Machine learning and learning theory research
2012 was a tumultuous year for me, but it was undeniably a great year for deep learning efforts. Signs of this include: Winning a Kaggle competition. Wide adoption of deep learning for speech recognition. Significant industry support. Gains in image recognition. This is a rare event in research: a significant capability breakout. Congratulations are definitely …
About 4 years ago, I speculated that decision trees qualify as a deep learning algorithm because they can make decisions which are substantially nonlinear in the input representation. Ping Li has proved this correct, empirically at UAI by showing that boosted decision trees can beat deep belief networks on versions of Mnist which are artificially …
“Deep learning” is used to describe learning architectures which have significant depth (as a circuit). One claim is that shallow architectures (one or two layers) can not concisely represent some functions while a circuit with more depth can concisely represent these same functions. Proving lower bounds on the size of a circuit is substantially harder …
Many learning algorithms used in practice are fairly simple. Viewed representationally, many prediction algorithms either compute a linear separator of basic features (perceptron, winnow, weighted majority, SVM) or perhaps a linear separator of slightly more complex features (2-layer neural networks or kernelized SVMs). Should we go beyond this, and start using “deep” representations? What is …
The stark success of OpenAI’s GPT4 model surprised me shifting my view from “really good autocomplete” (roughly inline with intuitions here) to a dialog agent exhibiting a significant scope of reasoning and intelligence. Some of the MSR folks did a fairly thorough study of capabilities which seems like a good reference. I think of GPT4 …