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mian, 陈福黎|这一碗酸爽可口的冬阴功面,让你“咝”~啊~~

作者:   来源:  热度:31  时间:2021-03-30






天气转暖,阳光正好不断接收到来自春天的信号味蕾也开始准备换季这时候就该来碗酸.辣.咸.香.甜.五味俱全的泰式冬阴功汤面妥妥满足你的换季好胃口今日推荐冬阴功海鲜面

天气转暖,阳光正好

不断接收到来自春天的信号

味蕾也开始准备换季

这时候就该来碗

酸.辣.咸.香.甜.五味俱全的

泰式冬阴功汤面

妥妥满足你的换季好胃口

冬阴功海鲜面

Dong Yin Gong Hai Xian Mian

这碗精华之作,诚意十足,表面铺满了各种食材,已经看不见底层的面条了!与冬阴功汤底完美结合,直接将鲜美度提升几个level!

其中的大虾,鲜到无法自拔了,融入了东南亚风酸酸甜甜的味道,舌尖辣辣的刺激感,爽!

冬阴功肥牛面

Dong Yin Gong Fei Niu Mian

肥瘦相间的肥牛,一层油脂一层瘦肉,吸收了满满汤汁,鲜嫩可口!

冬阴功汤酸辣开胃,喝上一口食欲全开!根本停不下来!

冬阴功卜卜贝面

Dong Yin Gong Bo Bo Bei Mian

粒粒都是肉厚肥美的A+级白贝,泡在汤汁里,一开始滚就发出“啵啵”声,像是在唱歌。

吃卜卜贝前一定要先来一碗汤。调配好的汤底,有着酸甜辣三种味道,口水都要流了~

肥美鲜甜的白贝,吸收了酸甜辣的冬阴功汤汁,把贝壳的汤汁嗦干净。咬一口卜卜贝溅出酸甜辣汁,这个味道让人一吃就爱上!

没什么是一碗面解决不了的!

如果有就两碗

酸辣刺激的冬阴功面

正等你来品尝!

