The CEO of $2 billion AI training startup says that humans will stay involved in data creation for decades

Artificial intelligence won't be training AI anytime soon, says Invisible Technologies CEO.
IEEE

Application of Matrix Decomposition in Machine Learning

Abstract: Matrix decomposition is a mathematical method widely adopted in computer science for its reliability and outstanding performance. It is often chosen for use in the computer vision field, ...
Frontiers

Using a seasonal and trend decomposition algorithm to improve machine learning prediction of inflow from the Yellow River, China, into the sea

The Yellow River is the largest inflow into the Bohai Sea, and its inflow changes directly affect the ecological environment and marine health of the Bohai Sea. Therefore, accurate prediction of the ...
C&EN

Combining Bayesian Optimization, Singular Value Decomposition, and Machine Learning for Advanced Optical Design

Article Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to ...
GitHub

Matrix Calculus for Machine Learning and Beyond

We all know that calculus courses such as 18.01 and 18.02 are univariate and vector calculus, respectively. Modern applications such as machine learning and large-scale optimization require the next ...
C&EN

Inverse Design of Unitary Transmission Matrices in Silicon Photonic Coupled Waveguide Arrays Using a Neural Adjoint Model

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. The development of low-loss reconfigurable integrated optical devices enables further ...
marktechpost

Eleuther AI Introduces a Novel Machine Learning Framework for Analyzing Neural Network Training through the Jacobian Matrix

Neural networks have become foundational tools in computer vision, NLP, and many other fields, offering capabilities to model and predict complex patterns. The training process is at the center of ...
marktechpost

Matrix-Free Differentiation: Advancing Probabilistic Machine Learning

Automatic differentiation has transformed the development of machine learning models by eliminating complex, application-dependent gradient derivations. This transformation helps to calculate Jacobian ...