Showing papers by "Sebastian Thrun published in 2020"

Artificial intelligence and cancer

Abstract: Filtered through the analytical power of artificial intelligence, the wealth of available biomedical data promises to revolutionize cancer research, diagnosis and care. In this Viewpoint, six experts discuss some of the challenges, exciting developments and future questions arising at the interface of machine learning and oncology.

BanditPAM: Almost Linear Time k-Medoids Clustering via Multi-Armed Bandits

Abstract: Clustering is a ubiquitous task in data science. Compared to the commonly used $k$-means clustering, $k$-medoids clustering requires the cluster centers to be actual data points and support arbitrary distance metrics, which permits greater interpretability and the clustering of structured objects. Current state-of-the-art $k$-medoids clustering algorithms, such as Partitioning Around Medoids (PAM), are iterative and are quadratic in the dataset size $n$ for each iteration, being prohibitively expensive for large datasets. We propose BanditPAM, a randomized algorithm inspired by techniques from multi-armed bandits, that reduces the complexity of each PAM iteration from $O(n^2)$ to $O(n \log n)$ and returns the same results with high probability, under assumptions on the data that often hold in practice. As such, BanditPAM matches state-of-the-art clustering loss while reaching solutions much faster. We empirically validate our results on several large real-world datasets, including a coding exercise submissions dataset, the 10x Genomics 68k PBMC single-cell RNA sequencing dataset, and the MNIST handwritten digits dataset. In these experiments, we observe that BanditPAM returns the same results as state-of-the-art PAM-like algorithms up to 4x faster while performing up to 200x fewer distance computations. The improvements demonstrated by BanditPAM enable $k$-medoids clustering on a wide range of applications, including identifying cell types in large-scale single-cell data and providing scalable feedback for students learning computer science online. We also release highly optimized Python and C++ implementations of our algorithm.

BanditPAM: Almost Linear Time $k$-Medoids Clustering via Multi-Armed Bandits

Abstract: Clustering is a ubiquitous task in data science. Compared to the commonly used $k$-means clustering, $k$-medoids clustering requires the cluster centers to be actual data points and support arbitrary distance metrics, which permits greater interpretability and the clustering of structured objects. Current state-of-the-art $k$-medoids clustering algorithms, such as Partitioning Around Medoids (PAM), are iterative and are quadratic in the dataset size $n$ for each iteration, being prohibitively expensive for large datasets. We propose BanditPAM, a randomized algorithm inspired by techniques from multi-armed bandits, that reduces the complexity of each PAM iteration from $O(n^2)$ to $O(n \log n)$ and returns the same results with high probability, under assumptions on the data that often hold in practice. As such, BanditPAM matches state-of-the-art clustering loss while reaching solutions much faster. We empirically validate our results on several large real-world datasets, including a coding exercise submissions dataset, the 10x Genomics 68k PBMC single-cell RNA sequencing dataset, and the MNIST handwritten digits dataset. In these experiments, we observe that BanditPAM returns the same results as state-of-the-art PAM-like algorithms up to 4x faster while performing up to 200x fewer distance computations. The improvements demonstrated by BanditPAM enable $k$-medoids clustering on a wide range of applications, including identifying cell types in large-scale single-cell data and providing scalable feedback for students learning computer science online. We also release highly optimized Python and C++ implementations of our algorithm.

Electric vertical take-off and landing vehicle with wind turbine

Abstract: An electric vertical take-off and landing (eVTOL) vehicle is positioned to be in a charging position on the ground, wherein the eVTOL vehicle is capable of performing vertical take-offs and landings. The battery is charged while in the charging position on the ground using a wind turbine that includes the rotor.

Charging station for self-balancing multicopter

Abstract: A vertical landing is performed by an electric vertical take-off and landing (eVTOL) vehicle above a charger where the eVTOL vehicle includes a rotor that is configured to rotate during an occupant change state to keep the eVTOL vehicle stationary during the occupant change state. A vertically-oriented male charging port that is part of the eVTOL vehicle and a female charging port that is part of the charger are detachably coupled and a battery in the eVTOL vehicle is charged using the charger while the vertically-oriented male charging port and the female charging port are detachably coupled.