Model optimization with new Tensorflow tool
https://medium.com/tensorflow/tensorflow-model-optimization-toolkit-pruning-api-42cac9157a6a
https://medium.com/tensorflow/tensorflow-model-optimization-toolkit-pruning-api-42cac9157a6a
Medium
TensorFlow Model Optimization Toolkit — Pruning API
Since we introduced the Model Optimization Toolkit — a suite of techniques that developers, both novice and advanced, can use to optimize…
Brain Network Mechanisms of General Intelligence
https://www.biorxiv.org/content/biorxiv/early/2019/06/03/657205.full.pdf
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
https://towardsdatascience.com/randomly-wired-neural-networks-and-state-of-the-art-accuracy-yes-it-works-9fb3cedc8059
Medium
Randomly wired neural networks and state-of-the-art accuracy? Yes it works.
How do you design the best Convolutional Neural Network (CNN)?
Reprojection Losses: Deep Learning Surpassing Classical Geometry in Computer Vision?
https://alexgkendall.com/computer_vision/Reprojection_losses_geometry_computer_vision/
https://alexgkendall.com/computer_vision/Reprojection_losses_geometry_computer_vision/
Home
Reprojection Losses: Deep Learning Surpassing Classical Geometry in Computer Vision?
Research Website and Blog.
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
https://github.com/videoflow/videoflow
GitHub
GitHub - videoflow/videoflow: Python framework that facilitates the quick development of complex video analysis applications and…
Python framework that facilitates the quick development of complex video analysis applications and other series-processing based applications in a multiprocessing environment. - 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
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
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
Shen et al.: https://arxiv.org/abs/1906.00604
#ArtificialIntelligence #DeepLearning #MachineLearning
arXiv.org
RF-Net: An End-to-End Image Matching Network based on Receptive Field
This paper proposes a new end-to-end trainable matching network based on
receptive field, RF-Net, to compute sparse correspondence between images.
Building end-to-end trainable matching framework...
receptive field, RF-Net, to compute sparse correspondence between images.
Building end-to-end trainable matching framework...
Generating Diverse High-Fidelity Images with VQ-VAE-2
Razavi et al.: https://arxiv.org/abs/1906.00446
#ArtificialIntelligence #DeepLearning #MachineLearning
Razavi et al.: https://arxiv.org/abs/1906.00446
#ArtificialIntelligence #DeepLearning #MachineLearning
arXiv.org
Generating Diverse High-Fidelity Images with VQ-VAE-2
We explore the use of Vector Quantized Variational AutoEncoder (VQ-VAE) models for large scale image generation. To this end, we scale and enhance the autoregressive priors used in VQ-VAE to...
A parallel implementation of "graph2vec: Learning Distributed Representations of Graphs" (MLGWorkshop 2017).
https://github.com/benedekrozemberczki/graph2vec
https://github.com/benedekrozemberczki/graph2vec
GitHub
GitHub - benedekrozemberczki/graph2vec: A parallel implementation of "graph2vec: Learning Distributed Representations of Graphs"…
A parallel implementation of "graph2vec: Learning Distributed Representations of Graphs" (MLGWorkshop 2017). - benedekrozemberczki/graph2vec
A Survival Guide to a PhD
https://karpathy.github.io/2016/09/07/phd/ https://t.iss.one/ArtificialIntelligenceArticles
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
This paper answers this: https://www.profillic.com/paper/arxiv:1905.12688
Profillic
Profillic: AI research & source code to supercharge your projects
Explore state-of-the-art in machine learning, AI, and robotics research. Browse papers, source code, models, and more by topics and authors. Connect with researchers and engineers working on related problems in machine learning, deep learning, natural language…
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
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
[https://towardsdatascience.com/the-quiet-semi-supervised-revolution-edec1e9ad8c]
(https://towardsdatascience.com/the-quiet-semi-supervised-revolution-edec1e9ad8c)
#machinelearning #artificialintelligence
Medium
The Quiet Semi-Supervised Revolution
Time to dust off that unlabeled data?
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
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
https://www.youtube.com/watch?v=w2_N_p_Y-W4&feature=youtu.be
YouTube
Revolutionizing Medical Diagnosis with Deep Learning | Ankit Gupta | TEDxYouth@Ballston
17 million people worldwide died due to cardiovascular disease in 2015 alone. Drawing from his experiences as a machine learning researcher and software engi...