training
Paper: https://arxiv.org/abs/2101.08732v1
@Machine_learn
Stabilizing Live Speech Translation in Google Translate
http://ai.googleblog.com/2021/01/stabilizing-live-speech-translation-in.html
@Machine_learn
http://ai.googleblog.com/2021/01/stabilizing-live-speech-translation-in.html
@Machine_learn
research.google
Stabilizing Live Speech Translation in Google Translate
Posted by Naveen Arivazhagan, Senior Software Engineer and Colin Cherry, Staff Research Scientist, Google Research The transcription feature in the...
TIN: Transferable Interactiveness Network
Github: https://github.com/DirtyHarryLYL/Transferable-Interactiveness-Network
HOI-Learning-List: https://github.com/DirtyHarryLYL/HOI-Learning-List
Paper: https://arxiv.org/abs/2101.10292v1
@Machine_learn
Github: https://github.com/DirtyHarryLYL/Transferable-Interactiveness-Network
HOI-Learning-List: https://github.com/DirtyHarryLYL/HOI-Learning-List
Paper: https://arxiv.org/abs/2101.10292v1
@Machine_learn
Improving Mobile App Accessibility with Icon Detection
http://ai.googleblog.com/2021/01/improving-mobile-app-accessibility-with.html
@Machine_learn
http://ai.googleblog.com/2021/01/improving-mobile-app-accessibility-with.html
@Machine_learn
research.google
Improving Mobile App Accessibility with Icon Detection
Posted by Gilles Baechler and Srinivas Sunkara, Software Engineers, Google Research Voice Access enables users to control their Android device hand...
Evaluating Design Trade-offs in Visual Model-Based Reinforcement Learning
http://ai.googleblog.com/2021/02/evaluating-design-trade-offs-in-visual.html
@Machin_learn
http://ai.googleblog.com/2021/02/evaluating-design-trade-offs-in-visual.html
@Machin_learn
research.google
Evaluating Design Trade-offs in Visual Model-Based Reinforcement Learning
Posted by Mohammad Babaeizadeh, Research Engineer and Dumitru Erhan, Research Scientist, Google Research Model-free reinforcement learning has been...
2006.02493.pdf
2.4 MB
Adaptive Checkpoint Adjoint Method for Gradient Estimation in Neural ODE #paper @Machine_learn