標(biāo)題: Titlebook: Deep Learning Theory and Applications; 5th International Co Ana Fred,Allel Hadjali,Carlo Sansone Conference proceedings 2024 The Editor(s) [打印本頁] 作者: deduce 時(shí)間: 2025-3-21 19:58
書目名稱Deep Learning Theory and Applications影響因子(影響力)
書目名稱Deep Learning Theory and Applications影響因子(影響力)學(xué)科排名
書目名稱Deep Learning Theory and Applications網(wǎng)絡(luò)公開度
書目名稱Deep Learning Theory and Applications網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Deep Learning Theory and Applications被引頻次
書目名稱Deep Learning Theory and Applications被引頻次學(xué)科排名
書目名稱Deep Learning Theory and Applications年度引用
書目名稱Deep Learning Theory and Applications年度引用學(xué)科排名
書目名稱Deep Learning Theory and Applications讀者反饋
書目名稱Deep Learning Theory and Applications讀者反饋學(xué)科排名
作者: 有斑點(diǎn) 時(shí)間: 2025-3-21 23:24
978-3-031-66704-6The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: addict 時(shí)間: 2025-3-22 01:32
,Str?mungen mit mehreren Phasen,e from traditional statistical models to domain-specific algorithms, and more recently deep neural network models typically rely on raw time series observations, with alternative representations like higher-dimensional embedding used mainly for auxiliary analyses such as Topological Data Analysis (T作者: 襲擊 時(shí)間: 2025-3-22 06:11
nnovation and diversity in the field. This position paper advocates for a strategic pivot of small institutions to research directions that are computationally economical, specifically through a modular approach inspired by neurobiological mechanisms. We argue for a balanced approach that draws insp作者: 對(duì)手 時(shí)間: 2025-3-22 11:13
Zusammenfassung der Diskussionsformen,e heated HTF is then used in thermal power blocks to produce electricity in conventional steam generators. Unexpectedly high HTF temperatures may lead to degradation of the system components and reduced efficiency. Therefore, closely monitoring and maintaining the HTF’s operational temperatures is c作者: 有抱負(fù)者 時(shí)間: 2025-3-22 13:35 作者: 有抱負(fù)者 時(shí)間: 2025-3-22 17:29
Kompetenz: Den Alltag meistern,concentration of pollutant gas in the air, in order to estimate the position and intensity of a pollutant source. In a Hierarchical Agglomerative Clustering (HAC) framework we aim to regroup sensors of the same behavior, based on similarity measure, then, we keep only one sensor of each cluster. Unl作者: Rankle 時(shí)間: 2025-3-22 21:58
Denktaktik: Verschenke überraschungen,rdisciplinary field, delves into environmental themes within cultural works, broadening the scope of humanities’ focus on representation issues. Our objective is to pioneer a method for automated, dependable analysis of audiovisual narratives within fictional feature films, exploring the interplay b作者: 要素 時(shí)間: 2025-3-23 01:40 作者: 青石板 時(shí)間: 2025-3-23 08:21
,Führung in der Zweierbeziehung,n is most often encountered in agriculture for the classification of crop diseases. This can lead to challenges in training deep learning models, as they may become biased toward the majority class and perform poorly in predicting the minority class. One common approach to address class imbalance is作者: 逗留 時(shí)間: 2025-3-23 10:38
,Sich austauschen und unterstützen lassen,e typically used in such cases since mobile phones have become pervasive. However, this process can be time-consuming since a human is required to conduct the session and they must then upload responses to a database. We propose using Large Language Models (LLMs) to process an audio recording of the作者: 得體 時(shí)間: 2025-3-23 14:41
Kommunikation in Konfliktsituationen,tive monitoring of the flotation process and its associated production indicators. This study delves into a range of semantic segmentation methods and algorithms, notably including YOLO, Watershed, and Thresholding, to accurately process these images. Our investigation leads to the proposal of an in作者: 機(jī)構(gòu) 時(shí)間: 2025-3-23 20:45
,Personalentwicklung für virtuelle Teams,ent state-of-the-art SDD methods are already implementing some sort of self-supervision in their learning procedure, and we discuss how more advanced techniques inspired to Confident Learning can be used in a generic pipeline. We also propose One-Shot Removal strategy, a baseline approach that can b作者: Arthr- 時(shí)間: 2025-3-23 22:46 作者: 預(yù)測(cè) 時(shí)間: 2025-3-24 06:00
Philosophien und ihr praktischer Nutzen,om happening. One possible prevention method is monitoring areas most susceptible to fires and using computer vision techniques to detect these events as quickly as possible while they are still small-scale, accelerating the response of responsible authorities, and hence reducing environmental damag作者: 獨(dú)白 時(shí)間: 2025-3-24 09:55
