作者: Gene408 時間: 2025-3-21 22:39
: Capturing Different Behaviors in?Multiplex Heterogeneous Networks for?Recommendationerogeneous network has multiple types of nodes and edges (or relations). Multiplex heterogeneous network embedding aims to learn from abundant structural and semantic information of a graph and embed nodes into low-dimensional representations. Existing works usually split the graph into several rela作者: 空洞 時間: 2025-3-22 01:05 作者: Infiltrate 時間: 2025-3-22 05:08
Increasing Oversampling Diversity for Long-Tailed Visual Recognitione of the commonly used techniques to tackle this problem. In this paper, we first analyze that the commonly used oversampling technique tends to distort the representation learning and harm the network’s generalizability. Then we propose two novel methods to increase the minority feature’s diversity作者: homocysteine 時間: 2025-3-22 09:34
Odds Estimating with?Opponent Hand Belief for Texas Hold’em Poker Agentsate indefectible AI transfers to develop new AIs which can exploit opponents and explain its own decisions better. Hand odds estimating used to state abstracting, situation evaluating, decision assisting is one of the key foundations for such new agents. But all the current methods implicitly assume作者: 凝乳 時間: 2025-3-22 14:20
Remote Sensing Image Recommendation Using Multi-attribute Embedding and Fusion Collaborative Filterily. Introducing recommendation systems into the distribution of remote sensing images can lower the threshold to obtain images for the public and subvert the traditional image service pattern. Different from other industries, users in the remote sensing field do not purchase data frequently in most 作者: CLASH 時間: 2025-3-22 19:08 作者: MELON 時間: 2025-3-22 21:37 作者: affluent 時間: 2025-3-23 03:25 作者: harrow 時間: 2025-3-23 09:23 作者: insincerity 時間: 2025-3-23 13:01
Diagnosis of?Childhood Autism Using Multi-modal Functional Connectivity via?Dynamic Hypergraph LearnASD). In recent years, some studies have hypothesized the stationary assumption and revealed the relevance of the time-varying anomaly in FC to the autistic traits. While most existing work focus on exploring properties of static FC (sFC) and dynamic FC (dFC) separately, little efforts have been mad作者: 調(diào)整校對 時間: 2025-3-23 14:47 作者: compose 時間: 2025-3-23 19:36
White-Box Attacks on the CNN-Based Myoelectric Control Systemcomputer interaction. Nevertheless, it was found that CNN models are very easily tricked by adversarial instances, which are normal instances with tiny intentional perturbations. In this study, an attack framework based on universal adversarial perturbations (UAP) was proposed to attack the CNN-base作者: Intersect 時間: 2025-3-24 02:04 作者: FICE 時間: 2025-3-24 04:21 作者: Crayon 時間: 2025-3-24 08:06 作者: Obedient 時間: 2025-3-24 14:04 作者: exclamation 時間: 2025-3-24 18:02
Neighborhood Search Acceleration Based on Deep Reinforcement Learning for SSCFLPitated Facility Location Problem (SSCFLP). Specifically, we construct a deep reinforcement learning model which learns a disturbing strategy to iteratively select the customers to be adjusted, and design a neighborhood operator to generate a new solution by reassigning the selected customers. The pr作者: cardiovascular 時間: 2025-3-24 20:27
Darl Kuhn,Sam R. Alapati,Bill Padfield, we propose Gradient Re-weighting Module (GRM) to re-distribute each instance’s gradient contribution to the representation learning network. Extensive experiments on the long-tailed benchmark CIFAR10-LT, CIFAR100-LT and ImageNet-LT demonstrate the effectiveness of our proposed method.作者: 要素 時間: 2025-3-25 02:49
Increasing Oversampling Diversity for Long-Tailed Visual Recognition, we propose Gradient Re-weighting Module (GRM) to re-distribute each instance’s gradient contribution to the representation learning network. Extensive experiments on the long-tailed benchmark CIFAR10-LT, CIFAR100-LT and ImageNet-LT demonstrate the effectiveness of our proposed method.作者: accrete 時間: 2025-3-25 05:00 作者: CRATE 時間: 2025-3-25 07:31 作者: 制定 時間: 2025-3-25 12:30 作者: licence 時間: 2025-3-25 15:54
0302-9743 ence, held in?Hangzhou, China, in June 2021. Due to the COVID-19 pandemic the conference was partially held online.?.The 105 papers were thoroughly reviewed and selected from 307 qualified submissions. The papers are organized in topical sections on applications of AI; computer vision; data mining; 作者: Bronchial-Tubes 時間: 2025-3-25 22:26 作者: prostate-gland 時間: 2025-3-26 00:47
: Capturing Different Behaviors in?Multiplex Heterogeneous Networks for?Recommendation aggregation (MetAware), where MetAware aggregates information from different metapaths in each relation-specific subgraph and RsAtt combines and integrates the information with attentive weights. The experiments are conducted on three real-world datasets, and the experimental results show that our 作者: Notorious 時間: 2025-3-26 04:51 作者: 定點 時間: 2025-3-26 11:12 作者: 碌碌之人 時間: 2025-3-26 14:01
