Mission Moon 3-D: A New Perspective on the Space Race https://www.aitribune.com/book/2018111096
Aitribune
Mission Moon 3-D: A New Perspective on the Space Race | AI Tribune
By: David J. Eicher, Brian May
The story of the lunar landing and the events that led up to it, told in text and visually stunning 3-D images.
The story of the lunar landing and the events that led up to it, told in text and visually stunning 3-D images.
This paper evaluates some of the methods in the context of computer vision, specifically when identifying different types of objects and predicting how far away an object is in images. The new method is called 3D- BoNet.
paper: [https://www.profillic.com/paper/arxiv:1906.01140]
(https://www.profillic.com/paper/arxiv:1906.01140)
paper: [https://www.profillic.com/paper/arxiv:1906.01140]
(https://www.profillic.com/paper/arxiv:1906.01140)
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…
Make music with GANs
GANSynth is a new method for fast generation of high-fidelity audio.
🎵 Examples: https://goo.gl/magenta/gansynth-examples
⏯ Colab: https://goo.gl/magenta/gansynth-demo
📝 Paper: https://goo.gl/magenta/gansynth-paper
💻 Code: https://goo.gl/magenta/gansynth-code
⌨️ Blog: https://magenta.tensorflow.org/gansynth
#artificialintelligence #deeplearning #generativeadversarialnetworks
GANSynth is a new method for fast generation of high-fidelity audio.
🎵 Examples: https://goo.gl/magenta/gansynth-examples
⏯ Colab: https://goo.gl/magenta/gansynth-demo
📝 Paper: https://goo.gl/magenta/gansynth-paper
💻 Code: https://goo.gl/magenta/gansynth-code
⌨️ Blog: https://magenta.tensorflow.org/gansynth
#artificialintelligence #deeplearning #generativeadversarialnetworks
Google
Google Colaboratory
SAVOIAS: A Diverse, Multi-Category Visual Complexity Dataset
By Elham Saraee, Mona Jalal, Margrit Betke : https://arxiv.org/abs/1810.01771v1
GitHub : https://github.com/esaraee/Savoias-Dataset
By Elham Saraee, Mona Jalal, Margrit Betke : https://arxiv.org/abs/1810.01771v1
GitHub : https://github.com/esaraee/Savoias-Dataset
Geometric deep learning is a very exciting new field, but its mathematics is slowly drifting into the territory of algebraic topology and theoretical physics.
Background story: https://towardsdatascience.com/an-easy-guide-to-gauge-equivariant-convolutional-networks-9366fb600b70
Paper: https://arxiv.org/abs/1902.04615
Background story: https://towardsdatascience.com/an-easy-guide-to-gauge-equivariant-convolutional-networks-9366fb600b70
Paper: https://arxiv.org/abs/1902.04615
Machine learning programs created by the Robotics Systems Lab at ETH Zurich teaches the dog-like robot "ANYmal" to run fast and "roll over" when it falls. As seen in Science Robotics:
https://robotics.sciencemag.org/content/4/26/eaau5872
https://robotics.sciencemag.org/content/4/26/eaau5872
Science Robotics
Learning agile and dynamic motor skills for legged robots
Legged robots pose one of the greatest challenges in robotics. Dynamic and agile maneuvers of animals cannot be imitated by existing methods that are crafted by humans. A compelling alternative is reinforcement learning, which requires minimal craftsmanship…
Using machine-learning and sensory hardware, Assistant Professor Alberto Rodriguez and members of MIT's MCube lab have developed a robot that is learning how to play the game Jenga®
Learn more about how this robot combines vision and touch to learn the game of Jenga: https://mitsha.re/fQP630nwST1
Learn more about how this robot combines vision and touch to learn the game of Jenga: https://mitsha.re/fQP630nwST1
MIT News
MIT robot combines vision and touch to learn the game of Jenga
Machine-learning approach could help robots assemble cellphones and other small parts in a manufacturing line.
