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Found 8 papers, 0 papers with code
High-Order Associative Learning Based on Memristive Circuits for Efficient Learning
Memristive associative learning has gained significant attention for its ability to mimic fundamental biological learning mechanisms while maintaining system simplicity.
On the Pinsker bound of inner product kernel regression in large dimensions
Building on recent studies of large-dimensional kernel regression, particularly those involving inner product kernels on the sphere $\mathbb{S}^{d}$, we investigate the Pinsker bound for inner product kernel regression in such settings.
Cortex: Harnessing Correlations to Boost Query Performance
Unlike prior work, Cortex can adapt itself to any existing primary index, whether single or multi-dimensional, to harness a broad variety of correlations, such as those that exist between more than two attributes or have a large number of outliers.
The Case for Learned Spatial Indexes
This exponential growth in spatial data has led the research community to focus on building systems and applications that can process spatial data efficiently.
Tsunami: A Learned Multi-dimensional Index for Correlated Data and Skewed Workloads
Filtering data based on predicates is one of the most fundamental operations for any modern data warehouse.
Learning Multi-dimensional Indexes
Scanning and filtering over multi-dimensional tables are key operations in modern analytical database engines.
LISA: Towards Learned DNA Sequence Search
Next-generation sequencing (NGS) technologies have enabled affordable sequencing of billions of short DNA fragments at high throughput, paving the way for population-scale genomics.
ALEX: An Updatable Adaptive Learned Index
The original work by Kraska et al. shows that a learned index beats a B+Tree by a factor of up to three in search time and by an order of magnitude in memory footprint.
