Figure 5 - available via license: Creative Commons Attribution 4.0 International
Content may be subject to copyright.
Accuracy of LeNet ONNs, depending on the amount of inserted identity layers and the variance level of the ONN, for (a) a network with tanh activation function and one copy, (b) a network with ReLU activation function and one copy, (c) a network with linear activation function and one copy, (d) a network with tanh activation function and two copies, (e) a network with ReLU activation function and two copies, (f) a network with linear activation function and two copies.
Source publication
All analog signal processing is fundamentally subject to noise, and this is also the case in modern implementations of Optical Neural Networks (ONNs). Therefore, to mitigate noise in ONNs, we propose two designs that are constructed from a given, possibly trained, Neural Network (NN) that one wishes to implement. Both designs have the capability th...
Contexts in source publication
Context 1
... insertion pattern is illustrated in Finally, we tune the variance terms of the covariance matrix in our noise model. The results are displayed in Figure 5. ...
Context 2
... Figure 5, we observe that the tanh and the ReLU networks perform as expected. Additional noisy layers decrease the accuracy and thus the same level of performance can only be achieved if the variance is lower. ...
Context 3
... short, in this paper, we have discussed the noise present in ONNs and described a mathematical model for the noise. We also investigate the numerical implications of the the mathematical model, with a specific focus on the effects of depth ( Figure 5). The proposed noise reduction schemes yield greater accuracy and the theoretical results (Theorem 1 and Corollary 3) guarantee that ONNs work just as noiseless NNs in the many copies limit. ...
Similar publications
Dunhuang murals are an artistic treasure of world cultural heritage. Dunhuang murals were in their heyday during the Tang Dynasty. The murals of this period were extremely artistic and representative. Integrating them with modern clothing design has great cultural significance and room for development. This article explores the integration of Dunhu...
Offline model-based optimization (MBO) aims to maximize a black-box objective function using only an offline dataset of designs and scores. A prevalent approach involves training a conditional generative model on existing designs and their associated scores, followed by the generation of new designs conditioned on higher target scores. However, the...
The integration of Large Language Models (LLMs) into autonomous driving systems demonstrates strong common sense and reasoning abilities, effectively addressing the pitfalls of purely data-driven methods. Current LLM-based agents require lengthy inference times and face challenges in interacting with real-time autonomous driving environments. A key...
![Figure 1: Aoluguya Ewenki traditional women's robe [1].](publication/382425811/figure/fig1/AS:11431281261373627@1721524584173/Aoluguya-Ewenki-traditional-womens-robe-1_Q320.jpg)
![Figure 3: Aoluguya Ewenki ethnic "birch bark culture" [4].](publication/382425811/figure/fig2/AS:11431281261346744@1721524584324/Aoluguya-Ewenki-ethnic-birch-bark-culture-4_Q320.jpg)
This paper explores the innovative design and cultural heritage of traditional costumes of the Aoluguya Ewenki ethnic group in the digital age. Through digital techniques, traditional costumes have been infused with modern elements, enhancing the intricacy and diversity of their designs, thus introducing new means of design for traditional attire....
All analog signal processing is fundamentally subject to noise, and this is also the case in next generation implementations of optical neural networks (ONNs). Therefore, we propose the first hardware-based approach to mitigate noise in ONNs. A tree-like and an accordion-like design are constructed from a given NN that one wishes to implement. Both...