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cs.CV 方向,今日共计52篇

[检测分类相关]:【1】 Sewer-ML: A Multi-Label Sewer Defect Classification Dataset and  Benchmark标题:SEWER-ML:一种多标签下水道缺陷分类数据集和基准作者:Joakim Bruslund Haurum,Thomas B. Moeslund机构:Visual Analysis and Perception (VAP) Laboratory, Aalborg University, Denmark备注:CVPR 2021. Project webpage: this https URL链接:https://arxiv.org/abs/2103.10895【2】 CE-FPN: Enhancing Channel Information for Object Detection标题:CE-FPN:增强目标检测的信道信息作者:Yihao Luo,Xiang Cao,Juntao Zhang,Xiang Cao,Jingjuan Guo,Haibo Shen,Tianjiang Wang,Qi Feng备注:9pages链接:https://arxiv.org/abs/2103.10643【3】 Training image classifiers using Semi-Weak Label Data标题:使用半弱标签数据训练图像分类器作者:Anxiang Zhang,Ankit Shah,Bhiksha Raj机构:Carnegie Mellon University Carnegie Mellon University Carnegie Mellon University备注:First two authors contributed equally链接:https://arxiv.org/abs/2103.10608【4】 PSCC-Net: Progressive Spatio-Channel Correlation Network for Image  Manipulation Detection and Localization标题:PSCC-NET:用于图像处理检测和定位的渐进式空间信道相关网络作者:Xiaohong Liu,Yaojie Liu,Jun Chen,Xiaoming Liu机构:McMaster University ,Michigan State University备注:11 pages, 6 figures链接:https://arxiv.org/abs/2103.10596【5】 Variational Knowledge Distillation for Disease Classification in Chest  X-Rays标题:用于胸部X线片疾病分类的变分知识提炼作者:Tom van Sonsbeek,Xiantong Zhen,Marcel Worring,Ling Shao机构:University of Amsterdam, The Netherlands,  Inception Institute of Artificial Intelligence, U.A.E链接:https://arxiv.org/abs/2103.10825【6】 Cluster-to-Conquer: A Framework for End-to-End Multi-Instance Learning  for Whole Slide Image Classification标题:整幅幻灯片图像分类的端到端多实例学习框架--聚类到征服(Cluster-to-Conquer)作者:Yash Sharma,Aman Shrivastava,Lubaina Ehsan,Christopher A. Moskaluk,Sana Syed,Donald E. Brown机构:CAM,PQVIRGINIA.EDU,  University of Virginia, Charlottesville, Virginia, USA, Editors: Under Review for MIDL备注:Submitted to MIDL, 2021 - this https URL链接:https://arxiv.org/abs/2103.10626[分割/语义相关]:【1】 ClawCraneNet: Leveraging Object-level Relation for Text-based Video  Segmentation标题:ClawCraneNet:利用对象级关系进行基于文本的视频分割作者:Chen Liang,Yu Wu,Yawei Luo,Yi Yang机构:Zhejiang University Baidu Research University of Technology Sydney链接:https://arxiv.org/abs/2103.10702【2】 Improving Image co-segmentation via Deep Metric Learning标题:基于深度度量学习的图像共分割改进作者:Zhengwen Li,Xiabi Liu机构:Beijing Polytechnic University备注:11 pages, 5 figures链接:https://arxiv.org/abs/2103.10670【3】 Neural Networks for Semantic Gaze Analysis in XR Settings标题:XR环境下语义凝视分析的神经网络方法作者:Lena Stubbemann,Dominik Dürrschnabel,Robert Refflinghaus机构:Virtual-reality(VR)and augmented-reality (AR)technology is increasingly combined with eye-tracking. This combination broadens, both fields and opens up new areas of application, in which visual perception and related cognitive processes can be studied in, interactive but still well controlled settings. However, performing a semantic gaze analysis of eye-tracking data from interactive, three-dimensional scenes is a resource-intense task, which so far has been an obstacle to economic use. In this paper we present a, novel approach which minimizes time and information necessary to annotate volumes of interest(VOIs) by using techniques from, object recognition. To do so, we train convolutional neural networks(CNNs)on synthetic data sets derived from virtual models using, image augmentation techniques. We evaluate our method in real and virtual environments, showing that the method can compete with备注:16 pages, 6 figures, 1 table, Accepted to: ETRA2021, ACM Symposium on Eye Tracking Research and Applications链接:https://arxiv.org/abs/2103.10451【4】 Deep Label Fusion: A 3D End-to-End Hybrid Multi-Atlas Segmentation and  Deep Learning Pipeline标题:深度标签融合:一种端到端的三维混合多地图集分割和深度学习流水线作者:Long Xie,Laura E. M. Wisse,Jiancong Wang,Sadhana Ravikumar,Trevor Glenn,Anica Luther,Sydney Lim,David A. Wolk,Paul A. Yushkevich机构:Penn Image Computing and Science Laboratory(CSL), Univer-, Lund University, Lund, Sweden,  Penn Memory Center, University of Pennsylvania, Philadelphia, USA备注:12 pages paper accepted by the international conference of Information Processing in Medical Imaging (IPMI) 2021链接:https://arxiv.org/abs/2103.10892【5】 UNETR: Transformers for 3D Medical Image Segmentation标题:UNETR:用于三维医学图像分割的转换器作者:Ali Hatamizadeh,Dong Yang,Holger Roth,Daguang Xu机构:NVIDIA, Santa Clara, CA, USA备注:11 pages, 2 figures链接:https://arxiv.org/abs/2103.10504[人脸相关]:【1】 Recent Advances in Deep Learning Techniques for Face Recognition标题:深度学习技术在人脸识别中的研究进展作者:Md. Tahmid Hasan Fuad,Awal Ahmed Fime,Delowar Sikder,Md. Akil Raihan Iftee,Jakaria Rabbi,Mabrook S. Al-rakhami,Abdu Gumae,Ovishake Sen,Mohtasim Fuad,Md. Nazrul Islam机构:The authors are grateful to the Deanship of Scientific Research, king Saud University for funding through Vice Deanship of Scientific, Research Chairs.