A new way to map how cells choose their fate

Researchers from Kyushu University have developed an innovative computational method, called ddHodge, that can reconstruct ...
News-Medical.Net on MSN

New computational method reconstructs how cells decide their fate

Researchers from Kyushu University have developed an innovative computational method, called ddHodge, that can reconstruct ...
IEEE

Ultrafast Density Gradient Accumulation in 3D Analytical Placement with Divergence Theorem

Abstract: Density gradient accumulation plays a pivotal role in 3D analytical placement. Analytical placers rely on this fundamental operation during the backward step of each iteration to compute the ...
GitHub

Saving memory using gradient-checkpointing

Training very deep neural networks requires a lot of memory. Using the tools in this package, developed jointly by Tim Salimans and Yaroslav Bulatov, you can trade off some of this memory usage with ...
GitHub

Stein Variational Gradient Descent (SVGD)

SVGD is a general purpose variational inference algorithm that forms a natural counterpart of gradient descent for optimization. SVGD iteratively transports a set of particles to match with the target ...
IEEE

Activated Gradients for Deep Neural Networks

Abstract: Deep neural networks often suffer from poor performance or even training failure due to the ill-conditioned problem, the vanishing/exploding gradient problem, and the saddle point problem.
C&EN

Analytic Gradients for Complete Active Space Pair-Density Functional Theory

† Department of Chemistry, Chemical Theory Center, and the Minnesota Supercomputing Institute, The University of Minnesota, Minneapolis, Minnesota 55455, United States ‡ Department of ...