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Found 5 papers, 2 papers with code
MuZero with Self-competition for Rate Control in VP9 Video Compression
Specifically, we target the problem of learning a rate control policy to select the quantization parameters (QP) in the encoding process of libvpx, an open source VP9 video compression library widely used by popular video-on-demand (VOD) services.
Neural Rate Control for Video Encoding using Imitation Learning
In modern video encoders, rate control is a critical component and has been heavily engineered.
DDSP: Differentiable Digital Signal Processing
In this paper, we introduce the Differentiable Digital Signal Processing (DDSP) library, which enables direct integration of classic signal processing elements with deep learning methods.
Graph Matching Networks for Learning the Similarity of Graph Structured Objects
This paper addresses the challenging problem of retrieval and matching of graph structured objects, and makes two key contributions.
A Generalized Framework for Population Based Training
Population Based Training (PBT) is a recent approach that jointly optimizes neural network weights and hyperparameters which periodically copies weights of the best performers and mutates hyperparameters during training.