备注:30 pages and will be submitted to IEEE Access Journal链接:https://arxiv.org/abs/2103.10492[GAN/对抗式/生成式相关]:【1】 LSDAT: Low-Rank and Sparse Decomposition for Decision-based Adversarial  Attack标题:LSDAT:基于决策的对抗性攻击的低秩稀疏分解作者:Ashkan Esmaeili,Marzieh Edraki,Nazanin Rahnavard,Mubarak Shah,Ajmal Mian机构:University of Central Florida Center for Research in Computer Vision University of Western Australia链接:https://arxiv.org/abs/2103.10787【2】 Boosting Adversarial Transferability through Enhanced Momentum标题:通过增强动量来提高对手的可转换性作者:Xiaosen Wang,Jiadong Lin,Han Hu,Jingdong Wang,Kun He机构:Huazhong University of Science and Technology, Microsoft Research Asia备注:13 pages链接:https://arxiv.org/abs/2103.10609【3】 Reading Isn't Believing: Adversarial Attacks On Multi-Modal Neurons标题:阅读是不相信的:对多模态神经元的对抗性攻击作者:David A. Noever,Samantha E. Miller Noever机构:PeopleTec, Inc., Huntsville, Alabama, USA链接:https://arxiv.org/abs/2103.10480[图像/视频检索]:【1】 Connecting Images through Time and Sources: Introducing Low-data,  Heterogeneous Instance Retrieval标题:通过时间和来源连接图像:引入低数据、异构实例检索作者:Dimitri Gominski,Valérie Gouet-Brunet,Liming Chen机构:Valerie Gouet-Brunet, Univ. Gustave Eiffel, IGNENSG- LaSTIG Univ. Gustave Eiffel, IGNENSG-LaSTIG Ecole Centrale Lyon -LIRIS, Ecole Centrale Lyon LIRIS链接:https://arxiv.org/abs/2103.10729【2】 MDMMT: Multidomain Multimodal Transformer for Video Retrieval标题:MDMMT:面向视频检索的多域多模式转换器作者:Maksim Dzabraev,Maksim Kalashnikov,Stepan Komkov,Aleksandr Petiushko机构:Huawei Moscow Research Center链接:https://arxiv.org/abs/2103.10699[行为/时空/光流/姿态/运动]:【1】 Skeleton Merger: an Unsupervised Aligned Keypoint Detector标题:骨架合并:一种无监督的对准关键点检测器作者:Ruoxi Shi,Zhengrong Xue,Yang You,Cewu Lu机构:Shanghai Jiao Tong University备注:CVPR 2021链接:https://arxiv.org/abs/2103.10814【2】 Computational Emotion Analysis From Images: Recent Advances and Future  Directions标题:基于图像的计算情感分析:最新进展与发展方向作者:Sicheng Zhao,Quanwei Huang,Youbao Tang,Xingxu Yao,Jufeng Yang,Guiguang Ding,Bj?rn W. Schuller备注:Accepted chapter in the book "Human Perception of Visual Information Psychological and Computational Perspective"链接:https://arxiv.org/abs/2103.10798【3】 Learning Multiscale Correlations for Human Motion Prediction标题:用于人体运动预测的多尺度相关学习作者:Honghong Zhou,Caili Guo,Hao Zhang,Yanjun Wang机构:Beijing Key Laboratory of Network System Architecture and Convergence, Beijing University of Posts and Telecommunicatio, Beijing, China, Beijing Laboratory of Advanced Information Networks, Beijing, China, China Telecom Dict Application Capability Center, China备注:The paper has submitted to IEEE ICDL 2021, The codes will be available after the paper was accepted链接:https://arxiv.org/abs/2103.10674【4】 Fusion-FlowNet: Energy-Efficient Optical Flow Estimation using Sensor  Fusion and Deep Fused Spiking-Analog Network Architectures标题:Fusion-FlowNet:基于传感器融合和深度融合尖峰-模拟网络结构的节能光流估计作者:Chankyu Lee,Adarsh Kumar Kosta,Kaushik Roy机构:Purdue University, West Lafayette, IN , USA链接:https://arxiv.org/abs/2103.10592【5】 Hopper: Multi-hop Transformer for Spatiotemporal Reasoning标题:Hopper:时空推理的多跳转换器作者:Honglu Zhou,Asim Kadav,Farley Lai,Alexandru Niculescu-Mizil,Martin Renqiang Min,Mubbasir Kapadia,Hans Peter Graf机构:Rutgers University, Piscataway, NJ, USA,  NEC Laboratories America, Inc., San Jose, CA, USA链接:https://arxiv.org/abs/2103.10574【6】 3D Human Pose Estimation with Spatial and Temporal Transformers标题:基于时空变换的三维人体姿态估计作者:Ce Zheng,Sijie Zhu,Matias Mendieta,Taojiannan Yang,Chen Chen,Zhengming Ding机构:University of North Carolina at Charlotte, Tulane University链接:https://arxiv.org/abs/2103.10455[半/弱/无监督相关]:【1】 UniMoCo: Unsupervised, Semi-Supervised and Full-Supervised Visual  Representation Learning标题:UniMoCo:无监督、半监督和全监督视觉表征学习作者:Zhigang Dai,Bolun Cai,Yugeng Lin,Junying Chen机构:ISouth China University of Technology ,Tencent Wechat AI链接:https://arxiv.org/abs/2103.10773【2】 There and Back Again: Self-supervised Multispectral Correspondence  