標(biāo)題: Titlebook: Neural Information Processing; 30th International C Biao Luo,Long Cheng,Chaojie Li Conference proceedings 2024 The Editor(s) (if applicable [打印本頁(yè)] 作者: melancholy 時(shí)間: 2025-3-21 18:02
書(shū)目名稱Neural Information Processing影響因子(影響力)
書(shū)目名稱Neural Information Processing影響因子(影響力)學(xué)科排名
書(shū)目名稱Neural Information Processing網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱Neural Information Processing網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱Neural Information Processing被引頻次
書(shū)目名稱Neural Information Processing被引頻次學(xué)科排名
書(shū)目名稱Neural Information Processing年度引用
書(shū)目名稱Neural Information Processing年度引用學(xué)科排名
書(shū)目名稱Neural Information Processing讀者反饋
書(shū)目名稱Neural Information Processing讀者反饋學(xué)科排名
作者: violate 時(shí)間: 2025-3-21 23:57 作者: BANAL 時(shí)間: 2025-3-22 01:23 作者: condone 時(shí)間: 2025-3-22 08:06
https://doi.org/10.1007/978-981-99-8070-3pattern recognition; affective and cognitive learning; big data; bioinformatics; brain-machine interface作者: 青春期 時(shí)間: 2025-3-22 08:57 作者: SIT 時(shí)間: 2025-3-22 15:56 作者: 價(jià)值在貶值 時(shí)間: 2025-3-22 19:49
GRF-GMM: A Trajectory Optimization Framework for?Obstacle Avoidance in?Learning from?Demonstrationaussian mixture model/Gaussian mixture regression (GMM/GMR) has been widely used for its robustness and effectiveness. However, there still exist many problems of GMM when an obstacle, which is not presented in original demonstrations, appears in the workspace of robots. To address these problems, t作者: 聲明 時(shí)間: 2025-3-23 00:23 作者: 正常 時(shí)間: 2025-3-23 03:56
CrowdNav-HERO: Pedestrian Trajectory Prediction Based Crowded Navigation with?Human-Environment-Roboonmental layout usually significantly impacts crowd distribution and robotic motion decision-making during crowded navigation. However, previous methods almost either learn and evaluate navigation strategies in unrealistic barrier-free settings or assume that expensive features like pedestrian speed作者: 使人煩燥 時(shí)間: 2025-3-23 08:05
Modeling User’s Neutral Feedback in?Conversational Recommendationns. Although CRS has shown success in generating recommendation lists based on user’s preferences, existing methods restrict users to make binary responses, i.e., accept and reject, after recommending, which limits users from expressing their needs. In fact, the user’s rejection feedback may contain作者: Merited 時(shí)間: 2025-3-23 11:21 作者: interior 時(shí)間: 2025-3-23 15:30 作者: Project 時(shí)間: 2025-3-23 20:37 作者: Musculoskeletal 時(shí)間: 2025-3-24 00:02 作者: VERT 時(shí)間: 2025-3-24 05:53 作者: 無(wú)能力之人 時(shí)間: 2025-3-24 07:45 作者: 使害羞 時(shí)間: 2025-3-24 11:09
A Compliant Elbow Exoskeleton with?an?SEA at?Interaction Port proposes an SEA composed of wave springs and installs it at human-robot interaction port. Considering the hysteresis nonlinear characteristics of the SEA, displacement-force models of the SEA are established based on long short-term memory (LSTM) model and T-S fuzzy model in a nonlinear auto-regres作者: Expertise 時(shí)間: 2025-3-24 17:38 作者: 協(xié)定 時(shí)間: 2025-3-24 22:36 作者: 槍支 時(shí)間: 2025-3-25 02:33
On Efficient Federated Learning for?Aerial Remote Sensing Image Classification: A Filter Pruning App framework CALIM-FL, short for Cross-All-Layers Importance Measure pruning-based Federated Learning. In CALIM-FL, an efficient one-shot filter pruning mechanism is intertwined with the standard FL procedure. The model size is adapted during FL to reduce both communication and computation overhead at作者: jet-lag 時(shí)間: 2025-3-25 04:29
ASGNet: Adaptive Semantic Gate Networks for?Log-Based Anomaly Diagnosisagnose system failures. For anomaly diagnosis, existing methods generally use log event data extracted from historical logs to build diagnostic models. However, we find that existing methods do not make full use of two types of features, (1) statistical features: some inherent statistical features i作者: 宮殿般 時(shí)間: 2025-3-25 09:09