Wonder how the most powerful #opensource #DeepLearning #AI frameworks are tied together by #PowerAI?
Watch video to find out:
https://mediacenter.ibm.com/media/Tanmay+BakshiA+Power+AI+-+Why+I'm+fascinated+by+the+combination+of+AI+and+deep+learning+frameworks/0_347tcpe2
#PowerAI is a great technology with huge potential in challenges like Call for Code!
Watch video to find out:
https://mediacenter.ibm.com/media/Tanmay+BakshiA+Power+AI+-+Why+I'm+fascinated+by+the+combination+of+AI+and+deep+learning+frameworks/0_347tcpe2
#PowerAI is a great technology with huge potential in challenges like Call for Code!
IBM MediaCenter
Tanmay Bakshi: Power AI - Why I'm fascinated by the combination of AI and deep learning frameworks
Tanmay Bakshi talks about how IBM Power AI can help solve some key challenges with deep learning and allows domain or subject matter experts (SMEs) to create their own deep learning models – without needing coding or deep learning expertise. "With machine…
Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution
arxiv.org/abs/1904.05049
arxiv.org/abs/1904.05049
"How I made top 0.3% on a Kaggle competition"
By Lavanya Shukla: https://www.kaggle.com/lavanyashukla01/how-i-made-top-0-3-on-a-kaggle-competition
#ArtificialIntelligence #MachineLearning #Kaggle
By Lavanya Shukla: https://www.kaggle.com/lavanyashukla01/how-i-made-top-0-3-on-a-kaggle-competition
#ArtificialIntelligence #MachineLearning #Kaggle
Visualizing and Measuring the Geometry of BERT
Coenen et al.: https://arxiv.org/abs/1906.02715
#ArtificialIntelligence #BERT #NaturalLanguageProcessing
Coenen et al.: https://arxiv.org/abs/1906.02715
#ArtificialIntelligence #BERT #NaturalLanguageProcessing
arXiv.org
Visualizing and Measuring the Geometry of BERT
Transformer architectures show significant promise for natural language processing. Given that a single pretrained model can be fine-tuned to perform well on many different tasks, these networks...
Selfie: Self-supervised Pretraining for Image Embedding
Trinh et al.: https://arxiv.org/abs/1906.02940
#ArtificialIntelligence #MachineLearning #SelfSupervised
Trinh et al.: https://arxiv.org/abs/1906.02940
#ArtificialIntelligence #MachineLearning #SelfSupervised
arXiv.org
Selfie: Self-supervised Pretraining for Image Embedding
We introduce a pretraining technique called Selfie, which stands for SELFie supervised Image Embedding. Selfie generalizes the concept of masked language modeling of BERT (Devlin et al., 2019) to...
Summaries of Top AI Research Papers of 2018
https://www.topbots.com/most-important-ai-research-papers-2018/
#NeurIPS #NeurIPS2018 #NIPS #NIPS2018
https://www.topbots.com/most-important-ai-research-papers-2018/
#NeurIPS #NeurIPS2018 #NIPS #NIPS2018
TOPBOTS
Easy-To-Read Summary of Important AI Research Papers of 2018
UPDATE: We’ve also summarized the top 2019 and top 2020 AI & machine learning research papers. Trying to keep up with AI research papers can feel like an exercise in futility given how quickly the industry moves. If you’re buried in papers to read that you…
Google Dataset Search : You can search for datasets using Google now https://toolbox.google.com/datasetsearch
Omnidirectional Scene Text Detection with Sequential-free Box Discretization. arxiv.org/abs/1906.02371
Brain-Network Clustering via Kernel-ARMA Modeling and the Grassmannian. arxiv.org/abs/1906.02292
Automated Machine Learning: State-of-The-Art and Open Challenges. arxiv.org/abs/1906.02287
How Bayesian inference works
https://brohrer.github.io/how_bayesian_inference_works.html
https://brohrer.github.io/how_bayesian_inference_works.html