Inferring the quantum density matrix with machine learning
Cranmer et al.: https://arxiv.org/abs/1904.05903
#QuantumPhysics #Physics #ArtificialIntelligence #MachineLearning

Linguistic Knowledge and Transferability of Contextual Representations
Liu et al.: https://arxiv.org/abs/1903.08855
#ArtificialIntelligence #DeepLearning #MachineLearning

CS294-158 Deep Unsupervised Learning
Ilya Sutskever @ilyasut guest lecture on GPT-2: https://youtu.be/X-B3nAN7YRM

#DeepLearning #MachineLearning #UnsupervisedLearning

Artificial Intelligence, deep learning, machine learning — whatever you’re doing if you don’t understand it — learn it. Because otherwise you’re going to be a dinosaur within 3 years . Mark Cuban

There's a new convolution operation in town!

For CNNs there's a proposed "Octave Convolution" (OctConv) which can be used as a direct replacement of plain vanilla convolutions without any adjustments in the network architecture.
The idea of OctConv is that, in images, information is conveyed at different frequencies i.e. high frequencies show fine details whereas low-frequencies show global structures.

The idea then is to factorize the feature maps into a high-frequency/low-frequency feature maps and then reduce the spatial resolutions of the low-frequency maps by an octave. This not only leads to lower memory/computation cost but also to better evaluation results such as accuracy in an image classification task.

Paper: https://export.arxiv.org/pdf/1904.05049

Evolved Art with Transparent, Overlapping, and Geometric Shapes
Berg et al.: https://arxiv.org/abs/1904.06110
#NeuralComputing #EvolutionaryComputing #ArtificialIntelligence

Full Stack Deep Learning Bootcamp
(Most of) Lectures of Day 1: https://fullstackdeeplearning.com/march2019
Happy learning!
#ArtificialIntelligence #DeepLearning #MachineLearning

L2 Regularization and Batch Norm

Blog by David Wu: https://blog.janestreet.com/l2-regularization-and-batch-norm/

#artificialintelligence #datascience #machinelearning

A Repository of Conversational Datasets

https://arxiv.org/abs/1904.06472

Mini Course in Deep Learning with PyTorch for AIMS
https://github.com/Atcold/pytorch-Deep-Learning-Minicourse

Google releases massive visual databases for machine learning

https://www.datasciencecentral.com/profiles/blogs/google-releases-massive-visual-databases-for-machine-learning

Visual-Inertial Mapping with Non-Linear Factor Recovery. https://arxiv.org/abs/1904.06504

Towards Self-similarity Consistency and Feature Discrimination for Unsupervised Domain https://arxiv.org/abs/1904.06490

YouTube UGC Dataset for Video Compression Research. https://arxiv.org/abs/1904.06457

Patch redundancy in images: a statistical testing framework and some applications. https://arxiv.org/abs/1904.06428

Towards Accurate One-Stage Object Detection with AP-Loss. https://arxiv.org/abs/1904.06373

The iWildCam 2018 Challenge Dataset. https://arxiv.org/abs/1904.05986

Best Paper Awards in Computer Science (since 1996)

A well maintained list: https://jeffhuang.com/best_paper_awards.html

#artificialintelligence #award #machinelearning #papers #research @ArtificialIntelligenceArticles

Disney trying to automate making an animation
https://arxiv.org/pdf/1904.05440.pdf

Visualizing Attention in Transformer-Based Language Representation Models
Jesse Vig: https://arxiv.org/abs/1904.02679
#ArtificialIntelligence #MachineLearning #NaturalLanguageProcessing

Natural Language Semantics With Pictures: Some Language & Vision Datasets and Potential U... https://arxiv.org/abs/1904.07318