30 citations · 46 across the 6 of their papers we have counts for
9 papers
Pruning by Active Attention Manipulation
Zahra Babaiee, Lucas Liebenwein, Ramin Hasani +2
Filter pruning of a CNN is typically achieved by applying discrete masks on the CNN's filter weights or activation maps, post-training. Here, we present a new filter-importance-sco…
End-to-End Sensitivity-Based Filter Pruning
Zahra Babaiee, Lucas Liebenwein, Ramin Hasani +2
In this paper, we present a novel sensitivity-based filter pruning algorithm (SbF-Pruner) to learn the importance scores of filters of each layer end-to-end. Our method learns the…
Lost in Pruning: The Effects of Pruning Neural Networks beyond Test Accuracy
Lucas Liebenwein, Cenk Baykal, Brandon Carter +2
Neural network pruning is a popular technique used to reduce the inference costs of modern, potentially overparameterized, networks. Starting from a pre-trained network, the proces…
Deep Latent Competition: Learning to Race Using Visual Control Policies in Latent Space
Wilko Schwarting, Tim Seyde, Igor Gilitschenski +4
Learning competitive behaviors in multi-agent settings such as racing requires long-term reasoning about potential adversarial interactions. This paper presents Deep Latent Competi…
Machine Learning-based Estimation of Forest Carbon Stocks to increase Transparency of Forest Preservation Efforts
Björn Lütjens, Lucas Liebenwein, Katharina Kramer
An increasing amount of companies and cities plan to become CO2-neutral, which requires them to invest in renewable energies and carbon emission offsetting solutions. One of the ch…
Provable Filter Pruning for Efficient Neural Networks
Lucas Liebenwein, Cenk Baykal, Harry Lang +2
We present a provable, sampling-based approach for generating compact Convolutional Neural Networks (CNNs) by identifying and removing redundant filters from an over-parameterized…