,Auswertung zu den ?Wissensfragmenten“,preserving crucial scientific concepts, findings, and conclusions. In this work, we present a novel loss function that incorporates semantic similarity, and use it in the parallel training of extractive and abstractive summarizers, thereby improving the performance of the individual summarizer units作者: Heresy 時(shí)間: 2025-3-24 13:13 作者: Cardiac-Output 時(shí)間: 2025-3-24 15:16
,Die Führungskraft als Vorbild,oup at University Freiburg implemented a pipeline based on the TransMIL model [.]. To improve the general performance, we compared four different CNNs for feature extraction in the TransMIL preprocessing pipeline CLAM [.]. Comprehensive evaluations, including detailed analyses of loss, accuracy, F1 作者: 暗諷 時(shí)間: 2025-3-24 22:26 作者: 泥土謙卑 時(shí)間: 2025-3-25 02:47 作者: 離開可分裂 時(shí)間: 2025-3-25 04:24
Eigenschaften der Generation Y,his task is achieved using different deep learning models, not all models work well for all classes and their instances. There is limited work in the use of ensemble methods for LiDAR point cloud analysis that finds the optimal model from a set of existing models. We propose a workflow for an ensemb作者: 模范 時(shí)間: 2025-3-25 08:40
Deep Learning Theory and Applications978-3-031-66705-3Series ISSN 1865-0929 Series E-ISSN 1865-0937 作者: ARIA 時(shí)間: 2025-3-25 11:56 作者: 一起 時(shí)間: 2025-3-25 16:57
Conference proceedings 2024 DeLTA 2024, which took place in Dijon, France, during July 10-11, 2024.?..The 44 papers included in these proceedings were carefully reviewed and selected from a total of 70 submissions. They focus on topics such as deep learning and big data analytics; machine-learning and artificial intelligence, etc.?.作者: 違反 時(shí)間: 2025-3-25 23:27
Zusammenfassung der Diskussionsformen,ed on the mean average error threshold. The study concludes by analysing the effectiveness of the encoder-decoder LSTM-based method in detecting over-temperature anomalies in historical plant data. The proposed approach allows operators to take preventive measures before any potential alarms by providing a 300-s forecast window.作者: Cupidity 時(shí)間: 2025-3-26 02:48 作者: flamboyant 時(shí)間: 2025-3-26 07:02 作者: 壓碎 時(shí)間: 2025-3-26 11:35
Eigenschaften der Generation Y,features as the meta-learning model, . the gating function. We tested the workflow on the nuScenes dataset using two ensembles, . different sets of CNNs to compare their performance. Our experimental results show that the ensemble of models demonstrates the expected results of overall improved accuracy.作者: 蘑菇 時(shí)間: 2025-3-26 14:21 作者: 意外 時(shí)間: 2025-3-26 17:01
,Automating the?Conducting of?Surveys Using Large Language Models,he text to a Large Language Model (GPT-4) which is prompted to extract the responses. The responses are then uploaded to a database. Finally we use an LLM to provide answers to questions about the survey responses. For multiple choice questions we obtained an accuracy score of 97%.作者: 勤勞 時(shí)間: 2025-3-26 22:14
,Deep Learning-Based Preprocessing Tools for?Turkish Natural Language Processing,del to train the character-level tools, and BERT and mT5 models for the token-based tools. We evaluate the framework for each task on the BOUN Treebank in the UD project and make both the tools and the codes publicly available.作者: 按時(shí)間順序 時(shí)間: 2025-3-27 02:02 作者: 滔滔不絕地講 時(shí)間: 2025-3-27 06:35
Conference proceedings 2024 DeLTA 2024, which took place in Dijon, France, during July 10-11, 2024.?..The 44 papers included in these proceedings were carefully reviewed and selected from a total of 70 submissions. They focus on topics such as deep learning and big data analytics; machine-learning and artificial intelligence,作者: 蘆筍 時(shí)間: 2025-3-27 10:16 作者: 下邊深陷 時(shí)間: 2025-3-27 13:40 作者: ambivalence 時(shí)間: 2025-3-27 20:21
,Personalentwicklung für virtuelle Teams,wed by removal of anomalous samples, and fine-tuning on the refined dataset. Experiments conducted on the challenging Kolektor SDD2 dataset show how this process enhances the representation of ‘normal’ data and mitigates overfitting risks.作者: 堅(jiān)毅 時(shí)間: 2025-3-28 01:35
,Pollutant Source Localization Based on?Siamese Neural Network Similarity Measure,orks (SNN). The methodology was tested on simulated measurements based on real atmospheric conditions. And Monte Carlo Markov Chain (MCMC) in a Bayesian inference framework was used to identify the source position and intensity.作者: 開始從未 時(shí)間: 2025-3-28 03:44 作者: Malcontent 時(shí)間: 2025-3-28 07:58