Estimation标题:往返:自监督多光谱对应估计作者:Celyn Walters,Oscar Mendez,Mark Johnson,Richard Bowden机构:Relative to RGB, cross-spectral correspondence estimation链接:https://arxiv.org/abs/2103.10768[跟踪相关]:【1】 DCF-ASN: Coarse-to-fine Real-time Visual Tracking via Discriminative  Correlation Filter and Attentional Siamese Network标题:基于判别相关过滤和注意暹罗网络的由粗到精实时视觉跟踪作者:Xizhe Xue,Ying Li,Xiaoyue Yin,Qiang Shen机构:Northwestern Polytechnical University, Xi'an, China, Aberystwyth University, Xian, China, Aberystwyth, SY,DB, UK,  January链接:https://arxiv.org/abs/2103.10607[迁移学习/domain/主动学习/自适应]:【1】 Robustness via Cross-Domain Ensembles标题:通过跨域集成实现健壮性作者:Teresa Yeo,O?uzhan Fatih Kar,Amir Zamir机构:Swiss Federal Institute of Technology(EPFL)备注:Project website at this https URL链接:https://arxiv.org/abs/2103.10919【2】 Dynamic Transfer for Multi-Source Domain Adaptation标题:一种多源域自适应的动态转移方法作者:Yunsheng Li,Lu Yuan,Yinpeng Chen,Pei Wang,Nuno Vasconcelos机构:UC San Diego, Microsoft备注:Accepted by CVPR 2021链接:https://arxiv.org/abs/2103.10583[裁剪/量化/加速相关]:【1】 Toward Compact Deep Neural Networks via Energy-Aware Pruning标题:基于能量感知修剪的紧凑深度神经网络作者:Seul-Ki Yeom,Kyung-Hwan Shim,Jee-Hyun Hwang机构:Nota Al gmBH, Winterfeldstrasse , Berlin, Germany, F, Daedong Bldg, Seolleung-ro ,-gil, Gangnam-gu, Seoul备注:11 pages, 5 figures, 2 tables链接:https://arxiv.org/abs/2103.10858【2】 CDFI: Compression-Driven Network Design for Frame Interpolation标题:CDFI:压缩驱动的帧内插网络设计作者:Tianyu Ding,Luming Liang,Zhihui Zhu,Ilya Zharkov机构:Johns Hopkins University ,Microsoft BUniversity of Denver备注:To be published in the proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)链接:https://arxiv.org/abs/2103.10559[数据集dataset]:【1】 Carton dataset synthesis based on foreground texture replacement标题:基于前景纹理替换的纸盒数据集合成作者:Lijun Gou,Shengkai Wu,Jinrong Yang,Hangcheng Yu,Linchen Xi,Xiaoping Li,Chao Deng机构:Denga, State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and, Technology, Wuhan, China.链接:https://arxiv.org/abs/2103.10738[超分辨率]:【1】 Hyperspectral Image Super-Resolution in Arbitrary Input-Output Band  Settings标题:任意输入输出波段设置下的高光谱图像超分辨率作者:Zhongyang Zhang,Zhiyang Xu,Zia Ahmed,Asif Salekin,Tauhidur Rahman机构:University of Massachusetts Amherst University at Buffalot Syracuse University+链接:https://arxiv.org/abs/2103.10614[3D/3D重建等相关]:【1】 Concentric Spherical GNN for 3D Representation Learning标题:用于三维表示学习的同心球面GNN作者:James Fox,Bo Zhao,Sivasankaran Rajamanickam,Rampi Ramprasad,Le Song机构:Georgia Institute of Technology, Sandia National Laboratories, Mohamed bin Zayed University备注:This paper has been submitted for conference review链接:https://arxiv.org/abs/2103.10484[其他视频相关]:【1】 Ano-Graph: Learning Normal Scene Contextual Graphs to Detect Video  Anomalies标题:ANO-Graph:学习正常场景上下文图检测视频异常作者:Masoud Pourreza,Mohammadreza Salehi,Mohammad Sabokrou机构:Institute For Research In Fundamental Sciences(IPM)链接:https://arxiv.org/abs/2103.10502[其他]:【1】 Paint by Word标题:用文字作画作者:David Bau,Alex Andonian,Audrey Cui,YeonHwan Park,Ali Jahanian,Aude Oliva,Antonio Torralba机构:Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory, 图, 国, Metallic, Rustic, Purple, Floral, At This Location...., Paint This Word备注:10 pages, 9 figures链接:https://arxiv.org/abs/2103.10951【2】 MetaLabelNet: Learning to Generate Soft-Labels from Noisy-Labels标题:MetaLabelNet:学习从噪声标签生成软标签作者:G?rkem Algan,Ilkay Ulusoy链接:https://arxiv.org/abs/2103.10869【3】 GLOWin: A Flow-based Invertible Generative Framework for Learning  Disentangled Feature Representations in Medical Images标题:Glowin:一种基于流的医学图像解缠特征表示学习的可逆生成框架作者:Aadhithya Sankar,Matthias Keicher,Rami Eisawy,Abhijeet Parida,Franz Pfister,Seong Tae Kim,Nassir Navab机构:Computer Aided Medical Procedures, Technical University of Munich, Germany,  deepc Gmbh, Ludwig Maximilians University Munich, Germany, Kyung Hee University, Korea, Computer Aided Medical Procedures, Johns Hopkins University, USA备注:12 pages, 7 figures链接:https://arxiv.org/abs/2103.10868【4】 CoordiNet: uncertainty-aware pose regressor for reliable vehicle  localization标题:CoordiNet:面向车辆可靠定位的不确定性感知姿态回归器作者:Arthur Moreau,Nathan Piasco,Dzmitry Tsishkou,Bogdan Stanciulescu,Arnaud de La Fortelle备注:8 pages, 8 figures. Submitted to IROS 2021链接:https://arxiv.org/abs/2103.10796【5】 DFS: A Diverse Feature Synthesis Model for Generalized Zero-Shot  Learning标题:DFS:一种面向广义零点学习的多元特征合成模型作者:Bonan Li,Xuecheng Nie,Congying Han机构:University