Generative Adversarial Networks continues to be one of the main deep learning techniques in current computer vision with machine learning developments. But they have been shown to have some issues regarding the quality of images it outputs from a generator's map input space. This may have some causal explanation with the way GANs process of … Continue reading Paper with Code Series: Adversarial Latent Autoencoders
Lex Fridman's MIT list of videos about self-driving cars was published since the beginning of this year. I was trying to get my schedule right in this blog to start posting on this list, and now the time has come. The videos follow and complement the page on the course from MIT MIT 6.S094: Deep Learning … Continue reading State-of-the-art in self-driving cars and autonomous vehicles: a list of videos from MIT
This is a re-sharing from the excellent weekly newsletter I receive from the Import AI blog written by Jack Clark. There are several other re-posts such as this one in this blog, and I usually decide for it when I see and feel it that the choices of papers or relevant articles, resources and … Continue reading From the Import AI blog: a Vision-Based High Speed Driving with a Deep Dynamic Observer or how Self-driving cars will drive off-roads
The Massachusetts Institute of Technology (MIT) has been given a series of lectures titled MIT 6.S099: Artificial General Intelligence. It is part of the syllabus of a course on Artificial General Intelligence and Deep Learning delivered by Lex Fridman. It featured a series of conversations with some prominent researchers in the fields of machine learning, … Continue reading A conversation on AI from MIT Artificial General Intelligence Lectures
Hello. I am starting today a new series of posts here in The Intelligence of Information. I know there is this hiatus of several months without posting here in this blog. I may have said the reasons for this, so I will skip ahead. Just to remind: this still is a work in progress blog, … Continue reading Papers with Code Series: Self-Attention Generative Adversarial Networks
Snorkel: rapid training data creation with weak supervision Ratner et al., VLDB’18 Earlier this week we looked at Sparser, which comes from the Stanford Dawn project, “a five-year research project to democratize AI by making it dramatically easier to build AI-powered applications.” Today’s paper choice, Snorkel, is from the same stable. It tackles one of […] … Continue reading Required share fom The Morning Paper: Snorkel: rapid training data creation with weak supervision — the morning paper
DeepTest: automated testing of deep-neural-network-driven autonomous cars Tian et al., ICSE’18 How do you test a DNN? We’ve seen plenty of examples of adversarial attacks in previous editions of The Morning Paper, but you couldn’t really say that generating adversarial images is enough to give you confidence in the overall behaviour of a model under […] … Continue reading ReBlog from The Morning Paper: DeepTest: automated testing of deep-neural-network-driven autonomous cars — the morning paper
A team of researchers from Facebook AI research released an interesting paper about sequence to sequence learning with convolutional neural networks (CNNs). CNNs has been mainly used in computer vision implementations, being a state-of-the-art stack for the the researche and development in object recognition or image recognition. Less often have CNNs been implemented for machine … Continue reading Sequence to sequence learning with Convolutional Neural networks
Today I would like to post a more technical and pure engineering topic. The heart of the matter in Artificial Intelligence(AI) is more practical/empirical based than theoretical. Even though the conceptual framework is undoubtedly important. But to get a good grasp of the real work involved in setting up all the apparatus for a … Continue reading How to train, deploy and develop TensorFlow AI Models, SparkML from Jupyter Notebook to production
From now and then I just wonder how good my social media connections really are. Often crossed my mind that social media is more noise than signal, real good signals of the best we can be and do as human beings. There is a lot of not that good about what human beings should be … Continue reading Success with deep learning architectures, surrogate random matrices and spectral ergodicity
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