標(biāo)題: Titlebook: Machine Learning and Knowledge Discovery in Databases: Research Track; European Conference, Danai Koutra,Claudia Plant,Francesco Bonchi Con [打印本頁] 作者: 債務(wù)人 時間: 2025-3-21 17:45
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書目名稱Machine Learning and Knowledge Discovery in Databases: Research Track被引頻次
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書目名稱Machine Learning and Knowledge Discovery in Databases: Research Track讀者反饋
書目名稱Machine Learning and Knowledge Discovery in Databases: Research Track讀者反饋學(xué)科排名
作者: Venules 時間: 2025-3-21 22:03 作者: 野蠻 時間: 2025-3-22 01:57 作者: 看法等 時間: 2025-3-22 07:15
Machine Learning and Knowledge Discovery in Databases: Research Track978-3-031-43415-0Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: MAZE 時間: 2025-3-22 11:16 作者: faculty 時間: 2025-3-22 15:42
Scoring Rule Nets: Beyond Mean Target Prediction in?Multivariate Regressioncorrelation. We then show in a variety of experiments on both synthetic and real data, that Conditional CRPS often outperforms MLE, and produces results comparable to state-of-the-art non-parametric models, such as Distributional Random Forest (DRF).作者: 鞠躬 時間: 2025-3-22 19:50 作者: ironic 時間: 2025-3-22 22:43 作者: 起波瀾 時間: 2025-3-23 03:28 作者: 好忠告人 時間: 2025-3-23 06:36
Rényi Divergence Deep Mutual Learningll reach nearby local optima but continue searching within a bounded scope, which may help mitigate overfitting. Finally, our extensive empirical results demonstrate the advantage of combining DML and the?Rényi divergence, leading to further improvement in model generalization.作者: 舊石器時代 時間: 2025-3-23 12:43
Machine Learning and Knowledge Discovery in Databases: Research TrackEuropean Conference,作者: 勾引 時間: 2025-3-23 15:23 作者: lipids 時間: 2025-3-23 18:23
matopoietic stem cells, tissue development, circadian rhythm, heat shock or DNA damage response, and sex determination. Thus, it is not surprising that dysregulation of the m.A machinery is also closely associated with pathogenesis and drug response of both solid tumors and hematologic malignancies.作者: strdulate 時間: 2025-3-24 00:50
Wenkai Chen,Chuang Zhu,Mengting Lits in complete clinical remission, commonly called “minimal residual disease” (MRD), has been demonstrated clearly. High-dose therapy approaches with stem cell support are attractive mechanisms to attempt to eradicate residual lymphoma cells. In addition, it has been shown that quantitative detectio作者: Mast-Cell 時間: 2025-3-24 06:00
Alexander Rakowski,Christoph Lippertherapy and relapses resulting from insufficiently intensive consolidation treatment in patients at high risk of relapse because of persistence of residual leukemic cells. In addition, such sensitive methods for MRD detection can contribute to the assessment of the efficacy of ex vivo purging protoco作者: 向前變橢圓 時間: 2025-3-24 09:08 作者: 斗爭 時間: 2025-3-24 11:00
Zichong Wang,Charles Wallace,Albert Bifet,Xin Yao,Wenbin Zhangal disease by sensitive techniques does not necessarily predict relapse. There are instances in the literature both of ‘clonal’ remissions [2] and stable persistence of residual disease during remission [3]. The large body of work on the influence of immune effector mechanisms in the control or elim作者: Senescent 時間: 2025-3-24 18:19
0302-9743 Learning..Part VI:.??Applied Machine Learning; Computational Social Sciences; Finance; Hardware and Systems; Healthcare & Bioinformatics; Human-Computer Intera978-3-031-43414-3978-3-031-43415-0Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 冰雹 時間: 2025-3-24 23:04
Haiyan Zhao,Tianyi Zhou,Guodong Long,Jing Jiang,Chengqi Zhang作者: 加入 時間: 2025-3-25 01:07
Shuxian Li,Liyan Song,Xiaoyu Wu,Zheng Hu,Yiu-ming Cheung,Xin Yao作者: Prognosis 時間: 2025-3-25 06:52 作者: relieve 時間: 2025-3-25 11:13
Weipeng Fuzzy Huang,Junjie Tao,Changbo Deng,Ming Fan,Wenqiang Wan,Qi Xiong,Guangyuan Piao作者: 睨視 時間: 2025-3-25 13:10 作者: transdermal 時間: 2025-3-25 19:41
Firas Laakom,Jenni Raitoharju,Alexandros Iosifidis,Moncef Gabbouj作者: 證實 時間: 2025-3-25 22:32
Srinivas Anumasa,Geetakrishnasai Gunapati,P. K. Srijith作者: prolate 時間: 2025-3-26 03:20 作者: Ingrained 時間: 2025-3-26 06:15
DCID: Deep Canonical Information Decompositionquantifying 3 aspects of the learned shared features. We further propose Deep Canonical Information Decomposition (DCID) - a simple, yet effective approach for learning the shared variables. We benchmark the models on a range of scenarios on synthetic data with known ground-truths and observe DCID o作者: Arthritis 時間: 2025-3-26 10:41 作者: AWRY 時間: 2025-3-26 15:20 作者: 嬉耍 時間: 2025-3-26 17:09
Make a?Long Image Short: Adaptive Token Length for?Vision Transformersngth Assigner (TLA) that allocates the optimal token length for each image during inference. The TLA enables ReViT to process images with the minimum sufficient number of tokens, reducing token numbers in the ViT model and improving inference speed. Our approach is general and compatible with modern作者: 時代錯誤 時間: 2025-3-27 00:44 作者: anaerobic 時間: 2025-3-27 04:01 作者: 思鄉(xiāng)病 時間: 2025-3-27 05:22 作者: Irrepressible 時間: 2025-3-27 10:38 作者: bonnet 時間: 2025-3-27 15:21
