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Agliari E, Alemanno F, Aquaro M, Fachechi A. Regularization, early-stopping and dreaming: A Hopfield-like setup to address generalization and overfitting. Neural Netw 2024; 177:106389. [PMID: 38788291 DOI: 10.1016/j.neunet.2024.106389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Revised: 04/12/2024] [Accepted: 05/12/2024] [Indexed: 05/26/2024]
Abstract
In this work we approach attractor neural networks from a machine learning perspective: we look for optimal network parameters by applying a gradient descent over a regularized loss function. Within this framework, the optimal neuron-interaction matrices turn out to be a class of matrices which correspond to Hebbian kernels revised by a reiterated unlearning protocol. Remarkably, the extent of such unlearning is proved to be related to the regularization hyperparameter of the loss function and to the training time. Thus, we can design strategies to avoid overfitting that are formulated in terms of regularization and early-stopping tuning. The generalization capabilities of these attractor networks are also investigated: analytical results are obtained for random synthetic datasets, next, the emerging picture is corroborated by numerical experiments that highlight the existence of several regimes (i.e., overfitting, failure and success) as the dataset parameters are varied.
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Affiliation(s)
- E Agliari
- Dipartimento di Matematica "Guido Castelnuovo", Sapienza Università di Roma, Italy; GNFM-INdAM, Gruppo Nazionale di Fisica Matematica (Istituto Nazionale di Alta Matematica), Italy.
| | - F Alemanno
- Dipartimento di Matematica, Università di Bologna, Italy; GNFM-INdAM, Gruppo Nazionale di Fisica Matematica (Istituto Nazionale di Alta Matematica), Italy
| | - M Aquaro
- Dipartimento di Matematica "Guido Castelnuovo", Sapienza Università di Roma, Italy; GNFM-INdAM, Gruppo Nazionale di Fisica Matematica (Istituto Nazionale di Alta Matematica), Italy
| | - A Fachechi
- Dipartimento di Matematica "Guido Castelnuovo", Sapienza Università di Roma, Italy; GNFM-INdAM, Gruppo Nazionale di Fisica Matematica (Istituto Nazionale di Alta Matematica), Italy
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Chen X, He W, Ye Z, Gai J, Lu W, Xing G. Soybean seed pest damage detection method based on spatial frequency domain imaging combined with RL-SVM. PLANT METHODS 2024; 20:130. [PMID: 39164761 PMCID: PMC11337654 DOI: 10.1186/s13007-024-01257-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Accepted: 08/02/2024] [Indexed: 08/22/2024]
Abstract
Soybean seeds are susceptible to damage from the Riptortus pedestris, which is a significant factor affecting the quality of soybean seeds. Currently, manual screening methods for soybean seeds are limited to visual inspection, making it difficult to identify seeds that are phenotypically defect-free but have been punctured by stink bugs on the sub-surface. To facilitate the convenient and efficient identification of healthy soybean seeds, this paper proposes a soybean seed pest detection method based on spatial frequency domain imaging combined with RL-SVM. Firstly, soybean optical data is obtained using single integration sphere technique, and the vigor index of soybean seeds is obtained through germination experiments. Then, based on the above two data items using feature extraction algorithms (the successive projections algorithm and the competitive adaptive reweighted sampling algorithm), the characteristic wavelengths of soybeans are identified. Subsequently, the spatial frequency domain imaging technique is used to obtain the sub-surface images of soybean seeds in a forward manner, and the optical coefficients such as the reduced scattering coefficientμ ' s and absorption coefficient μ a of soybean seeds are inverted. Finally, RL-MLR, RL-GRNN, and RL-SVM prediction models are established based on the ratio of the area of insect-damaged sub-surface to the entire seed, soybean varieties, and μ a at three wavelengths (502 nm, 813 nm, and 712 nm) for predicting and identifying soybean the stinging and sucking pest damage levels of soybean seeds. The experimental results show that the spatial frequency domain imaging technique yields small errors in the optical coefficients of soybean seeds, with errors of less than 15% for μ a and less than 10% forμ ' s . After parameter adjustment through reinforcement learning, the Macro-Recall metrics of each model have improved by 10%-15%, and the RL-SVM model achieves a high Macro-Recall value of 0.9635 for classifying the pest damage levels of soybean seeds.
