As the new congestion control aims at offering a lower than best effort transport service, this reaction was not built on solid technical foundation. This announcement immediately raised an unmotivated buzz about a new, imminent congestion collapse of the whole Internet.
Qarc meeting professional#
This is the first time that a computer program has defeated a human professional player in the full-sized game of Go, a feat previously thought to be at least a decade away.Ī few months ago, BitTorrent developers announced that the transfer of torrent data in the official client was about to switch to a new application-layer congestion-control protocol using UDP at the transport-layer. Using this search algorithm, our program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0. We also introduce a new search algorithm that combines Monte Carlo simulation with value and policy networks. Without any lookahead search, the neural networks play Go at the level of state-of-the-art Monte Carlo tree search programs that simulate thousands of random games of self-play.
These deep neural networks are trained by a novel combination of supervised learning from human expert games, and reinforcement learning from games of self-play. Here we introduce a new approach to computer Go that uses ‘value networks’ to evaluate board positions and ‘policy networks’ to select moves. The game of Go has long been viewed as the most challenging of classic games for artificial intelligence owing to its enormous search space and the difficulty of evaluating board positions and moves. In this paper, we describe the TensorFlow dataflow model in contrast to existing systems, and demonstrate the compelling performance that TensorFlow achieves for several real-world applications. Several Google services use TensorFlow in production, we have released it as an open-source project, and it has become widely used for machine learning research. TensorFlow supports a variety of applications, with particularly strong support for training and inference on deep neural networks. This architecture gives flexibility to the application developer: whereas in previous "parameter server" designs the management of shared state is built into the system, TensorFlow enables developers to experiment with novel optimizations and training algorithms. It maps the nodes of a dataflow graph across many machines in a cluster, and within a machine across multiple computational devices, including multicore CPUs, general-purpose GPUs, and custom designed ASICs known as Tensor Processing Units (TPUs). TensorFlow uses dataflow graphs to represent computation, shared state, and the operations that mutate that state. TensorFlow is a machine learning system that operates at large scale and in heterogeneous environments.
Qarc meeting Offline#
Meanwhile, Comparing QARC with offline optimal high bitrate method on various network conditions also yields a solid result. We evaluate QARC over a trace-driven emulation, outperforming existing approach with improvements in average video quality of 18\% - 25\% and decreases in average latency with 23% -45%. To overcome the "state explosion problem", we design a neural network to predict future perceptual video quality as a vector for taking the place of the raw picture in the DRL's inputs. Starting from scratch, QARC uses deep reinforcement learning(DRL) algorithm to train a neural network to select future bitrates based on previously observed network status and past video frames. In this paper, we propose QARC (video Quality Awareness Rate Control), a rate control algorithm that aims to have a higher perceptual video quality with possibly lower sending rate and transmission latency.
Qarc meeting how to#
Nevertheless, we notice that there exists a trade-off between sending bitrate and video quality, which motivates us to focus on how to get a balance between them.
To tackle this problem, most adaptive bitrate control methods have been proposed to provide high video bitrates instead of video qualities.
Due to the fluctuation of throughput under various network conditions, how to choose a proper bitrate adaptively has become an upcoming and interestingly issue. Real-time video streaming is now one of the main applications in all network environments.
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