Hey, we're Terry & Salma 馃憢

The technical tagteam behind this blog. We aim to showcase the latest research, tools, and hardware for developing AI applications.

Machine Learning on Video

Many groups have found recent success in productizing computer vision applications with some formulation of transfer learning pre-trained convolutional neural networks. Factors such as cheaper bandwidth and storage, expanded remote work, streaming entertainment, social media, robotics and autonomous vehicles, all contribute to the rapidly increasing volume of video data. Despite the increased data and research attention, benchmark ML video tasks in perception, activity recognition, and video understanding have thus far eluded simple recipes or the broad adoption enjoyed by image applications....

 路 5 min 路 Terry Rodriguez & Salma Mayorquin

Jacked About Jax

As others have discussed, we鈥檝e noticed a recent uptick in research implemented using Jax. You could chalk it up as yet another ML platform, but Jax is emerging as the tool of choice for faster research iteration at DeepMind. After exploring for ourselves, we鈥檙e excited to find Jax is principally designed for fast differentiation. Excited because differentiation is foundational to gradient-based learning strategies supported in many ML algorithms. Moreover, the derivative is also ubiquitous in scientific computing, making Jax one powerful hammer!...

 路 4 min 路 Terry Rodriguez

NLS on the Lumpy Torus

Recently, I鈥檝e found a fascinating line of work directed at advancing computational fluid dynamics using machine-learned preconditioners to speed up convergence in linear iterative solvers. In fact, the number of steps until convergence influences the performance bound of many classical optimization algorithms. Machine learning helps us to trade a cheap, data-driven approximation for fewer, costly optimization steps in the endgame of convergence. Given this context, I鈥檝e been revisiting my studies on numerical PDE like the Nonlinear Schrodinger Equation (NLS) and here I鈥檒l share some of the background work I took part in during the Summer of 2012....

 路 5 min 路 Terry Rodriguez

Bitrate Optimization using Spark and FFmpeg

Check out this part 1 notebook and this part 2 notebook that accompany this post! Streaming video is a major part of how users consume information across a variety of applications. As more users turn to mobile devices, the screen sizes are also increasing. At the same time, consumers expect high quality video without lag or distortion. This frames an engineering challenge to optimize the way video is streamed for consumers using a wide variety of hardware....

 路 3 min 路 Terry Rodriguez & Salma Mayorquin

Scalable Image Deduplication With Spark

Make sure to check out the databricks notebook for this post! Modern internet companies maintain many image/video assets rendered at various resolutions to optimize content delivery. This demand gives rise to very interesting optimization problems. Groups like Netflix have even taken steps to personalize the images presented to each user, but as they describe, this involves subproblems in organizing the collection of images. In particular, Netflix researchers described extracting image metadata to help cluster near duplicate images so they could more efficiently apply techniques like contextual bandits for image personalization....

 路 2 min 路 Terry Rodriguez & Salma Mayorquin