Data-Efficient Image Recognition with Contrastive Predictive Coding
Article: https://arxiv.org/abs/1905.09272
Article: https://arxiv.org/abs/1905.09272
Moving Camera, Moving People: A Deep Learning Approach to Depth Prediction
http://ai.googleblog.com/2019/05/moving-camera-moving-people-deep.html
http://ai.googleblog.com/2019/05/moving-camera-moving-people-deep.html
research.google
Moving Camera, Moving People: A Deep Learning Approach to Depth Prediction
Posted by Tali Dekel, Research Scientist and Forrester Cole, Software Engineer, Machine Perception The human visual system has a remarkable abili...
How to Perform Object Detection in Photographs Using Mask R-CNN with Keras
https://machinelearningmastery.com/how-to-perform-object-detection-in-photographs-with-mask-r-cnn-in-keras/
https://machinelearningmastery.com/how-to-perform-object-detection-in-photographs-with-mask-r-cnn-in-keras/
TensorWatch: a debugging and visualization tool designed for deep learning
https://github.com/microsoft/tensorwatch
https://github.com/microsoft/tensorwatch
GitHub
GitHub - microsoft/tensorwatch: Debugging, monitoring and visualization for Python Machine Learning and Data Science
Debugging, monitoring and visualization for Python Machine Learning and Data Science - microsoft/tensorwatch
Estimators, Loss Functions, Optimizers —Core of ML Algorithms
https://towardsdatascience.com/estimators-loss-functions-optimizers-core-of-ml-algorithms-d603f6b0161a]
https://towardsdatascience.com/estimators-loss-functions-optimizers-core-of-ml-algorithms-d603f6b0161a]
Medium
Estimators, Loss Functions, Optimizers —Core of ML Algorithms
In order to understand how a machine learning algorithm learns from data to predict an outcome, it is essential to understand the…
Torchvision 0.3: segmentation, detection models, new datasets
https://pytorch.org/blog/torchvision03/
https://pytorch.org/blog/torchvision03/
COBRA: Data-Efficient Model-Based RL through Unsupervised Object Discovery and Curiosity-Driven Exploration
https://arxiv.org/abs/1905.09275
https://arxiv.org/abs/1905.09275
illustrated Artificial Intelligence cheatsheets covering the content of the CS 221 class
Link: https://stanford.edu/~shervine/teaching/cs-221/
Reflex-based models with Machine Learning: https://stanford.edu/~shervine/teaching/cs-221/cheatsheet-reflex-models
Link: https://stanford.edu/~shervine/teaching/cs-221/
Reflex-based models with Machine Learning: https://stanford.edu/~shervine/teaching/cs-221/cheatsheet-reflex-models
stanford.edu
Teaching - CS 221
Teaching page of Shervine Amidi, Graduate Student at Stanford University.
How degenerate is the parametrization of neural networks with the ReLU activation function?
https://arxiv.org/abs/1905.09803
https://arxiv.org/abs/1905.09803
How to Perform Object Detection With YOLOv3 in Keras
https://machinelearningmastery.com/how-to-perform-object-detection-with-yolov3-in-keras/
https://machinelearningmastery.com/how-to-perform-object-detection-with-yolov3-in-keras/
MachineLearningMastery.com
How to Perform Object Detection With YOLOv3 in Keras - MachineLearningMastery.com
Object detection is a task in computer vision that involves identifying the presence, location, and type of one or more objects in a given photograph. It is a challenging problem that involves building upon methods for object recognition (e.g. where are they)…
Forwarded from Artificial Intelligence
Unsupervised Learning with Graph Neural Networks
video: http://www.ipam.ucla.edu/programs/workshops/workshop-iv-deep-geometric-learning-of-big-data-and-applications/?tab=schedule
guide: http://helper.ipam.ucla.edu/publications/glws4/glws4_15546.pdf
video: http://www.ipam.ucla.edu/programs/workshops/workshop-iv-deep-geometric-learning-of-big-data-and-applications/?tab=schedule
guide: http://helper.ipam.ucla.edu/publications/glws4/glws4_15546.pdf
IPAM
Workshop IV: Deep Geometric Learning of Big Data and Applications - IPAM
Augmented Neural ODEs
Github: https://github.com/EmilienDupont/augmented-neural-odes
Article: https://arxiv.org/abs/1904.01681
Github: https://github.com/EmilienDupont/augmented-neural-odes
Article: https://arxiv.org/abs/1904.01681
GitHub
GitHub - EmilienDupont/augmented-neural-odes: Pytorch implementation of Augmented Neural ODEs :sunflower:
Pytorch implementation of Augmented Neural ODEs :sunflower: - EmilienDupont/augmented-neural-odes
SimpleSelfAttention
The purpose of this repository is two-fold:
-demonstrate improvements brought by the use of a self-attention layer in an image -classification model.
introduce a new layer which I call SimpleSelfAttention
https://github.com/sdoria/SimpleSelfAttention
The purpose of this repository is two-fold:
-demonstrate improvements brought by the use of a self-attention layer in an image -classification model.
introduce a new layer which I call SimpleSelfAttention
https://github.com/sdoria/SimpleSelfAttention
GitHub
GitHub - sdoria/SimpleSelfAttention: A simpler version of the self-attention layer from SAGAN, and some image classification results.
A simpler version of the self-attention layer from SAGAN, and some image classification results. - sdoria/SimpleSelfAttention
How to Train an Object Detection Model to Find Kangaroos in Photographs (R-CNN with Keras)
https://machinelearningmastery.com/how-to-train-an-object-detection-model-with-keras/
https://machinelearningmastery.com/how-to-train-an-object-detection-model-with-keras/
MachineLearningMastery.com
How to Train an Object Detection Model with Keras - MachineLearningMastery.com
Object detection is a challenging computer vision task that involves predicting both where the objects are in the image and what type of objects were detected. The Mask Region-based Convolutional Neural Network, or Mask R-CNN, model is one of the state-of…
EfficientNets
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
link: https://arxiv.org/abs/1905.11946.
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
link: https://arxiv.org/abs/1905.11946.
arXiv.org
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Convolutional Neural Networks (ConvNets) are commonly developed at a fixed resource budget, and then scaled up for better accuracy if more resources are available. In this paper, we systematically...
Multi-Sample Dropout for Accelerated Training and Better Generalization
Link: https://arxiv.org/abs/1905.09788
Link: https://arxiv.org/abs/1905.09788
arXiv.org
Multi-Sample Dropout for Accelerated Training and Better Generalization
Dropout is a simple but efficient regularization technique for achieving better generalization of deep neural networks (DNNs); hence it is widely used in tasks based on DNNs. During training,...
A Gentle Introduction to Deep Learning for Face Recognition
https://machinelearningmastery.com/introduction-to-deep-learning-for-face-recognition/
https://machinelearningmastery.com/introduction-to-deep-learning-for-face-recognition/
MachineLearningMastery.com
A Gentle Introduction to Deep Learning for Face Recognition - MachineLearningMastery.com
Face recognition is the problem of identifying and verifying people in a photograph by their face. It is a task that is trivially performed by humans, even under varying light and when faces are changed by age or obstructed with accessories and facial hair.…