From Machine Learning to Machine Reasoning
Bottou et al.: Link
#machinelearning #artificialintelligence #paper
@pythonicAI
Bottou et al.: Link
#machinelearning #artificialintelligence #paper
@pythonicAI
arXiv.org
From Machine Learning to Machine Reasoning
A plausible definition of "reasoning" could be "algebraically manipulating previously acquired knowledge in order to answer a new question". This definition covers first-order logical inference or...
AutoGMM: Automatic Gaussian Mixture Modeling in Python.
http://arxiv.org/abs/1909.02688
#paper #machinelearning #artificialintelligence
@pythonicAI
http://arxiv.org/abs/1909.02688
#paper #machinelearning #artificialintelligence
@pythonicAI
arXiv.org
AutoGMM: Automatic and Hierarchical Gaussian Mixture Modeling in Python
Background: Gaussian mixture modeling is a fundamental tool in clustering, as well as discriminant analysis and semiparametric density estimation. However, estimating the optimal model for any...
Y-Autoencoders: disentangling latent representations via sequential-encoding
https://arxiv.org/abs/1907.10949
#paper #artificialintelligence
@pythonicAI
https://arxiv.org/abs/1907.10949
#paper #artificialintelligence
@pythonicAI
arXiv.org
Y-Autoencoders: disentangling latent representations via...
In the last few years there have been important advancements in generative models with the two dominant approaches being Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs)....
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
How normalization applied to layers helps to reach faster convergence.
https://arxiv.org/abs/1502.03167
#paper #deeplearning #artificialintelligence
@pythonicAI
How normalization applied to layers helps to reach faster convergence.
https://arxiv.org/abs/1502.03167
#paper #deeplearning #artificialintelligence
@pythonicAI
arXiv.org
Batch Normalization: Accelerating Deep Network Training by...
Training Deep Neural Networks is complicated by the fact that the distribution of each layer's inputs changes during training, as the parameters of the previous layers change. This slows down the...
CvxNets: Learnable Convex Decomposition
by Geoffrey Hinton, Boyang Deng, Kyle Genova, Soroosh Yazdani, Sofien Bouaziz, Andrea Tagliasacchi
https://arxiv.org/abs/1909.05736
#paper #artificialintelligence
@pythonicAI
by Geoffrey Hinton, Boyang Deng, Kyle Genova, Soroosh Yazdani, Sofien Bouaziz, Andrea Tagliasacchi
https://arxiv.org/abs/1909.05736
#paper #artificialintelligence
@pythonicAI
arXiv.org
CvxNet: Learnable Convex Decomposition
Any solid object can be decomposed into a collection of convex polytopes (in short, convexes). When a small number of convexes are used, such a decomposition can be thought of as a piece-wise...
Deep Reinforcement Learning Algorithm for Dynamic Pricing of Express Lanes with Multiple Access Locations
http://arxiv.org/abs/1909.04760
#paper #deeplearning #artificialintelligence
@pythonicAI
http://arxiv.org/abs/1909.04760
#paper #deeplearning #artificialintelligence
@pythonicAI
Useful paper about calibration of NN to reduce overfit
https://arxiv.org/pdf/1706.04599.pdf
#paper #neuralnetwork #artificialintelligence
@pythonicAI
https://arxiv.org/pdf/1706.04599.pdf
#paper #neuralnetwork #artificialintelligence
@pythonicAI
Understanding Transfer Learning for Medical Imaging
ArXiV: https://arxiv.org/abs/1902.07208
#paper #machinelearning #artificialintelligence
@pythonicAI
ArXiV: https://arxiv.org/abs/1902.07208
#paper #machinelearning #artificialintelligence
@pythonicAI
arXiv.org
Transfusion: Understanding Transfer Learning for Medical Imaging
Transfer learning from natural image datasets, particularly ImageNet, using standard large models and corresponding pretrained weights has become a de-facto method for deep learning applications...
Can you classify two class circle data using neural network with only two neurons?
https://arxiv.org/abs/1901.00109
#paper #neuralnetwork #artificialintelligence
@pythonicAI
https://arxiv.org/abs/1901.00109
#paper #neuralnetwork #artificialintelligence
@pythonicAI
YOLO Nano: a Highly Compact You Only Look Once Convolutional Neural Network for Object Detection
YOLO Nano possesses a model size of ~4.0MB (>15.1x and >8.3x smaller than Tiny YOLOv2 and Tiny YOLOv3, respectively) and requires 4.57B operations for inference (>34% and ~17% lower than Tiny YOLOv2 and Tiny YOLOv3, respectively) while still achieving an mAP of ~69.1% on the VOC 2007 dataset (~12% and ~10.7% higher than Tiny YOLOv2 and Tiny YOLOv3, respectively).
https://arxiv.org/pdf/1910.01271
#paper #deeplearning #artificialintelligence
@pythonicAI
YOLO Nano possesses a model size of ~4.0MB (>15.1x and >8.3x smaller than Tiny YOLOv2 and Tiny YOLOv3, respectively) and requires 4.57B operations for inference (>34% and ~17% lower than Tiny YOLOv2 and Tiny YOLOv3, respectively) while still achieving an mAP of ~69.1% on the VOC 2007 dataset (~12% and ~10.7% higher than Tiny YOLOv2 and Tiny YOLOv3, respectively).
https://arxiv.org/pdf/1910.01271
#paper #deeplearning #artificialintelligence
@pythonicAI
A Novel AI-enabled Framework to Diagnose Coronavirus COVID 19 using Smartphone Embedded Sensors: Design Study
https://arxiv.org/abs/2003.07434
#paper #deeplearning #artificialintelligence #corona
@pythonicAi
https://arxiv.org/abs/2003.07434
#paper #deeplearning #artificialintelligence #corona
@pythonicAi