Propheter: Prophetic Teacher Guided Long-Tailed Distribution Learningications. To tackle the heavily-skewed dataset issue in long-tailed classification, prior efforts have sought to augment existing deep models with the elaborate class-balancing strategies, such as class rebalancing, data augmentation, and module improvement. Despite the encouraging performance, the 作者: 語(yǔ)言學(xué) 時(shí)間: 2025-3-25 13:28
Litao Zhang,Chunming Yang,Chunlin He,Hui Zhang von Suchoperationen zu minimieren. Aufgrund verschiedener Arten des Fehlverhaltens, so vor allem im Datenverwaltungssystem selbst, kann es zu Verst??en gegen die Sortierordnung in den gespeicherten Daten kommen. Falls die Suchalgorithmen keine geeigneten (redundanten) Ma?nahmen zur Fehlererkennung 作者: 沙文主義 時(shí)間: 2025-3-25 16:12 作者: frenzy 時(shí)間: 2025-3-25 22:36
Hui Zeng,Biwei Chen,Rongsong Yang,Chenggang Li,Anjie Pengnsolved issues in SIHFT.Includes supplementary material: .Software-Implemented Hardware Fault Tolerance addresses the innovative topic of software-implemented hardware fault tolerance (SIHFT), i.e., how to deal with faults affecting the hardware by only (or mainly) acting on the software...The first作者: 釋放 時(shí)間: 2025-3-26 04:12
Qipeng Song,Jingbo Cao,Yue Li,Xueru Gao,Chengzhi Shangguan,Linlin Liangnsolved issues in SIHFT.Includes supplementary material: .Software-Implemented Hardware Fault Tolerance addresses the innovative topic of software-implemented hardware fault tolerance (SIHFT), i.e., how to deal with faults affecting the hardware by only (or mainly) acting on the software...The first作者: Acetaldehyde 時(shí)間: 2025-3-26 08:02 作者: transdermal 時(shí)間: 2025-3-26 11:59
Wenxiang Xu,Yongcheng Jing,Linyun Zhou,Wenqi Huang,Lechao Cheng,Zunlei Feng,Mingli Songnsolved issues in SIHFT.Includes supplementary material: .Software-Implemented Hardware Fault Tolerance addresses the innovative topic of software-implemented hardware fault tolerance (SIHFT), i.e., how to deal with faults affecting the hardware by only (or mainly) acting on the software...The first作者: Inertia 時(shí)間: 2025-3-26 16:43 作者: 割讓 時(shí)間: 2025-3-26 20:14 作者: negligence 時(shí)間: 2025-3-26 22:54
SLG-NET: Subgraph Neural Network with?Local-Global Braingraph Feature Extraction Modules and?a?Novell subgraph generation algorithm based on sub-structure information of brain. To improve feature extraction capabilities, a local and global braingraph feature extraction modules are proposed to extract braingraph properties at both local and global levels. Comprehensive experiments performed on rest作者: 傾聽(tīng) 時(shí)間: 2025-3-27 02:09
CrowdNav-HERO: Pedestrian Trajectory Prediction Based Crowded Navigation with?Human-Environment-Robo of pedestrians, and a series of realistic environments is customized to train and evaluate crowded navigation strategies. . Then, a pedestrian trajectory prediction module is introduced to eliminate the dependence of navigation strategies on pedestrian speed features. . Finally, a novel crowded nav作者: 預(yù)防注射 時(shí)間: 2025-3-27 09:08
Modeling User’s Neutral Feedback in?Conversational Recommendationral Feedback in Conversational Recommendation (NFCR). We adopt a joint learning task framework for feature extraction and use inverse reinforcement learning to train the decision network, helping CRS make appropriate decisions at each turn. Finally, we utilize the fine-grained neutral feedback from 作者: 易發(fā)怒 時(shí)間: 2025-3-27 13:16 作者: promote 時(shí)間: 2025-3-27 17:06
BIN: A Bio-Signature Identification Network for?Interpretable Liver Cancer Microvascular Invasion Prive) by utilizing Non-MVI and MVI biosignatures. The adoption of a transparent decision-making process in BIN ensures interpretability, while the proposed biosignatures overcome the limitations associated with the manual feature extraction. Moreover, a multi-modal MRI based BIN method is also explor作者: cruise 時(shí)間: 2025-3-27 19:18
