Yiren Zhou, Seyed-Mohsen Moosavi-Dezfooli, Ngai-Man Cheung, and Pascal Frossard, "Adaptive quantization for deep neural network," in AAAI, 2018.Y. Zhou, S. M. Moosavi Dezfooli, N.-M. Cheung, and P. Frossard. Adaptive quantization for deep neural network. In AAAI, number CONF, 2018...
We propose a Double EXponential Adaptive Threshold (DEXAT) neuron model that improves the performance of neuromorphic Recurrent Spiking Neural Networks (RSNNs) by providing faster convergence, higher accuracy and a flexible long short-term memory. We pre
The HSTNN presents a general hybrid strategy for integrating different network paradigms, suitable for various sequential learning tasks. We first evaluate its comprehensive performance in terms of both task accuracy and the computational cost in four different types of tasks, as shown in Fig.2. For...
Deep partial updating (2020) arXiv preprint arXiv:2007.03071 Google Scholar [7] Zhongnan Qu, Zimu Zhou, Yun Cheng, Lothar Thiele, Adaptive loss-aware quantization for multi-bit networks, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 7988–799...
A Deep Recur- rent Neural Network with LSTM blocks [31] was proposed, which was able to adapt between different scenarios seamlessly, but it doesn't take speed into ac- count for requiring 0.8-1.6 seconds for inference. Tonioni et al. [8] proposed the first real-time self-adaptive deep...
Deep Learning with Low Precision by Half-Wave Gaussian Quantization a piecewise constant sign function, which is used in feedforward network computations, and a piecewise linear hard tanh function, used in the backpropagatio... Z Cai,X He,S Jian,... - IEEE 被引量: 75发表: 2017年 Erf...
FedPAQ: a communication-efficient federated learning method with periodic averaging and quantization (2020), pp. 2021-31 View in ScopusGoogle Scholar 32. N Shlezinger, M Chen, YC Eldar, HV Poor, S Cui UVeQFed: universal vector quantization for federated learning IEEE Trans Signal Process, 69...
Specifically, in the process of building context model, there will be the problem of context dilution, so we need to design a suitable quantization method for mean value so that solve the problem of context dilution, this is a dynamic programming problem and the optimization objective of the qu...
correlation time and power spectral density, and total variance method is effective to evaluate five kinds of stochastic noise of load data before and after feature engineering, including load random walk (L), bias instability (B), rate ramp walk (K), rate ramp (R) and quantization noise (...
This paper studies the problem of fuzzy adaptive secure tracking control for nonstrict-feedback nonlinear systems with unknown false data injection (FDI) attacks and input quantization. In the nonstrict-feedback nonlinear systems, the nonlinear functions contain all state variables so that the existing...