Just presented our work on Recurrent Computations for Pattern Completion at the NIPS 2016 Brains & Bits Workshop!
Here’s the poster that I presented.
It was an awesome conference, lots of new work and amazing individuals.
Here’s a really short summary, but I highly recommend going through the papers and talks:
- unsupervised learning and GANs are hot
- learning to learn is becoming hot
- new threshold for deep: 1202 layers
After some requests, I have uploaded my (really short) analysis of Google’s TensorFlow to arXiv: https://arxiv.org/abs/1611.08903.
It is really just a small seminar paper, the main finding is that while using any Machine Learning framework is generally a good idea, TensorFlow has a really good chance of sticking around due to its already widespread usage within Google and research coupled with a growing community.
My main interest is in bridging Machine Learning and Neuroscience. I am focusing on building deep neural network models of the brain’s ventral stream that are more human-like in their behavior as well as their internals.
Previous work includes research in computer vision at Harvard, and natural language processing and reinforcement learning at Salesforce. My educational background is largely in computer science. I am currently a PhD/Graduate student at MIT BCS (Brain and Cognitive Sciences) with Jim DiCarlo and collaborate with Josh Tenenbaum and Gabriel Kreiman at Harvard.
We just won the Integration Prize of the Government of Swabia with Integreat!
Although it’s been a while, I thought I’d upload my Bachelor’s Thesis for others to read: Scalable Database Concurrency Control using Transactional Memory.pdf.
The work consists of two parts:
Part 1 analyzes the constraints of Hardware Transactional Memory (HTM) and identifies data structures that profit most of this technique.
Part 2 attempts different implementations of HTM in MySQL’s InnoDB storage component and evaluates the results.