Human-to-Human Interaction Detection merging stage which reconstructs the relationship between instances and groups. All SaMFormer components are jointly trained in an end-to-end manner. Extensive experiments on AVA-I validate the superiority of SaMFormer over representative methods.作者: Calculus 時(shí)間: 2025-3-27 22:16
ASGNet: Adaptive Semantic Gate Networks for?Log-Based Anomaly Diagnosisance of log anomaly diagnosis is the key point of this paper. In this paper, we propose an adaptive semantic gate networks (ASGNet) that combines statistical features and semantic features to selectively use statistical features to consolidate log text semantic representation. Specifically, ASGNet e作者: Anticlimax 時(shí)間: 2025-3-28 04:55
Propheter: Prophetic Teacher Guided Long-Tailed Distribution Learningions. The target long-tailed prediction model is then optimized under the instruction of the well-trained “Propheter”, such that the distributions of different classes are as distinguishable as possible from each other. Experiments on eight long-tailed benchmarks across three architectures demonstra作者: Hormones 時(shí)間: 2025-3-28 08:44
Bin Ye,Peng Yu,Cong Hu,Binbin Qiu,Ning Tannt es wichtig, den Stand der Technik in Theorie und Praxis zu erfassen und eine Bestandsaufnahme von laufenden Aktivitaten zu versuchen. Dieser Band gibt einen 978-3-540-13383-4978-3-642-69705-0Series ISSN 0343-3005 作者: visceral-fat 時(shí)間: 2025-3-28 13:27 作者: yohimbine 時(shí)間: 2025-3-28 17:55 作者: Hippocampus 時(shí)間: 2025-3-28 18:45 作者: 現(xiàn)代 時(shí)間: 2025-3-29 02:43
Wenxiang Xu,Yongcheng Jing,Linyun Zhou,Wenqi Huang,Lechao Cheng,Zunlei Feng,Mingli Songware fault tolerance, as well as the practical aspects related to put it at work on real examples. By evaluating accurately the advantages and disadvantages of the already available approaches, the book?prov978-1-4419-3861-9978-0-387-32937-6作者: 排他 時(shí)間: 2025-3-29 04:42
Siyi Lu,Bolei Chen,Ping Zhong,Yu Sheng,Yongzheng Cui,Run Liu作者: EVADE 時(shí)間: 2025-3-29 07:49
Xizhe Li,Chenhao Hu,Weiyang Kong,Sen Zhang,Yubao Liu作者: 節(jié)省 時(shí)間: 2025-3-29 11:45
Siqi Ma,Zhe Liu,Yuqing Song,Yi Liu,Kai Han,Yang Jiang作者: 誘惑 時(shí)間: 2025-3-29 17:02
Sai Siddhartha Vivek Dhir Rangoju,Keshav Garg,Rohith Dandi,Om Prakash Patel,Neha Bharill作者: Eclampsia 時(shí)間: 2025-3-29 20:33 作者: JOT 時(shí)間: 2025-3-30 02:18 作者: Palliation 時(shí)間: 2025-3-30 06:26 作者: CHAFE 時(shí)間: 2025-3-30 10:36 作者: HEAVY 時(shí)間: 2025-3-30 16:28 作者: 劇毒 時(shí)間: 2025-3-30 16:55
Differential Fault Analysis Against AES Based on a Hybrid Fault Modelund, thus it is easier to carry out to an attacker. When considering AES-192, fewer faulty ciphertexts are needed. In addition, for both AES-192 and 256, our method requires fewer depths of induced fault (the entire key can be retrieved only need to induce fault in the T-2 round). Thus, the DFA proposed in this article is more efficient.作者: legislate 時(shí)間: 2025-3-30 22:03
Towards Undetectable Adversarial Examples: A Steganographic Perspectivehe proposed scheme is compatible with various existing attacks and can significantly boost the undetectability of adversarial examples against both human inspection and statistical analysis of the same attack ability. The code is available at ..作者: NEG 時(shí)間: 2025-3-31 01:27
On Efficient Federated Learning for?Aerial Remote Sensing Image Classification: A Filter Pruning Appfrom the perspective of the whole neural networks; and 2) a communication-efficient one-shot pruning mechanism without data transmission from the devices. Comprehensive experiment results show that CALIM-FL is effective in a variety of scenarios, with a resource overhead saving of 88.4% at the cost of . accuracy loss.作者: foppish 時(shí)間: 2025-3-31 08:49 作者: ARBOR 時(shí)間: 2025-3-31 09:53 作者: 泄露 時(shí)間: 2025-3-31 17:26 作者: lobster 時(shí)間: 2025-3-31 17:38
