Turbo-SMT: Parallel Coupled Sparse Matrix-Tensor Factorizations and Applications.

How can we correlate the neural activity in the human brain as it responds to typed words, with properties of these terms (like 'edible', 'fits in hand')? In short, we want to find latent variables, that jointly explain both the brain activity, as well as the behavioral responses. This is one of many settings of the (CMTF) problem. Can we enhance CMTF solver, so that it can operate on potentially very large datasets that may not fit in main memory? We introduce Turbo-SMT, a meta-method capable of doing exactly that: it boosts the performance of CMTF algorithm, produces sparse and interpretable solutions, and parallelizes CMTF algorithm, producing sparse and interpretable solutions (up to ).

Good-Enough Brain Model: Challenges, Algorithms, and Discoveries in Multisubject Experiments.

Given a simple noun such as apple, and a question such as "Is it edible?," what processes take place in the human brain? More specifically, given the stimulus, what are the interactions between (groups of) neurons (also known as functional connectivity) and how can we automatically infer those interactions, given measurements of the brain activity? Furthermore, how does this connectivity differ across different human subjects? In this work, we show that this problem, even though originating from the field of neuroscience, can benefit from big data techniques; we present a simple, novel good-enough brain model, or GeBM in short, and a novel algorithm Sparse-SysId, which are able to effectively model the dynamics of the neuron interactions and infer the functional connectivity. Moreover, GeBM is able to simulate basic psychological phenomena such as habituation and priming (whose definition we provide in the main text). We evaluate GeBM by using real brain data.

Turbo-SMT: Accelerating Coupled Sparse Matrix-Tensor Factorizations by 200×.

How can we correlate the neural activity in the human brain as it responds to typed words, with properties of these terms (like 'edible', 'fits in hand')? In short, we want to find latent variables, that jointly explain both the brain activity, as well as the behavioral responses. This is one of many settings of the (CMTF) problem. Can we accelerate CMTF solver, so that it runs within a few minutes instead of tens of hours to a day, while maintaining good accuracy? We introduce TURBO-SMT, a meta-method capable of doing exactly that: it boosts the performance of CMTF algorithm, by up to ×, along with an up to increase in sparsity, with comparable accuracy to the baseline.