Asia
Fully Neural Network Based Speech Recognition on Mobile and Embedded Devices
Jinhwan Park, Yoonho Boo, Iksoo Choi, Sungho Shin, Wonyong Sung
Neural Information Processing Systems http://nips.cc/
ed189de2611f200bd4c2ab30c576e99e-Supplemental-Conference.pdf
Because the above experiments are based on a VGG source model condition, we try to find out whether SACfingerprints thesource model successfully ondifferentmodel architectures.
Enhancing User Intent Capture in Session-Based Recommendation with Attribute Patterns
Besides evaluating the next-item prediction, we estimate the models' capabilities to capture
From voxels to pixels and back: Self-supervision in natural-image reconstruction from fMRI
Roman Beliy, Guy Gaziv, Assaf Hoogi, Francesca Strappini, Tal Golan, Michal Irani
Developing amethod forhigh-quality reconstruction ofseenimages fromthecorresponding brain activity is an important milestone towards decoding the contents of dreams and mental imagery (Fig 1a). In this task, one attempts to solve for the mapping between fMRI recordings and their corresponding natural images, using many "labeled"{Image, fMRI} pairs (i.e., images and their corresponding fMRIresponses).
Revisit last-iterate convergence of mSGD under milder requirement on step size
Understanding convergence of stochastic gradient descent (SGD) based optimization algorithms can help deal with enormous machine learning problems.
Exploiting Activation Sparsity with Dense to Dynamic-k Mixture-of-Experts Conversion Filip Szatkowski IDEAS NCBR Warsaw University of Technology Bartosz Wójcik
Finally, we develop an efficient implementation that translates these computational savings into actual wall-clock speedup.
Generativemultitasklearningmitigatestarget-causing confounding
We refer to them astarget-causing confounders.