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Found 9 papers, 5 papers with code
Tenplex: Dynamic Parallelism for Deep Learning using Parallelizable Tensor Collections
After a GPU change, Scalai uses the PTC to transform the job state: the PTC repartitions the dataset state under data parallelism and exposes it to DL workers through a virtual file system; and the PTC obtains the model state as partitioned checkpoints and transforms them to reflect the new parallelization configuration.
Quiver: Supporting GPUs for Low-Latency, High-Throughput GNN Serving with Workload Awareness
Quiver's key idea is to exploit workload metrics for predicting the irregular computation of GNN requests, and governing the use of GPUs for graph sampling and feature aggregation: (1) for graph sampling, Quiver calculates the probabilistic sampled graph size, a metric that predicts the degree of parallelism in graph sampling.
Fast and Flexible Human Pose Estimation with HyperPose
When it comes to customising these algorithms for real-world applications, none of the existing libraries can offer both the flexibility of developing custom pose estimation algorithms and the high-performance of executing these algorithms on commodity devices.
Learning Generalized Spatial-Temporal Deep Feature Representation for No-Reference Video Quality Assessment
In this work, we propose a no-reference video quality assessment method, aiming to achieve high-generalization capability in cross-content, -resolution and -frame rate quality prediction.
Efficient Reinforcement Learning Development with RLzoo
RLzoo provides developers with (i) high-level yet flexible APIs for prototyping DRL agents, and further customising the agents for best performance, (ii) a model zoo where users can import a wide range of DRL agents and easily compare their performance, and (iii) an algorithm that can automatically construct DRL agents with custom components (which are critical to improve agent's performance in custom applications).
For Intelligent and Higher Spectrum Efficiency: A Variable Packing Ratio Transmission System Based on Faster-than-Nyquist and Deep Learning
With the rapid development of various services in wireless communications, spectrum resource has become increasingly valuable.
TailorGAN: Making User-Defined Fashion Designs
To overcome those limitations, we propose a novel self-supervised model to synthesize garment images with disentangled attributes (e. g., collar and sleeves) without paired data.
Active collaboration in relative observation for Multi-agent visual SLAM based on Deep Q Network
This paper proposes a unique active relative localization mechanism for multi-agent Simultaneous Localization and Mapping(SLAM), in which a agent to be observed are considered as a task, which is performed by others assisting that agent by relative observation.
Receiver Design for Faster-than-Nyquist Signaling: Deep-learning-based Architectures
Additionally, we propose a DL-based joint signal detection and decoding for FTN signaling to replace the complete baseband part in traditional FTN receivers.