Continuous Depth Recurrent Neural Differential Equationsations over both depth and time to predict an output for a given input in the sequence. Specifically, we propose continuous depth recurrent neural differential equations (CDR-NDE) which generalize RNN models by continuously evolving the hidden states in both the temporal and depth dimensions. CDR-ND作者: Pathogen 時間: 2025-3-27 20:20
Mitigating Algorithmic Bias with?Limited Annotationsand it is theoretically proved to be capable of bounding the algorithmic bias. According to the evaluation on five benchmark datasets, APOD outperforms the state-of-the-arts baseline methods under the limited annotation budget, and shows comparable performance to fully annotated bias mitigation, whi作者: Alopecia-Areata 時間: 2025-3-27 22:19 作者: Infant 時間: 2025-3-28 05:16 作者: 修改 時間: 2025-3-28 06:44
Sample Prior Guided Robust Model Learning to?Suppress Noisy Labelsabels have two key steps: 1) dividing samples into cleanly labeled and wrongly labeled sets by training loss, 2) using semi-supervised methods to generate pseudo-labels for samples in the wrongly labeled set. However, current methods always hurt the informative hard samples due to the similar loss d作者: SAGE 時間: 2025-3-28 11:04
DCID: Deep Canonical Information Decompositionons. Canonical Correlation Analysis (CCA)-based methods have traditionally been used to identify shared variables, however, they were designed for multivariate targets and only offer trivial solutions for univariate cases. In the context of Multi-Task Learning (MTL), various models were postulated t作者: 變白 時間: 2025-3-28 17:16 作者: 四目在模仿 時間: 2025-3-28 22:36 作者: 宣誓書 時間: 2025-3-29 01:56 作者: 極端的正確性 時間: 2025-3-29 06:27
Graph Rebasing and?Joint Similarity Reconstruction for?Cross-Modal Hash Retrievallarity is insufficient, and the large gap between modalities leads to semantic bias. In this paper, we propose a Graph Rebasing and Joint Similarity Reconstruction (GRJSR) method for cross-modal hash retrieval. Particularly, the graph rebasing module is used to filter out graph nodes with weak simil作者: modest 時間: 2025-3-29 08:33
ARConvL: Adaptive Region-Based Convolutional Learning for?Multi-class Imbalance Classificationity classes. A typical way to address such problem is to adjust the loss function of deep networks by making use of class imbalance ratios. However, such static between-class imbalance ratios cannot monitor the changing latent feature distributions that are continuously learned by the deep network t作者: 嚙齒動物 時間: 2025-3-29 14:58 作者: Extricate 時間: 2025-3-29 17:31 作者: Fissure 時間: 2025-3-29 23:17
Rényi Divergence Deep Mutual Learningeibler divergence, which is more flexible and tunable, to improve vanilla DML. This modification is able to consistently improve performance over vanilla DML with limited additional complexity. The convergence properties of the proposed paradigm are analyzed theoretically, and Stochastic Gradient De作者: 來就得意 時間: 2025-3-30 02:19 作者: Crayon 時間: 2025-3-30 06:34
Scoring Rule Nets: Beyond Mean Target Prediction in?Multivariate Regressionis mostly problematic in the multivariate domain. While univariate models often optimize the popular Continuous Ranked Probability Score (CRPS), in the multivariate domain, no such alternative to MLE has yet been widely accepted. The Energy Score – the most investigated alternative – notoriously lac作者: agitate 時間: 2025-3-30 10:21
Learning Distinct Features Helps, Provablyage .-distance between the hidden-layer features and theoretically investigate how learning non-redundant distinct features affects the performance of the network. To do so, we derive novel generalization bounds depending on feature diversity based on Rademacher complexity for such networks. Our ana作者: ingenue 時間: 2025-3-30 14:08 作者: Fluctuate 時間: 2025-3-30 20:02 作者: objection 時間: 2025-3-30 21:45
: Fairness-Aware Graph Generative Adversarial Networksenhancing performance, with the issue of fairness remaining largely unexplored. Existing graph generation models prioritize minimizing graph reconstruction’s expected loss, which can result in representational disparities in the generated graphs that unfairly impact marginalized groups. This paper a作者: 有助于 時間: 2025-3-31 01:19
spite of our increasingly detailed knowledge of the molecular alterations occurring in human tumors. In parallel, despite a growing number of clinical trials being conducted, the absolute number of drugs that are effective in humans is declining, and many new drugs move into the market without havi作者: 我不死扛 時間: 2025-3-31 07:07 作者: cancellous-bone 時間: 2025-3-31 11:02 作者: MEN 時間: 2025-3-31 13:20 作者: 猛擊 時間: 2025-3-31 20:50
