標(biāo)題: Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2024; 33rd International C Michael Wand,Kristína Malinovská,Igor V. Tetko Conferenc [打印本頁] 作者: 傷害 時間: 2025-3-21 19:17
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書目名稱Artificial Neural Networks and Machine Learning – ICANN 2024被引頻次
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書目名稱Artificial Neural Networks and Machine Learning – ICANN 2024讀者反饋
書目名稱Artificial Neural Networks and Machine Learning – ICANN 2024讀者反饋學(xué)科排名
作者: 系列 時間: 2025-3-21 23:48
Masked Image Modeling as?a?Framework for?Self-Supervised Learning Across Eye Movementstation influence the formation of category-specific representations. This allows us not only to better understand the principles behind MIM, but to then reassemble a MIM more in line with the focused nature of biological perception. We find that MIM disentangles neurons in latent space without expli作者: 無可爭辯 時間: 2025-3-22 01:29
Sparsity Aware Learning in?Feedback-Driven Differential Recurrent Neural Networks spatio-temporal input-output mappings. Our learning approach yields networks capable of accomplishing classification and sequential learning tasks with fewer neurons while exhibiting heightened performance compared to existing differential recurrent network training least-squares methods. Sparse d-作者: 減少 時間: 2025-3-22 08:25 作者: 愉快么 時間: 2025-3-22 11:36 作者: 狂熱語言 時間: 2025-3-22 16:53
Biologically-Plausible Markov Chain Monte Carlo Sampling from?Vector Symbolic Algebra-Encoded Distriistributions using Langevin dynamics in the VSA vector space, and demonstrate competitive sampling performance in a spiking-neural network implementation. Surprisingly, while the Langevin dynamics are not constrained to the manifold defined by the HRR encoding, the generated samples contain sufficie作者: paleolithic 時間: 2025-3-22 19:11
Dynamic Graph for?Biological Memory Modeling: A System-Level Validationaptive learning behavior is represented through a microcircuit centered around a variable resistor. We validated the model’s efficacy in storing and retrieving data through computer simulations. This approach offers a plausible biological explanation for memory realization and validates the memory t作者: meditation 時間: 2025-3-22 21:59
EEG Features Learned by?Convolutional Neural Networks Reflect Alterations of?Social Stimuli ProcessiCNN trained to detect P300 from EEG recordings of 15 ASD participants. Interpretable spectral and spatial features were extracted and used to define ICNN-derived measures. The ICNN-derived spatial measure at Pz, but not spectral measures, was found to be positively correlated to ADOS scores. Moreove作者: carbohydrate 時間: 2025-3-23 02:24
Estimate of?the?Storage Capacity of?,-Correlated Patterns in?Hopfield Neural Networksstimation of the storage capacity of memory NNs is crucial, as there is a limitation to the quantity of information that a finite NN can store and retrieve correctly. The storage capacity of the Hopfield associative memory model has been estimated to be proportional to the number of neurons in the n作者: entreat 時間: 2025-3-23 08:36 作者: SPALL 時間: 2025-3-23 10:48 作者: affinity 時間: 2025-3-23 16:49
Enhancing Counterfactual Image Generation Using Mahalanobis Distance with?Distribution Preferences iage counterfactual explanations. Our experiments demonstrate that the counterfactual explanations generated by our method closely resemble the original images in both pixel and feature spaces. Additionally, our method outperforms established baselines, achieving impressive experimental results.作者: monochromatic 時間: 2025-3-23 18:40
Exploring Task-Specific Dimensions in?Word Embeddings Through Automatic Rule Learningd the other for gender classification and sentiment analysis. Notably, the results reveal that the removal of gender-related dimensions significantly affects gender classification performance while having minimal impact on other tasks. This highlights that there exist different related dimensions fo作者: Adjourn 時間: 2025-3-23 23:46 作者: muster 時間: 2025-3-24 02:48 作者: 得罪人 時間: 2025-3-24 06:50 作者: 山崩 時間: 2025-3-24 12:10
Andrei N. Borodin,Paavo Salminentation influence the formation of category-specific representations. This allows us not only to better understand the principles behind MIM, but to then reassemble a MIM more in line with the focused nature of biological perception. We find that MIM disentangles neurons in latent space without expli作者: 組成 時間: 2025-3-24 17:06 作者: guzzle 時間: 2025-3-24 19:03 作者: 擁護者 時間: 2025-3-24 23:28
