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Found 5 papers, 0 papers with code
Continual Learning Long Short Term Memory
Catastrophic forgetting in neural networks indicates the performance decreasing of deep learning models on previous tasks while learning new tasks.
IS THE LABEL TRUSTFUL: TRAINING BETTER DEEP LEARNING MODEL VIA UNCERTAINTY MINING NET
During the training stage, we utilize all the available data (labeled and unlabeled) to train the classifier via a semi-supervised generative framework.
Hoard: A Distributed Data Caching System to Accelerate Deep Learning Training on the Cloud
Deep Learning system architects strive to design a balanced system where the computational accelerator -- FPGA, GPU, etc, is not starved for data.
Performance
dMath: Distributed Linear Algebra for DL
The paper presents a parallel math library, dMath, that demonstrates leading scaling when using intranode, internode, and hybrid-parallelism for deep learning (DL).
dMath: A Scalable Linear Algebra and Math Library for Heterogeneous GP-GPU Architectures
A new scalable parallel math library, dMath, is presented in this paper that demonstrates leading scaling when using intranode, or internode, hybrid-parallelism for deep-learning.
