LSH At Large - Distributed KNN Search in High Dimensions

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Decentralized storage networks offer services with intriguing possibilities to reduce inequalities in an extremely centralized market. Fair distribution of rewards, however, is still a persistent problem in the current generation of decentralized applicati ...

In the current era of big data, aggregation queries on high-dimensional datasets are frequently utilized to uncover hidden patterns, trends, and correlations critical for effective business decision-making. Data cubes facilitate such queries by employing p ...

We present Sudokube, a novel system that supports interactive speed querying on high-dimensional data using partially materialized data cubes. Given a storage budget, it judiciously chooses what projections to precompute and materialize during cube constru ...

Principal component analysis (PCA) is an essential algorithm for dimensionality reduction in many data science domains. We address the problem of performing a federated PCA on private data distributed among multiple data providers while ensuring data confi ...

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Extracting value and insights from increasingly heterogeneous data sources involves multiple systems combining and consuming the data. With multi-modal and context-rich data such as strings, text, videos, or images, the problem of standardizing the data mo ...

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In high dimension, low sample size (HDLSS) settings, classifiers based on Euclidean distances like the nearest neighbor classifier and the average distance classifier perform quite poorly if differences between locations of the underlying populations get m ...

Moving walkways are pedestrian dedicated hardware which generally decrease pedestrian travel time. We propose the utilization of these devices to dynamically control pedestrian flows in order to improve pedestrian dynamics. Three variations of a control st ...

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Recently, several theories including the replica method made predictions for the generalization error of Kernel Ridge Regression. In some regimes, they predict that the method has a 'spectral bias': decomposing the true function f* on the eigenbasis of the ...

LSH At Large - Distributed KNN Search in High Dimensions

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Learning the intrinsic dynamics of spatio-temporal processes through Latent Dynamics Networks

Social-Transmotion: Promptable Human Trajectory Prediction

An Iterative Adaptive Dynamic Programming Approach for Macroscopic Fundamental Diagram-Based Perimeter Control and Route Guidance

Towards Trustworthy Deep Learning for Image Reconstruction

Altruism, reciprocity, and tokens to reward forwarding data: Is that fair?

Interactive-time Exploration, Querying, and Analysis of Large High-dimensional Datasets

Aggregation and Exploration of High-Dimensional Data Using the Sudokube Data Cube Engine

Scalable and Privacy-Preserving Federated Principal Component Analysis

Promoting synergies for sustainability through peer-to-peer sharing: an analysis of drivers and barriers

STREAMING TENSOR TRAIN APPROXIMATION

E-Scan: Consuming Contextual Data with Model Plugins

Dimensionality reduction of time-series data, and systems and devices that use the resultant embeddings

Sparse Autoencoders for Speech Modeling and Recognition

Dual-frequency spectral radar retrieval of snowfall microphysics: a physics-driven deep-learning approach

Manifold Learning-Based Polynomial Chaos Expansions For High-Dimensional Surrogate Models

On Generalizations of Some Distance Based Classifiers for HDLSS Data

Controlling pedestrian flows with moving walkways

Fair Incentivization of Bandwidth Sharing in Decentralized Storage Networks

A machine learning approach to portfolio pricing and risk management for high-dimensional problems

Failure and success of the spectral bias prediction for Laplace Kernel Ridge Regression: the case of low-dimensional data