Sherri Sharp,Walter J. Meyer III M.D.e of PixelCNNs, as well as constraints in image resolution and complexity. In this work, we address these limitations and further investigate how attentional selection affects memory accuracy and generativity. First, we substitute the PixelCNN with a Transformer model (semantic memory) to capture un作者: 聽覺 時間: 2025-3-25 04:33
Maria Michaelidou M. D.,Manfred Freyistributions using Langevin dynamics in the VSA vector space, and demonstrate competitive sampling performance in a spiking-neural network implementation. Surprisingly, while the Langevin dynamics are not constrained to the manifold defined by the HRR encoding, the generated samples contain sufficie作者: 上腭 時間: 2025-3-25 09:02
Kathryn L. Butler,Robert L. Sheridanaptive learning behavior is represented through a microcircuit centered around a variable resistor. We validated the model’s efficacy in storing and retrieving data through computer simulations. This approach offers a plausible biological explanation for memory realization and validates the memory t作者: conflate 時間: 2025-3-25 14:34 作者: 小步舞 時間: 2025-3-25 18:44
Kunaal Jindal,Shahriar Shahrokhistimation of the storage capacity of memory NNs is crucial, as there is a limitation to the quantity of information that a finite NN can store and retrieve correctly. The storage capacity of the Hopfield associative memory model has been estimated to be proportional to the number of neurons in the n作者: sed-rate 時間: 2025-3-25 22:00 作者: 意外的成功 時間: 2025-3-26 03:34
Leopoldo C. Cancio,Jonathan B. Lundyimizing the impact of irrelevant context feature and purifying the feature space for more precise classification. We validate our model on CUB-200-2011, Stanford Cars, and WM-811K datasets. Both accuracy and robustness are significantly improved, which demonstrate notable improvements in accuracy an作者: 不能逃避 時間: 2025-3-26 05:13
Leopoldo C. Cancio,Steven E. Wolfage counterfactual explanations. Our experiments demonstrate that the counterfactual explanations generated by our method closely resemble the original images in both pixel and feature spaces. Additionally, our method outperforms established baselines, achieving impressive experimental results.作者: sclera 時間: 2025-3-26 09:01
https://doi.org/10.1007/978-3-030-18940-2d the other for gender classification and sentiment analysis. Notably, the results reveal that the removal of gender-related dimensions significantly affects gender classification performance while having minimal impact on other tasks. This highlights that there exist different related dimensions fo作者: 大都市 時間: 2025-3-26 13:21 作者: recede 時間: 2025-3-26 19:10
A Multiscale Resonant Spiking Neural Network for?Music Classificationobile devices becoming the dominant approaches to music, the light weight and mobile deployabiliy of music classification models are also of growing importance. Artificial Neural Networks(ANNs) have been the mainstream paradigms for music classification, but problems concerning with computational an作者: 委托 時間: 2025-3-26 21:06 作者: 鋼筆尖 時間: 2025-3-27 03:21
Serial Order Codes for?Dimensionality Reduction in?the?Learning of?Higher-Order Rules and?Compositiok for neural networks. One of the mechanisms that allows to capture hierarchical dependencies between items within sequences is ordinal coding. Ordinal patterns create a grammar, or a set of rules, that reduces the dimensionality of the search space and that can be used in a generative manner to com作者: indignant 時間: 2025-3-27 05:34
Sparsity Aware Learning in?Feedback-Driven Differential Recurrent Neural Networks effective learning of variable information gain makes training d-RNNs important for their inherent derivative of states property. In addition to training readout weights, the optimization of the intrinsic recurrent connection of the d-RNNs prove significant for performance enhancement. We introduce作者: Spina-Bifida 時間: 2025-3-27 13:00
Towards Scalable GPU-Accelerated SNN Training via?Temporal Fusionosely emulating the complex dynamics of biological neural networks. While SNNs show promising efficiency on specialized sparse-computational hardware, their practical training often relies on conventional GPUs. This reliance frequently leads to extended computation times when contrasted with traditi作者: 絕種 時間: 2025-3-27 14:44 作者: 真實的人 時間: 2025-3-27 19:00 作者: adj憂郁的 時間: 2025-3-27 23:09
Dynamic Graph for?Biological Memory Modeling: A System-Level Validation, but traditional graph models are static, lack the dynamic and autonomous behaviors of biological neural networks, rely on algorithms with a global view. This study introduces a novel dynamic directed graph model that simulates the brain’s memory process by empowering each node with adaptive learni作者: Bravado 時間: 2025-3-28 02:17 作者: 搜尋 時間: 2025-3-28 06:59 作者: REIGN 時間: 2025-3-28 13:17
