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Found 9 papers, 1 papers with code
Statistical Parameterized Physics-Based Machine Learning Digital Twin Models for Laser Powder Bed Fusion Process
In the context of laser powder bed fusion (LPBF) additive manufacturing, a digital twin of the manufacturing process can offer predictions for the produced parts, diagnostics for manufacturing defects, as well as control capabilities.
AutoQNN: An End-to-End Framework for Automatically Quantizing Neural Networks
QSS introduces five quantizing schemes and defines three new schemes as a candidate set for scheme search, and then uses the differentiable neural architecture search (DNAS) algorithm to seek the layer- or model-desired scheme from the set.
Human-Aware Robot Navigation via Reinforcement Learning with Hindsight Experience Replay and Curriculum Learning
In recent years, the growing demand for more intelligent service robots is pushing the development of mobile robot navigation algorithms to allow safe and efficient operation in a dense crowd.
Elastic Significant Bit Quantization and Acceleration for Deep Neural Networks
We implement ESB as an accelerator and quantitatively evaluate its efficiency on FPGAs.
HiDeNN-PGD: reduced-order hierarchical deep learning neural networks
This paper presents a proper generalized decomposition (PGD) based reduced-order model of hierarchical deep-learning neural networks (HiDeNN).
Bootstrap Inference for Hawkes and General Point Processes
Inference and testing in general point process models such as the Hawkes model is predominantly based on asymptotic approximations for likelihood-based estimators and tests.
EADNet: Efficient Asymmetric Dilated Network for Semantic Segmentation
Due to real-time image semantic segmentation needs on power constrained edge devices, there has been an increasing desire to design lightweight semantic segmentation neural network, to simultaneously reduce computational cost and increase inference speed.
Learning Mobile Robot Navigation in the Dense Crowd with Deep Reinforcement Learning
In recent years, the growing demand for more intelligent service robots is pushing the development of mobile robot navigation algorithms.
VecQ: Minimal Loss DNN Model Compression With Vectorized Weight Quantization
Quantization has been proven to be an effective method for reducing the computing and/or storage cost of DNNs.