of Chinese Academy of Sciences, Yitu Technology, 十备注:11 pages,5 figures,conference链接:https://arxiv.org/abs/2103.10764【6】 Tf-GCZSL: Task-Free Generalized Continual Zero-Shot Learning标题:TF-GCZSL:无任务广义连续Zero-Shot学习作者:Chandan Gautam,Sethupathy Parameswaran,Ashish Mishra,Suresh Sundaram机构:Indian Institute of Science, Bangalore Indian Institute of Science, Bangalore, IIT Madras, mishracse. iitm. ac. in链接:https://arxiv.org/abs/2103.10741【7】 ConViT: Improving Vision Transformers with Soft Convolutional Inductive  Biases标题:Convit:利用软卷积感应偏置改进视觉变换器作者:Stéphane d'Ascoli,Hugo Touvron,Matthew Leavitt,Ari Morcos,Giulio Biroli,Levent Sagun机构:Sagun, Facebook AI Research, Laboratoire de Physique de I'Ecole normale superieure, ENS, Universite PSL, CNRS, Universite de Paris, Paris, France, Sorbonne Universite, Paris, France链接:https://arxiv.org/abs/2103.10697【8】 Learning the Superpixel in a Non-iterative and Lifelong Manner标题:以非迭代和终身的方式学习超像素作者:Lei Zhu,Qi She,Bin Zhang,Yanye Lu,Zhilin Lu,Duo Li,Jie Hu机构:Institute of Medical Technology, Peking University Health Science Center, Peking University, Beijing University of Posts and Telecommunications, Institute of Biomedical Engineering, Peking University Shenzhen Graduate School备注:Accept by CVPR2021链接:https://arxiv.org/abs/2103.10681【9】 XProtoNet: Diagnosis in Chest Radiography with Global and Local  Explanations标题:XProtoNet:具有全局和局部解释的胸片诊断作者:Eunji Kim,Siwon Kim,Minji Seo,Sungroh Yoon机构:Seoul National University, Seoul, South Korea,  ASRI, INMC, ISRC, and Institute of Engineering Research, Seoul National University备注:10 pages, 7 figures. Accepted to CVPR2021链接:https://arxiv.org/abs/2103.10663【10】 Beyond Linear Subspace Clustering: A Comparative Study of Nonlinear  Manifold Clustering Algorithms标题:超越线性子空间聚类:非线性流形聚类算法的比较研究作者:Maryam Abdolali,Nicolas Gillis机构:Faculte Polytechnique, Universite de Mons, Rue de Houdain , Mons, Belgium备注:55 pages链接:https://arxiv.org/abs/2103.10656【11】 Degrade is Upgrade: Learning Degradation for Low-light Image Enhancement标题:降级即升级:微光图像增强的学习降级作者:Kui Jiang,Zhongyuan Wang,Zheng Wang,Peng Yi,Xiao Wang,Yansheng Qiu,Chen Chen,Chia-Wen Lin机构:Wuhan University,University of North Carolina at Charlotte,National Tsing Hua University链接:https://arxiv.org/abs/2103.10621【12】 Scalable Visual Transformers with Hierarchical Pooling标题:具有分层池的可扩展可视转换器作者:Zizheng Pan,Bohan Zhuang,Jing Liu,Haoyu He,Jianfei Cai机构:Dept of Data Science and, Monash University备注:10 pages链接:https://arxiv.org/abs/2103.10619【13】 Knowledge-Guided Object Discovery with Acquired Deep Impressions标题:基于获得深度印象的知识引导对象发现作者:Jinyang Yuan,Bin Li,Xiangyang Xue机构:Shanghai Key Laboratory of Intelligent Information Processing, Fudan University备注:AAAI 2021链接:https://arxiv.org/abs/2103.10611【14】 Noise Modulation: Let Your Model Interpret Itself标题:噪声调制:让您的模型自行解释作者:Haoyang Li,Xinggang Wang机构:Huazhong University of Science and Technology, Wuhan, Hubei, China链接:https://arxiv.org/abs/2103.10603【15】 Generic Perceptual Loss for Modeling Structured Output Dependencies标题:用于结构化输出相依性建模的一般知觉损失作者:Yifan Liu,Hao Chen,Yu Chen,Wei Yin,Chunhua Shen机构:The University of Adelaide Automind , Monash University, Australia备注:Accepted to Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2021链接:https://arxiv.org/abs/2103.10571【16】 CLTA: Contents and Length-based Temporal Attention for Few-shot Action  Recognition标题:CLTA:基于内容和长度的短镜头动作识别时间注意作者:Yang Bo,Yangdi Lu,Wenbo He机构:McMaster University备注:8 pages, 4 figures链接:https://arxiv.org/abs/2103.10567【17】 Image Synthesis for Data Augmentation in Medical CT using  DeepReinforcement Learning标题:基于深度强化学习的医学CT数据增强图像合成作者:Arjun Krishna,Kedar Bartake,Chuang Niu,Ge Wang,Youfang Lai,Xun Jia,Klaus Mueller机构:Stony Brook University, Stony Brook, NY USA, Center for Biotechnology Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY USA, UT Southwestern Medical Center, Dallas, TX USA备注:Fully3D 2021链接:https://arxiv.org/abs/2103.10493【18】 Localization of Cochlear Implant Electrodes from Cone Beam Computed  Tomography using Particle Belief Propagation标题:基于粒子置信传播的锥束CT人工耳蜗电极定位作者:Hendrik Hachmann,Benjamin Krüger,Bodo Rosenhahn,Waldo Nogueira机构:Leibniz University Hanover, Germany, Hannover Medical School, Hanover, Germany, Cluster of Excellence 'Hearing,All', Hanover, Germany链接:https://arxiv.org/abs/2103.10434机器翻译,仅供参考点击微信群二维码访问www.arxivdaily.com,获取带摘要及更多学科的学术速递。

