Building effective FAQ with Knowledge Bases, BERT and Sentence Clustering
https://towardsdatascience.com/building-effective-faq-with-knowledge-bases-bert-and-sentence-clustering-b0c15727bbdb
https://towardsdatascience.com/building-effective-faq-with-knowledge-bases-bert-and-sentence-clustering-b0c15727bbdb
Medium
Building effective FAQ with Knowledge Bases, BERT and Sentence Clustering
How to identify and expose the knowledge that matters
Fast Gradient Boosting with CatBoost
https://heartbeat.fritz.ai/fast-gradient-boosting-with-catboost-38779b0d5d9a
https://heartbeat.fritz.ai/fast-gradient-boosting-with-catboost-38779b0d5d9a
Using Selective Attention in Reinforcement Learning Agents
https://ai.googleblog.com/2020/06/using-selective-attention-in.html
https://ai.googleblog.com/2020/06/using-selective-attention-in.html
Googleblog
Using Selective Attention in Reinforcement Learning Agents
Fourier Features Let Networks Learn
High Frequency Functions in Low Dimensional Domains
https://people.eecs.berkeley.edu/~bmild/fourfeat/index.html
High Frequency Functions in Low Dimensional Domains
https://people.eecs.berkeley.edu/~bmild/fourfeat/index.html
Neural Manifold Ordinary Differential Equations
Article: https://arxiv.org/abs/2006.10254
Github: https://github.com/CUVL/Neural-Manifold-Ordinary-Differential-Equations
Article: https://arxiv.org/abs/2006.10254
Github: https://github.com/CUVL/Neural-Manifold-Ordinary-Differential-Equations
How to Avoid Data Leakage When Performing Data Preparation
https://machinelearningmastery.com/data-preparation-without-data-leakage/
https://machinelearningmastery.com/data-preparation-without-data-leakage/
MachineLearningMastery.com
How to Avoid Data Leakage When Performing Data Preparation - MachineLearningMastery.com
Data preparation is the process of transforming raw data into a form that is appropriate for modeling. A naive approach to preparing data applies the transform on the entire dataset before evaluating the performance of the model. This results in a problem…
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
Given a low-resolution input image, PULSE searches the outputs of a generative model for high-resolution images that are perceptually realistic and downscale correctly.
Github: https://github.com/adamian98/pulse
Paper: https://arxiv.org/abs/2003.03808v1
Given a low-resolution input image, PULSE searches the outputs of a generative model for high-resolution images that are perceptually realistic and downscale correctly.
Github: https://github.com/adamian98/pulse
Paper: https://arxiv.org/abs/2003.03808v1
Google & DeepMind Researchers Revamp ImageNet
https://syncedreview.com/2020/06/23/google-deepmind-researchers-revamp-imagenet/
ImageNet: https://arxiv.org/pdf/2006.07159.pdf
https://syncedreview.com/2020/06/23/google-deepmind-researchers-revamp-imagenet/
ImageNet: https://arxiv.org/pdf/2006.07159.pdf
Synced | AI Technology & Industry Review
Google & DeepMind Researchers Revamp ImageNet | Synced
Google Brain in Zürich and DeepMind London researchers believe one of the world's most popular image databases may need a makeover.
A state-of-the-art, self-supervised framework for video understanding
https://ai.facebook.com/blog/a-state-of-the-art-self-supervised-framework-for-video-understanding/
https://ai.facebook.com/blog/a-state-of-the-art-self-supervised-framework-for-video-understanding/
Facebook
A state-of-the-art, self-supervised framework for video understanding
Generalized Data Transformations give us a systematic way of robustly learning the relationship between audio and visual information in order to learn about the structure of the world.
Learning Semantically Enhanced Feature for Fine-Grained Image Classification
Gitgub: https://github.com/cswluo/SEF
Paper: https://arxiv.org/abs/2006.13457v1
Gitgub: https://github.com/cswluo/SEF
Paper: https://arxiv.org/abs/2006.13457v1
Building AI Trading Systems
Lessons learned building a profitable algorithmic trading system using Reinforcement Learning techniques.
https://dennybritz.com/blog/ai-trading/
Lessons learned building a profitable algorithmic trading system using Reinforcement Learning techniques.
https://dennybritz.com/blog/ai-trading/
Dennybritz
Building AI Trading Systems
Lessons learned building a profitable algorithmic trading system using Reinforcement Learning techniques.
Tensor Programs II: Neural Tangent Kernel for Any Architecture
which shows the tangent kernel of any randomly initialized neural network converges in the large width limit.
Github: https://github.com/thegregyang/NTK4A
Paper: https://arxiv.org/abs/2006.14548
which shows the tangent kernel of any randomly initialized neural network converges in the large width limit.
Github: https://github.com/thegregyang/NTK4A
Paper: https://arxiv.org/abs/2006.14548
8 Top Books on Data Cleaning and Feature Engineering
https://machinelearningmastery.com/books-on-data-cleaning-data-preparation-and-feature-engineering/
https://machinelearningmastery.com/books-on-data-cleaning-data-preparation-and-feature-engineering/
SmartReply for YouTube Creators
https://ai.googleblog.com/2020/07/smartreply-for-youtube-creators.html
https://ai.googleblog.com/2020/07/smartreply-for-youtube-creators.html
Googleblog
SmartReply for YouTube Creators
Announcing CUDA on Windows Subsystem for Linux 2
https://developer.nvidia.com/blog/announcing-cuda-on-windows-subsystem-for-linux-2/
https://developer.nvidia.com/blog/announcing-cuda-on-windows-subsystem-for-linux-2/
NVIDIA Technical Blog
Announcing CUDA on Windows Subsystem for Linux 2
In response to popular demand, Microsoft announced a new feature of the Windows Subsystem for Linux 2 (WSL 2)—GPU acceleration—at the Build conference in May 2020. This feature opens the gate for many…