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Affiliation(s)
- Xuanyu Chen
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, 210031, China
| | - Wei He
- College of Engineering, Nanjing Agricultural University, Nanjing, 210031, China
| | - Zhihao Ye
- Soybean Research Institute, MARA National Center for Soybean Improvement, MARA Key Laboratory of Biology and Genetic Improvement of Soybean, National Key Laboratory for Crop Genetics & Germplasm Enhancement and Utilization, Jiangsu Collaborative Innovation Center for Modern Crop Production, College of Agriculture, Nanjing Agricultural University, Nanjing, 210095, China
| | - Junyi Gai
- Soybean Research Institute, MARA National Center for Soybean Improvement, MARA Key Laboratory of Biology and Genetic Improvement of Soybean, National Key Laboratory for Crop Genetics & Germplasm Enhancement and Utilization, Jiangsu Collaborative Innovation Center for Modern Crop Production, College of Agriculture, Nanjing Agricultural University, Nanjing, 210095, China
| | - Wei Lu
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, 210031, China.
| | - Guangnan Xing
- Soybean Research Institute, MARA National Center for Soybean Improvement, MARA Key Laboratory of Biology and Genetic Improvement of Soybean, National Key Laboratory for Crop Genetics & Germplasm Enhancement and Utilization, Jiangsu Collaborative Innovation Center for Modern Crop Production, College of Agriculture, Nanjing Agricultural University, Nanjing, 210095, China.
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3
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Yuan H, Hong C, Jiang PT, Zhao G, Tran NTA, Xu X, Yan YY, Liu N. Clinical domain knowledge-derived template improves post hoc AI explanations in pneumothorax classification. J Biomed Inform 2024; 156:104673. [PMID: 38862083 DOI: 10.1016/j.jbi.2024.104673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Revised: 06/01/2024] [Accepted: 06/07/2024] [Indexed: 06/13/2024]
Abstract
OBJECTIVE Pneumothorax is an acute thoracic disease caused by abnormal air collection between the lungs and chest wall. Recently, artificial intelligence (AI), especially deep learning (DL), has been increasingly employed for automating the diagnostic process of pneumothorax. To address the opaqueness often associated with DL models, explainable artificial intelligence (XAI) methods have been introduced to outline regions related to pneumothorax. However, these explanations sometimes diverge from actual lesion areas, highlighting the need for further improvement. METHOD We propose a template-guided approach to incorporate the clinical knowledge of pneumothorax into model explanations generated by XAI methods, thereby enhancing the quality of the explanations. Utilizing one lesion delineation created by radiologists, our approach first generates a template that represents potential areas of pneumothorax occurrence. This template is then superimposed on model explanations to filter out extraneous explanations that fall outside the template's boundaries. To validate its efficacy, we carried out a comparative analysis of three XAI methods (Saliency Map, Grad-CAM, and Integrated Gradients) with and without our template guidance when explaining two DL models (VGG-19 and ResNet-50) in two real-world datasets (SIIM-ACR and ChestX-Det). RESULTS The proposed approach consistently improved baseline XAI methods across twelve benchmark scenarios built on three XAI methods, two DL models, and two datasets. The average incremental percentages, calculated by the performance improvements over the baseline performance, were 97.8% in Intersection over Union (IoU) and 94.1% in Dice Similarity Coefficient (DSC) when comparing model explanations and ground-truth lesion areas. We further visualized baseline and template-guided model explanations on radiographs to showcase the performance of our approach. CONCLUSIONS In the context of pneumothorax diagnoses, we proposed a template-guided approach for improving model explanations. Our approach not only aligns model explanations more closely with clinical insights but also exhibits extensibility to other thoracic diseases. We anticipate that our template guidance will forge a novel approach to elucidating AI models by integrating clinical domain expertise.
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Affiliation(s)
- Han Yuan
- Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore
| | - Chuan Hong
- Department of Biostatistics and Bioinformatics, Duke University, USA
| | | | - Gangming Zhao
- Faculty of Engineering, The University of Hong Kong, China
| | | | - Xinxing Xu
- Institute of High Performance Computing, Agency for Science, Technology and Research, Singapore
| | - Yet Yen Yan
- Department of Radiology, Changi General Hospital, Singapore
| | - Nan Liu
- Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore; Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore; Institute of Data Science, National University of Singapore, Singapore.
