26 citations · 27 across the 3 of their papers we have counts for
5 papers
Mixture of Cache-Conditional Experts for Efficient Mobile Device Inference
Andrii Skliar, Ties van Rozendaal, Romain Lepert +5
Mixture of Experts (MoE) LLMs have recently gained attention for their ability to enhance performance by selectively engaging specialized subnetworks or "experts" for each input. H…
MobileNVC: Real-time 1080p Neural Video Compression on a Mobile Device
Ties van Rozendaal, Tushar Singhal, Hoang Le +10
Neural video codecs have recently become competitive with standard codecs such as HEVC in the low-delay setting. However, most neural codecs are large floating-point networks that…
Implicit Neural Video Compression
Yunfan Zhang, Ties van Rozendaal, Johann Brehmer +2
We propose a method to compress full-resolution video sequences with implicit neural representations. Each frame is represented as a neural network that maps coordinate positions t…
Lossy Compression with Distortion Constrained Optimization
Ties van Rozendaal, Guillaume Sautière, Taco S. Cohen
When training end-to-end learned models for lossy compression, one has to balance the rate and distortion losses. This is typically done by manually setting a tradeoff parameter $β…
Video Compression With Rate-Distortion Autoencoders
Amirhossein Habibian, Ties van Rozendaal, Jakub M. Tomczak +1
In this paper we present a a deep generative model for lossy video compression. We employ a model that consists of a 3D autoencoder with a discrete latent space and an autoregressi…