Deep Learning
LSH-MoE: Communication-efficient MoE Training via Locality-Sensitive Hashing
GPUs remains a significant challenge.
Incorporating Context into Language Encoding Models for fMRI
Neural Information Processing Systems http://nips.cc/
BinGAN: Learning Compact Binary Descriptors with a Regularized GAN
Maciej Zieba, Piotr Semberecki, Tarek El-Gaaly, Tomasz Trzcinski
We adjust this regularization term so that it concentrates on increasing the entropy of the particular pairs of binary vectors that are not correlated in high-dimensional space.
VTaC: A Benchmark Dataset of Ventricular Tachycardia Alarms from ICU Monitors Li-wei H. Lehman
Accurate and reliable algorithms that can distinguish between true vs. false arrhythmia alarms can
Normalization Helps Training of Quantized LSTM
Lu Hou, Jinhua Zhu, James Kwok, Fei Gao, Tao Qin, Tie-Yan Liu
However, existing quantization methods usually have inferior performancewhenusedonLSTMs.
StepbaQ: Stepping backward as Correction for Quantized Diffusion Models
Quantization of diffusion models has attracted considerable attention due to its potential to enable various applications on resource-constrained mobile devices.
Episodic Memory in Lifelong Language Learning
Cyprien de Masson d'Autume, Sebastian Ruder, Lingpeng Kong, Dani Yogatama
Neural Information Processing Systems http://nips.cc/