cs.LG 方向,今日共计69篇

【1】 The Shape of Learning Curves: a Review标题:学习曲线形状研究述评作者:Tom Viering,Marco Loog机构:IDelft University of Technology, The Netherlands, University of Copenhagen, Denmark, March 链接:https://arxiv.org/abs/2103.10948【2】 Empirical Analysis of Machine Learning Configurations for Prediction of  Multiple Organ Failure in Trauma Patients标题:机器学习结构预测创伤患者多器官功能衰竭的实证分析作者:Yuqing Wang,Yun Zhao,Rachael Callcut,Linda Petzold机构:University of California, Santa Barbara,  UC, Davis Health备注:ICDM 2021链接:https://arxiv.org/abs/2103.10929【3】 BERTSurv: BERT-Based Survival Models for Predicting Outcomes of Trauma  Patients标题:BERTSurv:基于BERT的创伤患者预后预测模型作者:Yun Zhao,Qinghang Hong,Xinlu Zhang,Yu Deng,Yuqing Wang,Linda Petzold机构:University of California, Santa Barbara, Northwestern University, yunzhaoocs. ucsb. edu备注:ICDM 2021链接:https://arxiv.org/abs/2103.10928【4】 Landscape analysis for shallow ReLU neural networks: complete  classification of critical points for affine target functions标题:浅层RELU神经网络的景观分析:仿射目标函数临界点的完全分类作者:Patrick Cheridito,Arnulf Jentzen,Florian Rossmannek链接:https://arxiv.org/abs/2103.10922【5】 Robustness via Cross-Domain Ensembles标题:通过跨域集成实现健壮性作者:Teresa Yeo,O?uzhan Fatih Kar,Amir Zamir机构:Swiss Federal Institute of Technology(EPFL)备注:Project website at this https URL链接:https://arxiv.org/abs/2103.10919【6】 Play the Shannon Game With Language Models: A Human-Free Approach to  Summary Evaluation标题:用语言模型玩香农游戏:摘要评估的一种无人方法作者:Nicholas Egan,Oleg Vasilyev,John Bohannon机构:Primer Technologies Inc., San Francisco, California链接:https://arxiv.org/abs/2103.10918【7】 Predicting Drug-Drug Interactions from Heterogeneous Data: An Embedding  Approach标题:从异质数据预测药物相互作用:一种嵌入方法作者:Devendra Singh Dhami,Siwen Yan,Gautam Kunapuli,David Page,Sriraam Natarajan备注:10 pages, 6 figures, Accepted as a short paper to 'Artificial Intelligence in Medicine 2021'链接:https://arxiv.org/abs/2103.10916【8】 Joint Parameter Discovery and Generative Modeling of Dynamic Systems标题:动态系统的联合参数发现与产生式建模作者:Gregory Barber,Mulugeta A. Haile,Tzikang Chen机构:Vehicle Technology Directorate, U.S. Army Research Laboratory, Aberdeen Proving Ground, MD 备注:11 pages, 7 figures链接:https://arxiv.org/abs/2103.10905【9】 From Static to Dynamic Prediction: Wildfire Risk Assessment Based on  Multiple Environmental Factors标题:从静电到动态预测:基于多环境因素的野火风险评估作者:Tanqiu Jiang,Sidhant K. Bendre,Hanjia Lyu,Jiebo Luo机构:University of Rochester链接:https://arxiv.org/abs/2103.10901【10】 Bilinear Classes: A Structural Framework for Provable Generalization in  RL标题:双线性类:RL中可证明推广的结构框架作者:Simon S. Du,Sham M. Kakade,Jason D. Lee,Shachar Lovett,Gaurav Mahajan,Wen Sun,Ruosong Wang链接:https://arxiv.org/abs/2103.10897【11】 Accelerating SLIDE Deep Learning on Modern CPUs: Vectorization,  Quantizations, Memory Optimizations, and More标题:在现代CPU上加速幻灯片深度学习:矢量化、量化、内存优化等作者:Shabnam Daghaghi,Nicholas Meisburger,Mengnan Zhao,Yong Wu,Sameh Gobriel,Charlie Tai,Anshumali Shrivastava链接:https://arxiv.org/abs/2103.10891【12】 MetaLabelNet: Learning to Generate Soft-Labels from Noisy-Labels标题:MetaLabelNet:学习从噪声标签生成软标签作者:G?rkem Algan,Ilkay Ulusoy链接:https://arxiv.org/abs/2103.10869【13】 Toward Compact Deep Neural Networks via Energy-Aware Pruning标题:基于能量感知修剪的紧凑深度神经网络作者:Seul-Ki Yeom,Kyung-Hwan Shim,Jee-Hyun Hwang机构:Nota Al gmBH, Winterfeldstrasse , Berlin, Germany, F, Daedong Bldg, Seolleung-ro ,-gil, Gangnam-gu, Seoul备注:11 pages, 5 figures, 2 tables链接:https://arxiv.org/abs/2103.10858【14】 Towards Better Adaptive Systems by Combining MAPE, Control Theory, and  Machine Learning标题:结合MAPE、控制理论和机器学习走向更好的自适应系统作者:Danny Weyns,Bradley Schmerl,Masako Kishida,Alberto Leva,Marin Litoiu,Necmiye Ozay,Colin Paterson,Kenji Tei机构:KU Leuven, Belgium, Carnegie Mellon University National Institute of Informatics Politecnico di milano, Linnaeus University, Sweden, Pittsburgh, USA, Tokyo, Japan, Milan, Italy, York University, University of Michigan, University of York, Waseda University, York, Canada, Ann Arbor, MI, USA, York, United Kingdom备注:7 