Brain Network Mechanisms of General Intelligence
https://www.biorxiv.org/content/biorxiv/early/2019/06/03/657205.full.pdf

Randomly wired neural networks and state-of-the-art accuracy? Yes it works.
https://towardsdatascience.com/randomly-wired-neural-networks-and-state-of-the-art-accuracy-yes-it-works-9fb3cedc8059

Robustness beyond Security: Representation Learning
https://gradientscience.org/robust_reps/

Reprojection Losses: Deep Learning Surpassing Classical Geometry in Computer Vision?
https://alexgkendall.com/computer_vision/Reprojection_losses_geometry_computer_vision/

Python framework that facilitates the quick development of complex video analysis applications and other series-processing based applications in a multiprocessing environment.
https://github.com/videoflow/videoflow

Natural Language Inference with Deep Learning (NAACL 2019 Tutorial)

the slides for the 2019 NAACL tutorial on Natural Language Inference with Deep Learning
by Sam Bowman and Xiaodan Zhu.

https://nlitutorial.github.io/nli_tutorial.pdf

Robotic Psychology What Do We Know about Human-Robot Interaction and What Do We Still Need to Learn?
https://scholarspace.manoa.hawaii.edu/bitstream/10125/59633/0193.pdf

RF-Net: An End-to-End Image Matching Network based on Receptive Field
Shen et al.: https://arxiv.org/abs/1906.00604
#ArtificialIntelligence #DeepLearning #MachineLearning

Generating Diverse High-Fidelity Images with VQ-VAE-2
Razavi et al.: https://arxiv.org/abs/1906.00446
#ArtificialIntelligence #DeepLearning #MachineLearning

A parallel implementation of "graph2vec: Learning Distributed Representations of Graphs" (MLGWorkshop 2017).

https://github.com/benedekrozemberczki/graph2vec

A Survival Guide to a PhD
https://karpathy.github.io/2016/09/07/phd/ https://t.iss.one/ArtificialIntelligenceArticles

Cross-lingual transfer is a powerful tool for low-resource NLP. But when you build a system for a new language (say Bengali, German or French), what language do you transfer from?

This paper answers this: https://www.profillic.com/paper/arxiv:1905.12688

PyFastBertTorch

https://medium.com/huggingface/introducing-fastbert-a-simple-deep-learning-library-for-bert-models-89ff763ad384

COBRA: Data-Efficient Model-Based RL through Unsupervised Object Discovery and Curiosity-Driven Exploration
Watters et al.: https://arxiv.org/abs/1905.09275
#MachineLearning #UnsupervisedLearning #ArtificialIntelligence

A very nice article on practical limitations of semi-supervised learning and the recent advances that seems to overcome them
[https://towardsdatascience.com/the-quiet-semi-supervised-revolution-edec1e9ad8c]


(https://towardsdatascience.com/the-quiet-semi-supervised-revolution-edec1e9ad8c)


#machinelearning #artificialintelligence

Table2Vec: Neural Word and Entity Embeddings for Table Population and Retrieval. arxiv.org/abs/1906.00041

Independent Component Analysis based on multiple data-weighting. arxiv.org/abs/1906.00028

Machine Learning Methods for Shark Detection. arxiv.org/abs/1905.13309

Brain Network Mechanisms of General Intelligence

https://www.biorxiv.org/content/biorxiv/early/2019/06/03/657205.full.pdf

Revolutionizing Medical Diagnosis with Deep Learning: TED Talk

https://www.youtube.com/watch?v=w2_N_p_Y-W4&feature=youtu.be

Understanding and Controlling Memory in Recurrent Neural Networks (ICML'19 oral)

This paper shows that RNNs are able to form long-term memories despite being trained only for short-term with a limited amount of timesteps, but that not all memories are created equal. The authors find that each memory is correlated with a dynamical object in the hidden-state phase space and that the objects properties can quantitatively predict long term effectiveness. By regularizing the dynamical object, the long-term functionality of the RNN is significantly improved, while not adding to the computational complexity of training.

Link to PDF: https://proceedings.mlr.press/v97/haviv19a/haviv19a.pdf