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Found 9 papers, 0 papers with code
CodeIt: Self-Improving Language Models with Prioritized Hindsight Replay
Our method iterates between 1) program sampling and hindsight relabeling, and 2) learning from prioritized experience replay.
MobileNVC: Real-time 1080p Neural Video Compression on a Mobile Device
This work presents the first neural video codec that decodes 1080p YUV420 video in real time on a mobile device.
A Residual Diffusion Model for High Perceptual Quality Codec Augmentation
Diffusion probabilistic models have recently achieved remarkable success in generating high quality image and video data.
Boosting neural video codecs by exploiting hierarchical redundancy
In video compression, coding efficiency is improved by reusing pixels from previously decoded frames via motion and residual compensation.
MobileCodec: Neural Inter-frame Video Compression on Mobile Devices
Realizing the potential of neural video codecs on mobile devices is a big technological challenge due to the computational complexity of deep networks and the power-constrained mobile hardware.
Instance-Adaptive Video Compression: Improving Neural Codecs by Training on the Test Set
We introduce a video compression algorithm based on instance-adaptive learning.
Parallelized Rate-Distortion Optimized Quantization Using Deep Learning
We train two classes of neural networks, a fully-convolutional network and an auto-regressive network, and evaluate each as a post-quantization step designed to refine cheap quantization schemes such as scalar quantization (SQ).
Predictive Sampling with Forecasting Autoregressive Models
Autoregressive models (ARMs) currently hold state-of-the-art performance in likelihood-based modeling of image and audio data.
Simulating Execution Time of Tensor Programs using Graph Neural Networks
Optimizing the execution time of tensor program, e. g., a convolution, involves finding its optimal configuration.
