263 citations · 278 across the 10 of their papers we have counts for
4 papers · 1 filter
Gradient-based Weight Density Balancing for Robust Dynamic Sparse Training
Mathias Parger, Alexander Ertl, Paul Eibensteiner +3
Training a sparse neural network from scratch requires optimizing connections at the same time as the weights themselves. Typically, the weights are redistributed after a predefine…
MotionDeltaCNN: Sparse CNN Inference of Frame Differences in Moving Camera Videos
Mathias Parger, Chengcheng Tang, Thomas Neff +4
Convolutional neural network inference on video input is computationally expensive and requires high memory bandwidth. Recently, DeltaCNN managed to reduce the cost by only process…
AdaNeRF: Adaptive Sampling for Real-time Rendering of Neural Radiance Fields
Andreas Kurz, Thomas Neff, Zhaoyang Lv +2
Novel view synthesis has recently been revolutionized by learning neural radiance fields directly from sparse observations. However, rendering images with this new paradigm is slow…
DeltaCNN: End-to-End CNN Inference of Sparse Frame Differences in Videos
Mathias Parger, Chengcheng Tang, Christopher D. Twigg +3
Convolutional neural network inference on video data requires powerful hardware for real-time processing. Given the inherent coherence across consecutive frames, large parts of a v…