Graph Neural Networks (GNNs)
(T) Graphs have recently been “the big thing” for deep learning. There are perfect examples of machine learning applications in the Non-Euclidian space. Graphs have been used to predict friends […]
Machine Learning in Non-Euclidian Spaces
(T) The fundamental representation of any feature for any machine learning model is the vector and its multidimensional generalization which is the tensor. This has led to developing machine learning […]
Distributed and Declarative Deep Learning Systems
(T) Developing deep learning models is hard. Debugging deep learning models is even harder. And, few companies have data pipelines that provide the training and inference of large scale deep […]
Tesla AI Day 2021
(T) Tesla had this week a recruiting event for data scientists and software engineers: “Tesla AI Day”. In my opinion, Andrej Karpathy and his team during that event have shown […]
Multi-Task Prediction of Organ Dysfunctions in ICUs
(T) An interesting research from Google Health on predicting organ dysfunctions in intensive care units (ICUs). The system uses multi-task models, which take into account a variety of competing risks along […]
Designing the Next Version of TPUs with Reinforcement Learning and Graph CNNs
(T) A team of Google researchers developed a reinforcement learning system to design the billions of transistors in the next version of Google’s Tensor Processing Unit (TPU) chips optimized for computing TensorFlow […]
Could Machine Learning Become a New Instrument for Musicians?
(T) When I am listening to Bach’s Brandenburg Concertos, I have always been fascinated by how much I have the wrong certitude of listening to a concerto for violins from […]
Generating Perfect Humanoids through Reinforcement Learning
(T) This is probably the state of the art (or close to it) of character physics-based animation for generating humanoids through reinforcement learning (RL) from a team of researchers at […]
Understanding Deep Learning in the Infinite Width Limit
(T) Deep learning systems have achieved impressive results from language understanding, to protein sequencing, and autonomous vehicles. While it has long been known that artificial neural networks can approximate any […]
Google Brain Research in 2020
(T) As he did since 2017, Jeff Dean, the tech lead of the Google Brain Team, and probably one of the most famous Silicon Valley engineers, shared some of the key […]
The Transformer Architecture Replacing CNNs?
(T) Most of the innovations in deep learning have come over the last few years from new natural language processing (NLP) techniques. Google’s Transformer with self-attention mechanisms has certainly changed […]
How to Learn Faster? Multi-Task and Meta-Learning to the Rescue
(T) One of the key goals of many data scientist teams is to develop models faster for new use cases and new applications. To that end, “transfer learning” which aims […]
Second Scaled Machine Learning Conference @ the Computer History Museum
(T) This week, I had the opportunity to attend for the fourth time, the fifth Scaled Machine Learning conference that was held for the second time at the Computer History Museum in […]
How Reinforcement Learning Enables a new Generation of Robots
(T) I had the pleasure to listen to several great talks about reinforcement learning from Professor Pieter Abbeel from UC Berkeley at the ODSC West Conference last Fall. I am […]
Google Brain Research in 2019
(T) As he did since 2017, Jeff Dean, the tech lead of the Google Brain Team and probably one of the most famous Silicon Valley engineers, shared some of the […]
The Power and Limits of Deep Learning
(T) I recently listened to a lecture from Yann LeCun on “The Power and Limits of Deep Learning” organized by ACM. Following are a summary in plain English based on […]
ACM-IMS Interdisciplinary Summit on the Foundations of Data Science
(T) I was kindly invited to attend the first “ACM-IMS Interdisciplinary Summit on the Foundations of Data Science” yesterday at the Palace Hotel in San Francisco. The summit organized by […]
Scaled Machine Learning Conference @ the Computer History Museum
(T) This week, I had the opportunity to attend for the third time, the fourth Scaled Machine Learning conference that was not held at Stanford University but the Computer History Museum […]
Todays’ Turing Awards to the Three Musketeers of Deep Learning
(T) Earlier this year, I tried to summarize in one slide, the top 20 scientists who contributed the most to the field of artificial intelligence over three generations. Obviously, this […]
OpenAI’s GPT-2 Language Model
(T) OpenAI released this week its second-generation language model GPT-2. That model provides reading comprehension, summarization, translation, and question answering. It is so good so OpenAI fears that it could […]
Google Brain Research in 2018
(T) As he did last year, Jeff Dean, the tech lead of the Google Brain Team and probably one of the most famous Silicon Valley engineers, shared some of the […]
The Beauty of Generative Models
(T) Google, Facebook, Apple, and Amazon have a key asset: a huge amount of data from their customers. And with data, you can do so much! But what if you […]
Prof. Daskalakis Winning the 2018 Nevanlinna Prize
(T) Every four years at the International Congress of Mathematicians (ICM), the Nevanlinna Prize laureate is announced. This week, at the 2018 ICM in Rio de Janeiro, it was announced […]
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