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Agliari E, Alemanno F, Aquaro M, Barra A, Durante F, Kanter I. Hebbian dreaming for small datasets. Neural Netw 2024; 173:106174. [PMID: 38359641 DOI: 10.1016/j.neunet.2024.106174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 01/02/2024] [Accepted: 02/09/2024] [Indexed: 02/17/2024]
Abstract
The dreaming Hopfield model constitutes a generalization of the Hebbian paradigm for neural networks, that is able to perform on-line learning when "awake" and also to account for off-line "sleeping" mechanisms. The latter have been shown to enhance storing in such a way that, in the long sleep-time limit, this model can reach the maximal storage capacity achievable by networks equipped with symmetric pairwise interactions. In this paper, we inspect the minimal amount of information that must be supplied to such a network to guarantee a successful generalization, and we test it both on random synthetic and on standard structured datasets (i.e., MNIST, Fashion-MNIST and Olivetti). By comparing these minimal thresholds of information with those required by the standard (i.e., always "awake") Hopfield model, we prove that the present network can save up to ∼90% of the dataset size, yet preserving the same performance of the standard counterpart. This suggests that sleep may play a pivotal role in explaining the gap between the large volumes of data required to train artificial neural networks and the relatively small volumes needed by their biological counterparts. Further, we prove that the model Cost function (typically used in statistical mechanics) admits a representation in terms of a standard Loss function (typically used in machine learning) and this allows us to analyze its emergent computational skills both theoretically and computationally: a quantitative picture of its capabilities as a function of its control parameters is achieved and consistency between the two approaches is highlighted. The resulting network is an associative memory for pattern recognition tasks that learns from examples on-line, generalizes correctly (in suitable regions of its control parameters) and optimizes its storage capacity by off-line sleeping: such a reduction of the training cost can be inspiring toward sustainable AI and in situations where data are relatively sparse.
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Affiliation(s)
- Elena Agliari
- Department of Mathematics of Sapienza Università di Roma, Rome, Italy.
| | - Francesco Alemanno
- Department of Mathematics and Physics of Università del Salento, Lecce, Italy
| | - Miriam Aquaro
- Department of Mathematics of Sapienza Università di Roma, Rome, Italy
| | - Adriano Barra
- Department of Mathematics and Physics of Università del Salento, Lecce, Italy.
| | - Fabrizio Durante
- Department of Economic Sciences of Università del Salento, Lecce, Italy
| | - Ido Kanter
- Department of Physics of Bar-Ilan University, Ramat Gan, Israel
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El Homsi M, Bane O, Fauveau V, Hectors S, Vietti Violi N, Sylla P, Ko HB, Cuevas J, Carbonell G, Nehlsen A, Vanguri R, Viswanath S, Jambawalikar S, Shaish H, Taouli B. Prediction of locally advanced rectal cancer response to neoadjuvant chemoradiation therapy using volumetric multiparametric MRI-based radiomics. Abdom Radiol (NY) 2024; 49:791-800. [PMID: 38150143 DOI: 10.1007/s00261-023-04128-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 11/06/2023] [Accepted: 11/12/2023] [Indexed: 12/28/2023]
Abstract
PURPOSE To assess the role of pretreatment multiparametric (mp)MRI-based radiomic features in predicting pathologic complete response (pCR) of locally advanced rectal cancer (LARC) to neoadjuvant chemoradiation therapy (nCRT). METHODS This was a retrospective dual-center study including 98 patients (M/F 77/21, mean age 60 years) with LARC who underwent pretreatment mpMRI followed by nCRT and total mesorectal excision or watch and wait. Fifty-eight patients from institution 1 constituted the training set and 40 from institution 2 the validation set. Manual segmentation using volumes of interest was performed on T1WI pre-/post-contrast, T2WI and diffusion-weighted imaging (DWI) sequences. Demographic information and serum carcinoembryonic antigen (CEA) levels were collected. Shape, 1st and 2nd order radiomic features were extracted and entered in models based on principal component analysis used to predict pCR. The best model was obtained using a k-fold cross-validation method on the training set, and AUC, sensitivity and specificity for prediction of pCR were calculated on the validation set. RESULTS Stage distribution was T3 (n = 79) or T4 (n = 19). Overall, 16 (16.3%) patients achieved pCR. Demographics, MRI TNM stage, and CEA were not predictive of pCR (p range 0.59-0.96), while several radiomic models achieved high diagnostic performance for prediction of pCR (in the validation set), with AUCs ranging from 0.7 to 0.9, with the best model based on high b-value DWI demonstrating AUC of 0.9 [95% confidence intervals: 0.67, 1], sensitivity of 100% [100%, 100%], and specificity of 81% [66%, 96%]. CONCLUSION Radiomic models obtained from pre-treatment MRI show good to excellent performance for the prediction of pCR in patients with LARC, superior to clinical parameters and CEA. A larger study is needed for confirmation of these results.
