Deep Learning
Tensor Monte Carlo: Particle Methods for the GPU era
Neural Information Processing Systems http://nips.cc/
A Careful Examination of Large Language Model Performance on Grade School Arithmetic
Further analysis suggests a positive relationship (Spearman's r
Zero-Shot Tokenizer Transfer Benjamin Minixhofer
Our method comes close to the original models' performance in cross-lingual and
NAIS-Net: Stable Deep Networks from Non-Autonomous Differential Equations
Marco Ciccone, Marco Gallieri, Jonathan Masci, Christian Osendorfer, Faustino Gomez
Each block represents atime-invariant iterativeprocess as the first layer in thei-th block,xi(1), is unrolled into a pattern-dependent number,Ki, of processing stages, using weight matricesAi andBi. The skip connections from the input,ui, to all layers in blockimake the process nonautonomous. Blocks can be chained together (each block modeling adifferent latent space) by passing final latentrepresentation,xi(Ki),ofblockiastheinputtoblocki+1.
Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules
Niklas Gebauer, Michael Gastegger, Kristof Schütt
Neural Information Processing Systems http://nips.cc/
One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers
Ari Morcos, Haonan Yu, Michela Paganini, Yuandong Tian
Neural Information Processing Systems http://nips.cc/