pages链接:https://arxiv.org/abs/2103.10847【15】 Prediction of progressive lens performance from neural network  simulations标题:基于神经网络仿真的渐进式透镜性能预测作者:Alexander Leube,Lukas Lang,Gerhard Kelch,Siegfried Wahl机构:Institute for Ophthalmic Research, University of Tuebingen, Tuebingen, Germany, Carl Zeiss Vision International GmbH Aalen, Germany备注:9 pages, 4 figures链接:https://arxiv.org/abs/2103.10842【16】 Improved, Deterministic Smoothing for L1 Certified Robustness标题:改进的确定性平滑,可实现L1认证的稳健性作者:Alexander Levine,Soheil Feizi链接:https://arxiv.org/abs/2103.10834【17】 Enhancing Robustness of On-line Learning Models on Highly Noisy Data标题:增强在线学习模型对高噪声数据的鲁棒性作者:Zilong Zhao,Robert Birke,Rui Han,Bogdan Robu,Sara Bouchenak,Sonia Ben Mokhtar,Lydia Y. Chen备注:Published in IEEE Transactions on Dependable and Secure Computing. arXiv admin note: substantial text overlap with arXiv:1911.04383链接:https://arxiv.org/abs/2103.10824【18】 CoordiNet: uncertainty-aware pose regressor for reliable vehicle  localization标题:CoordiNet:面向车辆可靠定位的不确定性感知姿态回归器作者:Arthur Moreau,Nathan Piasco,Dzmitry Tsishkou,Bogdan Stanciulescu,Arnaud de La Fortelle备注:8 pages, 8 figures. Submitted to IROS 2021链接:https://arxiv.org/abs/2103.10796【19】 Quality Evolvability ES: Evolving Individuals With a Distribution of  Well Performing and Diverse Offspring标题:质量可演化性ES:具有表现良好和多样化的后代分布的进化个体作者:Adam Katona,Daniel W. Franks,James Alfred Walker机构:University of York, UK备注:submitted to "2021 Conference on Artificial Life"链接:https://arxiv.org/abs/2103.10790【20】 LSDAT: Low-Rank and Sparse Decomposition for Decision-based Adversarial  Attack标题:LSDAT:基于决策的对抗性攻击的低秩稀疏分解作者:Ashkan Esmaeili,Marzieh Edraki,Nazanin Rahnavard,Mubarak Shah,Ajmal Mian机构:University of Central Florida Center for Research in Computer Vision University of Western Australia链接:https://arxiv.org/abs/2103.10787【21】 Graph Attention Recurrent Neural Networks for Correlated Time Series  Forecasting标题:图注意递归神经网络在相关时间序列预测中的应用作者:Razvan-Gabriel Cirstea,Chenjuan Guo,Bin Yang机构:Aalborg University, Aalborg, Denmark链接:https://arxiv.org/abs/2103.10760【22】 Forward and Backward Bellman equations improve the efficiency of EM  algorithm for DEC-POMDP标题:前向和后向Bellman方程提高了EM算法求解DEC-POMDP的效率作者:Takehiro Tottori,Tetsuya J. Kobayashi机构:The University of Tokyo., Institute of Industrial Science, The University of Tokyo,Universal Biology Institute, The University of Tokyo链接:https://arxiv.org/abs/2103.10752【23】 PAMELI: A Meta-Algorithm for Computationally Expensive Multi-Objective  Optimization Problems标题:PAMELI:求解计算量大的多目标优化问题的元算法作者:Santiago Cuervo,Miguel Melgarejo,Angie Blanco-Ca?on,Laura Reyes-Fajardo,Sergio Rojas-Galeano链接:https://arxiv.org/abs/2103.10736【24】 AutoTune: Controller Tuning for High-Speed Flight标题:自动调谐:高速飞行的控制器调整作者:Antonio Loquercio,Alessandro Saviolo,Davide Scaramuzza机构:Fig. ,: A quadrotor flies a time-optimal trajectory with top speeds of , ms-,. We automatically find a controller configuration that can, fly such highspeed- maneuver with novel sampling-based- method called AutoTune. To get better sense of the speed achieved by the, quadrotor, please watch the supplementary movie备注:Video: this https URL; Code: this https URL链接:https://arxiv.org/abs/2103.10698【25】 ConViT: Improving Vision Transformers with Soft Convolutional Inductive  Biases标题:Convit:利用软卷积感应偏置改进视觉变换器作者:Stéphane d'Ascoli,Hugo Touvron,Matthew Leavitt,Ari Morcos,Giulio Biroli,Levent Sagun机构:Sagun, Facebook AI Research, Laboratoire de Physique de I'Ecole normale superieure, ENS, Universite PSL, CNRS, Universite de Paris, Paris, France, Sorbonne Universite, Paris, France链接:https://arxiv.org/abs/2103.10697【26】 QROSS: QUBO Relaxation Parameter Optimisation via Learning Solver  Surrogates标题:QROSS:基于学习求解器代理的Qubo松弛参数优化作者:Tian Huang,Siong Thye Goh,Sabrish Gopalakrishnan,Tao Luo,Qianxiao Li,Hoong Chuin Lau机构:Institute of High Performance Computing, Agency for Science Technology and Research, Singapore, March 备注:17 pages链接:https://arxiv.org/abs/2103.10695【27】 