Remote Sensing Image Recommendation Using Multi-attribute Embedding and Fusion Collaborative Filtericess via a knowledge graph. Next, we extract multidimensional features from the knowledge graphs of users and images with the help of knowledge representation learning and quantifiable features. Finally, using the high-dimensional spatial modeling capabilities of deep neural networks to perform a de作者: 愛社交 時間: 2025-3-26 20:16 作者: PANT 時間: 2025-3-26 22:48 作者: G-spot 時間: 2025-3-27 02:29
Selected Sample Retraining Semi-supervised Learning Method for?Aerial Scene Classification data set through the high probability sample selection method. Finally, the two segmented data sets are combined with the labeled data sets to train a scene classifier based on the semi-supervised learning method. To verify the effectiveness of the proposed method, it is further compared with sever作者: Blasphemy 時間: 2025-3-27 07:37 作者: archenemy 時間: 2025-3-27 09:42
Diagnosis of?Childhood Autism Using Multi-modal Functional Connectivity via?Dynamic Hypergraph Learnplex correlation among different subjects under static and dynamic modalities, respectively. To further moderate inappropriate or even wrong connections, a multi-modal dynamic hypergraph learning process is conducted to jointly learn the data correlation and predict the subject labels, i.e., HC or A作者: galley 時間: 2025-3-27 17:19 作者: 終點 時間: 2025-3-27 19:48 作者: 6Applepolish 時間: 2025-3-27 22:48
MMG-HCI: A Non-contact Non-intrusive Real-Time Intelligent Human-Computer Interaction Systemnstruction of millimeter-wave radar gesture dataset. Since there is no suitable open-source dataset at present, we collect and build our own dataset through the real-time system. 3, Training graph neural network (GNN) for HAR based on millimeter-wave radar. In order to classify the mmWave radar poin作者: 審問,審訊 時間: 2025-3-28 05:18
DSGSR: Dynamic Semantic Generation and Similarity Reasoning for Image-Text Matchinghbor node information on matching accuracy when measuring the image-text similarity. A large number of experiments and analyses show that the DSGSR model surpass state-of-the-art methods on Flickr30K and MSCOCO datasets.作者: Abnormal 時間: 2025-3-28 07:45 作者: 解決 時間: 2025-3-28 12:11 作者: Interstellar 時間: 2025-3-28 15:34 作者: 鋼筆尖 時間: 2025-3-28 20:24 作者: 重畫只能放棄 時間: 2025-3-29 02:34 作者: 壟斷 時間: 2025-3-29 03:06 作者: circumvent 時間: 2025-3-29 08:12
edict the opponent’s hand range based on the opponent model and observed action not fold, their additional computation complexity is .(1). The expected win rate algorithm with opponent hand distribution (EWR-HD) is the third method suitable for all rounds which uses the opponent model and observed a作者: 委屈 時間: 2025-3-29 13:46 作者: Nomogram 時間: 2025-3-29 18:52
Philip Bonanno,Abigail Colson,Simon Frenchderation and is thus less conservative and more robust in regards to random scenes. An additional analysis indicates that the proposed SSR performs well on classical metrics. The effectiveness of the proposed SSR model is demonstrated comparing with state-of-the-art methods in unknown scenes.作者: Commodious 時間: 2025-3-29 22:13 作者: 史前 時間: 2025-3-30 02:04
Philip Bonanno,Abigail Colson,Simon French data set through the high probability sample selection method. Finally, the two segmented data sets are combined with the labeled data sets to train a scene classifier based on the semi-supervised learning method. To verify the effectiveness of the proposed method, it is further compared with sever作者: Ornament 時間: 2025-3-30 07:18
Philip Bonanno,Abigail Colson,Simon Frenchof entities with the guide of texts, and vice versa. Finally, we introduce the graph convolutional network further to enhance the fusion representation of entities and texts. Extensive experiments on large-scale patent data demonstrate the superior performance of our model on the patent classificati作者: AWRY 時間: 2025-3-30 09:41
Thomas R. Stewart,Claude McMillan Jr.plex correlation among different subjects under static and dynamic modalities, respectively. To further moderate inappropriate or even wrong connections, a multi-modal dynamic hypergraph learning process is conducted to jointly learn the data correlation and predict the subject labels, i.e., HC or A作者: 從屬 時間: 2025-3-30 13:43 作者: 去才蔑視 時間: 2025-3-30 19:30 作者: 角斗士 時間: 2025-3-30 21:18 作者: 圓桶 時間: 2025-3-31 04:05 作者: 轎車 時間: 2025-3-31 08:19
Retailing the Female Intellectual,he filter-based feature selection method is designed to remove the irrelevant and redundant features. Based on the selected feature subset, a regression model is trained to predict the MRR. The effectiveness of the proposed method is evaluated on a challenge dataset.作者: compassion 時間: 2025-3-31 09:42 作者: 民間傳說 時間: 2025-3-31 17:06 作者: 領(lǐng)巾 時間: 2025-3-31 18:10 作者: 翅膀拍動 時間: 2025-3-31 22:51
