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Efficiently Exploring Combinatorial Perturbations From High Dimensional Observation

Offered By: Valence Labs via YouTube

Tags

Machine Learning Courses Data Science Courses Algorithms Courses Statistical Analysis Courses Computational Biology Courses Combinatorial Optimization Courses High-dimensional Data Courses

Course Description

Overview

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Explore efficient methods for investigating combinatorial perturbations derived from high-dimensional observations in this 45-minute conference talk by Jason Hartford from Valence Labs. Recorded at the 2024 Molecular Machine Learning conference hosted at Mila, the presentation delves into advanced techniques for analyzing complex data structures. Learn about cutting-edge approaches to handle high-dimensional data and their applications in molecular machine learning. Gain insights into how combinatorial perturbations can be effectively explored and utilized in research and practical applications. Connect with the speaker and other attendees through the Valence Labs Portal for further discussions and networking opportunities.

Syllabus

Efficiently Exploring Combinatorial Perturbations From High Dimensional Observation | Jason Hartford


Taught by

Valence Labs

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