Since 2014, the NIH funded iDASH (integrating Data for Analysis, Anonymization, SHaring) National Center for Biomedical Computing has hosted yearly competitions on the topic of private computing for genomic data.
While federated learning is a promising approach for training deep learning models over distributed sensitive datasets, it presents new challenges for machine learning, especially when applied in the medical domain where multi-centric data heterogeneity is common.
Federated Learning enables one to jointly train a machine learning model across distributed clients holding sensitive datasets.
One notable application comes from the field of differential privacy, where per-example gradients must be norm-bounded in order to limit the impact of each example on the aggregated batch gradient.
Timely assessment of compound toxicity is one of the biggest challenges facing the pharmaceutical industry today.
Ranked #2 on Drug Discovery on Tox21
Analysis of histopathology slides is a critical step for many diagnoses, and in particular in oncology where it defines the gold standard.
Detection of interactions between treatment effects and patient descriptors in clinical trials is critical for optimizing the drug development process.
In statistical learning for real-world large-scale data problems, one must often resort to "streaming" algorithms which operate sequentially on small batches of data.
Restricted Boltzmann machines (RBMs) are energy-based neural-networks which are commonly used as the building blocks for deep architectures neural architectures.
In this work, we consider compressed sensing reconstruction from $M$ measurements of $K$-sparse structured signals which do not possess a writable correlation model.
Restricted Boltzmann machines are undirected neural networks which have been shown tobe effective in many applications, including serving as initializations fortraining deep multi-layer neural networks.
In this paper, the problem of compressive imaging is addressed using natural randomization by means of a multiply scattering medium.
Restricted Boltzmann machines are undirected neural networks which have been shown to be effective in many applications, including serving as initializations for training deep multi-layer neural networks.
Approximate Message Passing (AMP) has been shown to be an excellent statistical approach to signal inference and compressed sensing problem.
These notes review six lectures given by Prof. Andrea Montanari on the topic of statistical estimation for linear models.
Approximate Message Passing (AMP) has been shown to be a superior method for inference problems, such as the recovery of signals from sets of noisy, lower-dimensionality measurements, both in terms of reconstruction accuracy and in computational efficiency.