,Self-supervised Learning for?Robust Surface Defect Detection,wed by removal of anomalous samples, and fine-tuning on the refined dataset. Experiments conducted on the challenging Kolektor SDD2 dataset show how this process enhances the representation of ‘normal’ data and mitigates overfitting risks.作者: 不自然 時(shí)間: 2025-3-28 10:50
1865-0929 lications, DeLTA 2024, which took place in Dijon, France, during July 10-11, 2024.?..The 44 papers included in these proceedings were carefully reviewed and selected from a total of 70 submissions. They focus on topics such as deep learning and big data analytics; machine-learning and artificial int作者: Invertebrate 時(shí)間: 2025-3-28 16:56
1865-0929 ed and selected from a total of 70 submissions. They focus on topics such as deep learning and big data analytics; machine-learning and artificial intelligence, etc.?.978-3-031-66704-6978-3-031-66705-3Series ISSN 1865-0929 Series E-ISSN 1865-0937 作者: STYX 時(shí)間: 2025-3-28 20:37 作者: Recessive 時(shí)間: 2025-3-29 02:04
Grundlegung des Problemhorizontes,tional prediction layers to produce personality predictions. For model evaluation, new metrics are adopted to assess the accuracy of the model at various levels of error tolerance. Additionally, a comparison of the Mean Squared Error (MSE) with the previous best results is provided.作者: chuckle 時(shí)間: 2025-3-29 04:24
,Version 8 of?YOLO for?Wildfire Detection,e and restoration costs. Convolutional neural networks (CNNs) is currently enjoying the best accuracy among other methods (e.g. feature modeling) for wildfire detection from images. This paper applies version 8 of YOLO to reduce computational costs while maintaining the high detection capability.作者: FUME 時(shí)間: 2025-3-29 08:07 作者: Ganglion 時(shí)間: 2025-3-29 14:38 作者: Epithelium 時(shí)間: 2025-3-29 16:35
,Brains Over?Brawn: Small AI Labs in?the?Age of?Datacenter-Scale Compute,s approach not only aligns with the imperative to make AI research more sustainable and inclusive but also leverages the brain’s proven strategies for efficient computation. We posit that there exists a middle ground between the brain and datacenter-scale models that eschews the need for excessive c作者: Prostaglandins 時(shí)間: 2025-3-29 22:45
,Bayes Classification Using an?Approximation to?the?Joint Probability Distribution of?the?Attributesich means our approach (unlike the Gaussian and Laplace approaches) takes into consideration dependencies among the attribute values. We illustrate the performance of the proposed approach on a wide range of datasets taken from the University of California at Irvine (UCI) Machine Learning Repository作者: agonist 時(shí)間: 2025-3-29 23:58 作者: Colonnade 時(shí)間: 2025-3-30 04:08 作者: 吞吞吐吐 時(shí)間: 2025-3-30 11:36 作者: 奇思怪想 時(shí)間: 2025-3-30 13:43 作者: 津貼 時(shí)間: 2025-3-30 19:17 作者: 集中營(yíng) 時(shí)間: 2025-3-31 00:06
,Skin Cancer Classification: A Comparison of?CNN-Backbones for?Feature-Extraction,ular difficulty in distinguishing between the three classes, likely due to the inherent complexities associated with these categories. Despite these challenges, the EfficientNet model remains the most balanced among all those evaluated.作者: inundate 時(shí)間: 2025-3-31 02:48
Multilingual Detection of Cyberbullying on Social Networks Using a Fine-Tuned GPT-3.5 Model,l to specialized solutions such as Perspective API, Moderation, Content Safety, Toxic Bert, and Gemini. The results demonstrate that our approach outperforms existing models in metrics such as precision, f1-score, and accuracy, making it the most suitable choice for effective cyberbullying detection作者: 定點(diǎn) 時(shí)間: 2025-3-31 05:04
,Str?mungen mit mehreren Phasen,lutional neural network (CNN) model in parallel as sub-modules alongside the geometrical realization model. To assess the efficacy of the proposed model, we conduct evaluations on diverse time series datasets spanning various domains, including electricity load demands and M4 competition datasets. F作者: 極小量 時(shí)間: 2025-3-31 12:28 作者: addition 時(shí)間: 2025-3-31 16:08 作者: 最低點(diǎn) 時(shí)間: 2025-3-31 19:59 作者: 巨大沒有 時(shí)間: 2025-3-31 22:11 作者: tendinitis 時(shí)間: 2025-4-1 02:34 作者: macrophage 時(shí)間: 2025-4-1 10:01 作者: 結(jié)合 時(shí)間: 2025-4-1 14:03
,Auswertung zu den ?Wissensfragmenten“,ractive and abstractive summarizers both gain significant performance boosts. It is conjectured that the new semantic similarity-induced cross-entropy loss combined with the parallel training will improve any combination of quality extractive and abstractive summarizers.