Revealing Functions of?Extra-Large Excitatory Postsynaptic Potentials: Insights from?Dynamical Chara-tailed excitatory postsynaptic potentials (EPSPs), involving a minority of extra-large (XL) EPSPs, are currently garnering much attention, which strongly relates to cognitive functions. In addition to physiological studies, mathematical modeling approaches are effective in neuroscience because they作者: transdermal 時間: 2025-3-28 17:45
Counterfactual Contrastive Learning for?Fine Grained Image Classificationse approaches typically fall short in addressing the deeper causal relationships that underlie the visible features, leading to potential biases and limited generalizability. This paper presents a fine-grained causal contrastive network (FCCN), a novel architecture that integrates causal inference w作者: HARP 時間: 2025-3-28 22:37 作者: 獨輪車 時間: 2025-3-29 02:34 作者: Overdose 時間: 2025-3-29 06:40
Generally-Occurring Model Change for?Robust Counterfactual Explanationsng. Counterfactual explanation is an important method in the field of interpretable machine learning, which can not only help users understand why machine learning models make specific decisions, but also help users understand how to change these decisions. Naturally, it is an important task to stud作者: 有害處 時間: 2025-3-29 08:33
Model Based Clustering of?Time Series Utilizing Expert ODEsrs in the parameter space (. healthy vs. diseased patients). The problem of identifying these clusters and that of identifying the model parameters are tightly coupled. In this work, we propose a novel model-based clustering method that makes it possible to utilize expert knowledge in the form of pa作者: Audiometry 時間: 2025-3-29 14:22
Conference proceedings 2024ne Learning, ICANN 2024, held in Lugano, Switzerland, during September 17–20, 2024...The 294 full papers and 16 short papers included in these proceedings were carefully reviewed and selected from 764 submissions. The papers cover the following topics:?..Part I - theory of neural networks and machin作者: Pessary 時間: 2025-3-29 19:34 作者: Nostalgia 時間: 2025-3-29 22:02 作者: 音樂等 時間: 2025-3-30 01:21
A Multiscale Resonant Spiking Neural Network for?Music Classificatione proposed the Multiscale Resonance SNN model that can comprehensively utilize the rich musical temporal information. With only binary activated neurons and sparse information flows, our model have achieved comparable music classification performance in various datasets.作者: RUPT 時間: 2025-3-30 06:39
Serial Order Codes for?Dimensionality Reduction in?the?Learning of?Higher-Order Rules and?Compositiopolate sequences of items from the given repertoire. We demonstrate how this framework can be used to make the solver robust to exponentially growing complexity of the given task by reducing its dimensionality.作者: 不透明 時間: 2025-3-30 09:51 作者: Jingoism 時間: 2025-3-30 15:31 作者: Muffle 時間: 2025-3-30 18:27 作者: 東西 時間: 2025-3-31 00:22 作者: Insulin 時間: 2025-3-31 02:58
Andrei N. Borodin,Paavo Salminennformation such as object category. Biological agents achieve this in a largely autonomous manner, presumably via self-super-vised learning. Whereas previous attempts to model the underlying mechanisms were largely discriminative in nature, there is ample evidence that the brain employs a generative作者: 個人長篇演說 時間: 2025-3-31 07:49 作者: notice 時間: 2025-3-31 12:39
Rehabilitation and scar management effective learning of variable information gain makes training d-RNNs important for their inherent derivative of states property. In addition to training readout weights, the optimization of the intrinsic recurrent connection of the d-RNNs prove significant for performance enhancement. We introduce作者: 多余 時間: 2025-3-31 13:35
Sherri Sharp,Walter J. Meyer III M.D.osely emulating the complex dynamics of biological neural networks. While SNNs show promising efficiency on specialized sparse-computational hardware, their practical training often relies on conventional GPUs. This reliance frequently leads to extended computation times when contrasted with traditi作者: Aspiration 時間: 2025-3-31 19:10
Sherri Sharp,Walter J. Meyer III M.D.putational models have investigated this process in detail, and existing models have some limitations. In this study we develop and analyze a computational model that complements episodic memory with semantic information, looking into how attention affects the recall process in this integrated model作者: Clinch 時間: 2025-3-31 22:20 作者: NIB 時間: 2025-4-1 02:19
