2 citations · 2 across the 1 of their papers we have counts for
3 papers
cs.LG2020
Holistic Filter Pruning for Efficient Deep Neural Networks
Lukas Enderich, Fabian Timm, Wolfram Burgard
Deep neural networks (DNNs) are usually over-parameterized to increase the likelihood of getting adequate initial weights by random initialization. Consequently, trained DNNs have…
cs.LG2020
SYMOG: learning symmetric mixture of Gaussian modes for improved fixed-point quantization
Lukas Enderich, Fabian Timm, Wolfram Burgard
Deep neural networks (DNNs) have been proven to outperform classical methods on several machine learning benchmarks. However, they have high computational complexity and require po…
cs.LG2019★ 2 cited
Learning Multimodal Fixed-Point Weights using Gradient Descent
Lukas Enderich, Fabian Timm, Lars Rosenbaum +1
Due to their high computational complexity, deep neural networks are still limited to powerful processing units. To promote a reduced model complexity by dint of low-bit fixed-poin…