Industry
Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte Carlo
Marton Havasi, José Miguel Hernández-Lobato, Juan José Murillo-Fuentes
The best-fit Gaussian distribution is denoted with adashed line.
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.
Dancing to Music
Hsin-Ying Lee, Xiaodong Yang, Ming-Yu Liu, Ting-Chun Wang, Yu-Ding Lu, Ming-Hsuan Yang, Jan Kautz
Neural Information Processing Systems http://nips.cc/
The Cluster Description Problem - Complexity Results, Formulations and Approximations
Ian Davidson, Antoine Gourru, S Ravi
Neural Information Processing Systems http://nips.cc/
Finding Order in Chaos: A Novel Data Augmentation Method for Time Series in Contrastive Learning
The success of contrastive learning is well known to be dependent on data augmentation.
Scaling the Poisson GLM to massive neural datasets through polynomial approximations
David Zoltowski, Jonathan W. Pillow
Neural Information Processing Systems http://nips.cc/
