Deep Learning for Symbolic Mathematics

Authors show that ANN can be surprisingly good at more elaborated tasks in mathematics, such as symbolic integration and solving differential equations.

#neuralnetworks #deeplearning #mathematics #math #matlab

https://arxiv.org/abs/1912.01412

Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data
Such et al.: https://arxiv.org/abs/1912.07768
#ArtificialIntelligence #MachineLearning #NeuralNetworks

Neural Smoke Stylization with Color Transfer
Christen et al.: https://arxiv.org/abs/1912.08757
#ArtificialIntelligence #DeepLearning #NeuralNetworks

Hierarchical Graph Pooling with Structure Learning
Zhang et al.: https://arxiv.org/abs/1911.05954
#ArtificialIntelligence #Graph #NeuralNetworks

How to generate ideas in Machine Learning?
Asadulaev Arip : https://medium.com/datadriveninvestor/how-to-generate-ideas-in-machine-learning-bdb9a7267392
#ArtificialIntelligence #MachineLearning #NeuralNetworks

Restoring ancient text using deep learning: a case study on Greek epigraphy"
Assael et al.: https://arxiv.org/abs/1910.06262
Code: https://github.com/sommerschield/ancient-text-restoration
#ArtificialIntelligence #DeepLearning #NeuralNetworks

Machine Learning from scratch!
Implementation of some classic Machine Learning model from scratch and benchmarking against popular ML library, by Quan Tran: https://github.com/anhquan0412/basic_model_scratch
#ArtificialIntelligence #DeepLearning #NeuralNetworks #MachineLearning

Deep RL Bootcamp
By Pieter Abbeel, Rocky Duan, Peter Chen, Andrej Karpathy et al.: https://sites.google.com/view/deep-rl-bootcamp/lectures
#ArtificialIntelligence #DeepLearning #MachineLearning #NeuralNetworks #ReinforcementLearning

Selective Brain Damage: Measuring the Disparate Impact of Model Compression
Sara Hooker, Aaron Courville, Yann Dauphin, Andrea Frome: https://weightpruningdamage.github.io
#ArtificialIntelligence #DeepLearning #NeuralNetworks

How Much Position Information Do Convolutional Neural Networks Encode?
Islam et al.: https://arxiv.org/abs/2001.08248
#ArtificialIntelligence #DeepLearning #NeuralNetworks

DNNs as Layers of Cooperating Classifiers
Davel et al.: https://arxiv.org/abs/2001.06178
#DeepLearning #MachineLearning #NeuralNetworks

An interpretable neural network model through piecewise linear approximation
Guo et al.: https://arxiv.org/abs/2001.07119
#MachineLearning #ArtificialIntelligence #NeuralNetworks

Neural Module Networks for Reasoning over Text
Gupta et al.: https://arxiv.org/abs/1912.04971
Code: https://nitishgupta.github.io/nmn-drop
#NeuralNetworks #Reasoning #SymbolicAI

PyTorch Wrapper version 1.1 is out!

New Features:

- Samplers for smart batching based on text length for faster training.

- Loss and Evaluation wrappers for token prediction tasks.

- New nn.modules for attention based models.

- Support for multi GPU training / evaluation / prediction.

- Verbose argument in system's methods.

- Examples using Transformer based models like BERT for text classification.

Check it out in the following links:

install with: pip install pytorch-wrapper

GitHub: https://github.com/jkoutsikakis/pytorch-wrapper

docs: https://pytorch-wrapper.readthedocs.io/en/latest/

examples: https://github.com/jkouts…/pytorch-wrapper/…/master/examples

#DeepLearning #PyTorch #NeuralNetworks #MachineLearning #DataScience #python #TensorFlow

These Lyrics Do Not Exist
Lyrics generated using Artificial Intelligence
Peter Ranieri: https://theselyricsdonotexist.com
#ArtificialIntelligence #Music #NeuralNetworks

Efficient Graph Generation with Graph Recurrent Attention Networks
Liao et al.: https://arxiv.org/abs/1910.00760
Code: https://github.com/lrjconan/GRAN
#Graph #NeuralNetworks #NeurIPS #NeurIPS2019