DataSketches: A Production Quality Sketching Library for Big Data Analysis
Explore sketching algorithms for efficient big data analysis, enabling fast approximate answers to complex queries and real-time processing of massive datasets in production environments.
On Quantum Linear Algebra for Machine Learning - IPAM at UCLA
Explore quantum singular value transformation for machine learning, its applications, limitations, and connections to classical algorithms in this technical talk on quantum linear algebra.
Sublinear Time Eigenvalue Approximation via Random Sampling
Explore efficient algorithms for approximating eigenvalues of large matrices using random sampling techniques, with applications in data analysis and machine learning.
Sketching and Randomized Linear Algebra
Explore advanced techniques in randomized linear algebra, focusing on sketching methods for efficient data analysis and algorithm design in high-dimensional spaces.
Maximum Matching in O(log log n) Passes in Dynamic Streams
Explore an algorithm for approximating maximum matching in dynamic streams, achieving O(1)-approximation in O(log log n) passes and O(n poly log n) space, with lower bound proof.
On Quantum Linear Algebra for Machine Learning - Quantum Colloquium
Explore quantum singular value transformation for machine learning, its applications, barriers to quantum speedups, and connections to classical algorithms.
Streaming and Learning Algorithms - Session 7C
Explore cutting-edge streaming and learning algorithms in this IEEE session, focusing on recent advancements and applications in data processing and machine learning.
Capacity Analysis of Vector Symbolic Architectures
Explores representation capacities of vector symbolic architectures, analyzing their ability to perform symbolic tasks and comparing them to a novel Hopfield network variant.
Sketching Algorithms for Max-DICUT and Other CSPs
Explore algorithms for Maximum Directed Cut problem in graph theory, focusing on sketching techniques, space complexity, and applications to Constraint Satisfaction Problems.
OctoSketch - Enabling Real-Time, Continuous Network Monitoring over Multiple Cores
Innovative framework for scaling sketching algorithms across multiple cores, enhancing network monitoring accuracy and performance with continuous, change-based aggregation mechanism.
Applications of Sub-linear Algorithms in Other Fields
Explore how sub-linear algorithms impact diverse fields, enhancing efficiency and scalability in data analysis and problem-solving across disciplines.
Sketching Algorithms: Making Sense of Big Data in a Single Stroke
Explore sketching algorithms for efficient big data processing, covering concepts, design principles, and practical applications using open-source tools like Apache DataSketches.
Private Analytics via Streaming, Sketching, and Silently Verifiable Proofs - 2024
Explore innovative techniques for private data analytics using streaming algorithms, sketching methods, and silently verifiable proofs in this cutting-edge IEEE presentation.