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Affiliation(s)
- Maria El Homsi
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, USA.
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York av, New York, USA.
| | - Octavia Bane
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Valentin Fauveau
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Stefanie Hectors
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Naik Vietti Violi
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Radiology, Lausanne University Hospital, Lausanne, Switzerland
| | - Patricia Sylla
- Department of Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Huai-Bin Ko
- Department of Pathology, Molecular and Cell Based Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Pathology, Columbia University Medical Center, New York, NY, USA
| | - Jordan Cuevas
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Guillermo Carbonell
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Radiology, Virgen de la Arrixaca University Clinical Hospital, University of Murcia, Murcia, Spain
| | - Anthony Nehlsen
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Rami Vanguri
- Department of Epidemiology & Biostatistics, Columbia University Medical Center, New York, NY, USA
| | - Satish Viswanath
- Department of Radiology, Case Western University, Cleveland, OH, USA
| | - Sachin Jambawalikar
- Department of Radiology, Columbia University Medical Center, New York, NY, USA
| | - Hiram Shaish
- Department of Radiology, Columbia University Medical Center, New York, NY, USA
| | - Bachir Taouli
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, USA
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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Leonetti M, Gosti G, Ruocco G. Photonic Stochastic Emergent Storage for deep classification by scattering-intrinsic patterns. Nat Commun 2024; 15:505. [PMID: 38218858 PMCID: PMC10787794 DOI: 10.1038/s41467-023-44498-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 12/15/2023] [Indexed: 01/15/2024] Open
Abstract
Disorder is a pervasive characteristic of natural systems, offering a wealth of non-repeating patterns. In this study, we present a novel storage method that harnesses naturally-occurring random structures to store an arbitrary pattern in a memory device. This method, the Stochastic Emergent Storage (SES), builds upon the concept of emergent archetypes, where a training set of imperfect examples (prototypes) is employed to instantiate an archetype in a Hopfield-like network through emergent processes. We demonstrate this non-Hebbian paradigm in the photonic domain by utilizing random transmission matrices, which govern light scattering in a white-paint turbid medium, as prototypes. Through the implementation of programmable hardware, we successfully realize and experimentally validate the capability to store an arbitrary archetype and perform classification at the speed of light. Leveraging the vast number of modes excited by mesoscopic diffusion, our approach enables the simultaneous storage of thousands of memories without requiring any additional fabrication efforts. Similar to a content addressable memory, all stored memories can be collectively assessed against a given pattern to identify the matching element. Furthermore, by organizing memories spatially into distinct classes, they become features within a higher-level categorical (deeper) optical classification layer.
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Affiliation(s)
- Marco Leonetti
- Soft and Living Matter Laboratory, Institute of Nanotechnology, 00185, Rome, Italy.
- Center for Life Nano- & Neuro-Science, Italian Institute of Technology, Rome, Italy.
- Rebel Dynamics-IIT CLN2S Jointlab, 00161, Roma, Italy.
| | - Giorgio Gosti
- Soft and Living Matter Laboratory, Institute of Nanotechnology, 00185, Rome, Italy
- Center for Life Nano- & Neuro-Science, Italian Institute of Technology, Rome, Italy
- Istituto di Scienze del Patrimonio Culturale, Sede di Roma, Consiglio Nazionale delle Ricerche, 00010, Montelibretti (RM), Italy
| | - Giancarlo Ruocco
- Center for Life Nano- & Neuro-Science, Italian Institute of Technology, Rome, Italy
- Department of Physics, University Sapienza, I-00185, Roma, Italy
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7
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Meir Y, Tevet O, Tzach Y, Hodassman S, Gross RD, Kanter I. Efficient shallow learning as an alternative to deep learning. Sci Rep 2023; 13:5423. [PMID: 37080998 PMCID: PMC10119101 DOI: 10.1038/s41598-023-32559-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 03/29/2023] [Indexed: 04/22/2023] Open
Abstract
The realization of complex classification tasks requires training of deep learning (DL) architectures consisting of tens or even hundreds of convolutional and fully connected hidden layers, which is far from the reality of the human brain. According to the DL rationale, the first convolutional layer reveals localized patterns in the input and large-scale patterns in the following layers, until it reliably characterizes a class of inputs. Here, we demonstrate that with a fixed ratio between the depths of the first and second convolutional layers, the error rates of the generalized shallow LeNet architecture, consisting of only five layers, decay as a power law with the number of filters in the first convolutional layer. The extrapolation of this power law indicates that the generalized LeNet can achieve small error rates that were previously obtained for the CIFAR-10 database using DL architectures. A power law with a similar exponent also characterizes the generalized VGG-16 architecture. However, this results in a significantly increased number of operations required to achieve a given error rate with respect to LeNet. This power law phenomenon governs various generalized LeNet and VGG-16 architectures, hinting at its universal behavior and suggesting a quantitative hierarchical time-space complexity among machine learning architectures. Additionally, the conservation law along the convolutional layers, which is the square-root of their size times their depth, is found to asymptotically minimize error rates. The efficient shallow learning that is demonstrated in this study calls for further quantitative examination using various databases and architectures and its accelerated implementation using future dedicated hardware developments.
