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Found 4 papers, 2 papers with code
TimeInf: Time Series Data Contribution via Influence Functions
Evaluating the contribution of individual data points to a model's prediction is critical for interpreting model predictions and improving model performance.
Towards a "universal translator" for neural dynamics at single-cell, single-spike resolution
Neuroscience research has made immense progress over the last decade, but our understanding of the brain remains fragmented and piecemeal: the dream of probing an arbitrary brain region and automatically reading out the information encoded in its neural activity remains out of reach.
Motion-Invariant Variational Auto-Encoding of Brain Structural Connectomes
We develop a generative model to learn low-dimensional representations of structural connectomes invariant to motion-induced artifacts, so that we can link brain networks and human traits more accurately, and generate motion-adjusted connectomes.
Exploration-Exploitation Motivated Variational Auto-Encoder for Recommender Systems
Recent years have witnessed rapid developments on collaborative filtering techniques for improving the performance of recommender systems due to the growing need of companies to help users discover new and relevant items.