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Affiliation(s)
- Yuval Meir
- Department of Physics, Bar-Ilan University, 52900, Ramat-Gan, Israel
| | - Ofek Tevet
- Department of Physics, Bar-Ilan University, 52900, Ramat-Gan, Israel
| | - Yarden Tzach
- Department of Physics, Bar-Ilan University, 52900, Ramat-Gan, Israel
| | - Shiri Hodassman
- Department of Physics, Bar-Ilan University, 52900, Ramat-Gan, Israel
| | - Ronit D Gross
- Department of Physics, Bar-Ilan University, 52900, Ramat-Gan, Israel
| | - Ido Kanter
- Department of Physics, Bar-Ilan University, 52900, Ramat-Gan, Israel.
- Gonda Interdisciplinary Brain Research Center, Bar-Ilan University, 52900, Ramat-Gan, Israel.
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Fachechi A, Barra A, Agliari E, Alemanno F. Outperforming RBM Feature-Extraction Capabilities by "Dreaming" Mechanism. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022; PP:1172-1181. [PMID: 35724278 DOI: 10.1109/tnnls.2022.3182882] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Inspired by a formal equivalence between the Hopfield model and restricted Boltzmann machines (RBMs), we design a Boltzmann machine, referred to as the dreaming Boltzmann machine (DBM), which achieves better performances than the standard one. The novelty in our model lies in a precise prescription for intralayer connections among hidden neurons whose strengths depend on features correlations. We analyze learning and retrieving capabilities in DBMs, both theoretically and numerically, and compare them to the RBM reference. We find that, in a supervised scenario, the former significantly outperforms the latter. Furthermore, in the unsupervised case, the DBM achieves better performances both in features extraction and representation learning, especially when the network is properly pretrained. Finally, we compare both models in simple classification tasks and find that the DBM again outperforms the RBM reference.
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Shougat MREU, Li X, Perkins E. Dynamic effects on reservoir computing with a Hopf oscillator. Phys Rev E 2022; 105:044212. [PMID: 35590621 DOI: 10.1103/physreve.105.044212] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Accepted: 04/06/2022] [Indexed: 06/15/2023]
Abstract
Limit cycle oscillators have the potential to be resourced as reservoir computers due to their rich dynamics. Here, a Hopf oscillator is used as a physical reservoir computer by discarding the delay line and time-multiplexing procedure. A parametric study is used to uncover computational limits imposed by the dynamics of the oscillator using parity and chaotic time-series prediction benchmark tasks. Resonance, frequency ratios from the Farey sequence, and Arnold tongues were found to strongly affect the computation ability of the reservoir. These results provide insights into fabricating physical reservoir computers from limit cycle systems.
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Affiliation(s)
- Md Raf E Ul Shougat
- Department of Mechanical & Aerospace Engineering, LAB2701: Nonlinear Dynamics Laboratory, North Carolina State University, Raleigh, North Carolina 27695, USA
| | - XiaoFu Li
- Department of Mechanical & Aerospace Engineering, LAB2701: Nonlinear Dynamics Laboratory, North Carolina State University, Raleigh, North Carolina 27695, USA
| | - Edmon Perkins
- Department of Mechanical & Aerospace Engineering, LAB2701: Nonlinear Dynamics Laboratory, North Carolina State University, Raleigh, North Carolina 27695, USA
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