Adversarial and Contrastive Variational Autoencoder for Sequential  Recommendation标题:用于顺序推荐的对抗性和对比性变分自动编码器作者:Zhe Xie,Chengxuan Liu,Yichi Zhang,Hongtao Lu,Dong Wang,Yue Ding机构:University of Michigan-Shanghai Jiao, Engineering, Tong University Joint Institute, Shanghai Jiao Tong University备注:11 pages, WWW 2021链接:https://arxiv.org/abs/2103.10693【28】 Interpretable Deep Learning: Interpretations, Interpretability,  Trustworthiness, and Beyond标题:可解释的深度学习:解释、可解释性、可信性和超越作者:Xuhong Li,Haoyi Xiong,Xingjian Li,Xuanyu Wu,Xiao Zhang,Ji Liu,Jiang Bian,Dejing Dou机构:Received: dateAccepted:date链接:https://arxiv.org/abs/2103.10689【29】 Controllable Generation from Pre-trained Language Models via Inverse  Prompting标题:通过反向提示从预先训练的语言模型中可控地生成作者:Xu Zou,Da Yin,Qingyang Zhong,Hongxia Yang,Zhilin Yang,Jie Tang机构: Beijing Academy of Artificial Intelligence,  Recurrent, Ltd., Alibaba Inc.链接:https://arxiv.org/abs/2103.10685【30】 Masked Conditional Random Fields for Sequence Labeling标题:用于序列标注的掩码条件随机场作者:Tianwen Wei,Jianwei Qi,Shenghuan He,Songtao Sun机构:Xiaomi Al备注:accepted by NAACL 2021链接:https://arxiv.org/abs/2103.10682【31】 Learning the Superpixel in a Non-iterative and Lifelong Manner标题:以非迭代和终身的方式学习超像素作者:Lei Zhu,Qi She,Bin Zhang,Yanye Lu,Zhilin Lu,Duo Li,Jie Hu机构:Institute of Medical Technology, Peking University Health Science Center, Peking University, Beijing University of Posts and Telecommunications, Institute of Biomedical Engineering, Peking University Shenzhen Graduate School备注:Accept by CVPR2021链接:https://arxiv.org/abs/2103.10681【32】 Cost-effective Deployment of BERT Models in Serverless Environment标题:无服务器环境下BERT模型的高性价比部署作者:Katarína Bene?ová,Andrej ?vec,Marek ?uppa链接:https://arxiv.org/abs/2103.10673【33】 API2Com: On the Improvement of Automatically Generated Code Comments  Using API Documentations标题:API2Com:关于使用API文档自动生成代码注释的改进作者:Ramin Shahbazi,Rishab Sharma,Fatemeh H. Fard机构:University of British Columbia, Canada, Kelowna链接:https://arxiv.org/abs/2103.10668【34】 USTC-NELSLIP System Description for DIHARD-III Challenge标题:用于DIHARD-III挑战的USTC-NELSLIP系统描述作者:Yuxuan Wang,Maokui He,Shutong Niu,Lei Sun,Tian Gao,Xin Fang,Jia Pan,Jun Du,Chin-Hui Lee机构:University of Science and Technology University of Science and Technology University of Science and Technology, of china, Hefei, Anhui, China, Flytek Research, University of Science and Technology of China, Georgia Institute of Technology, Atlanta, Georgia, USA链接:https://arxiv.org/abs/2103.10661【35】 Beyond Linear Subspace Clustering: A Comparative Study of Nonlinear  Manifold Clustering Algorithms标题:超越线性子空间聚类:非线性流形聚类算法的比较研究作者:Maryam Abdolali,Nicolas Gillis机构:Faculte Polytechnique, Universite de Mons, Rue de Houdain , Mons, Belgium备注:55 pages链接:https://arxiv.org/abs/2103.10656【36】 SoK: A Modularized Approach to Study the Security of Automatic Speech  Recognition Systems标题:SOK:一种研究自动语音识别系统安全性的模块化方法作者:Yuxuan Chen,Jiangshan Zhang,Xuejing Yuan,Shengzhi Zhang,Kai Chen,Xiaofeng Wang,Shanqing Guo机构:Key Laboratory of Cryptologic Technology and Information Security, Ministry of Education, Shandong University,China, Shandong University, China, SKLOIS, Institute of Information Engineering, Chinese Academy of Sciences, China, University of Chinese Academy of Sciences, China, Metropolitan College, Boston University, USA, Computing and Engineering, Indiana University Bloomington, USA备注:17 pages链接:https://arxiv.org/abs/2103.10651【37】 Cascade Weight Shedding in Deep Neural Networks: Benefits and Pitfalls  for Network Pruning标题:深度神经网络中的级联减权:网络修剪的利弊作者:Kambiz Azarian,Fatih Porikli机构:Qualcomm Technologies Inc., San Diego, CA , USA链接:https://arxiv.org/abs/2103.10629【38】 Knowledge-Guided Object Discovery with Acquired Deep Impressions标题:基于获得深度印象的知识引导对象发现作者:Jinyang Yuan,Bin Li,Xiangyang Xue机构:Shanghai Key Laboratory of Intelligent Information Processing, Fudan University备注:AAAI 2021链接:https://arxiv.org/abs/2103.10611【39】 Training image classifiers using Semi-Weak Label Data标题:使用半弱标签数据训练图像分类器作者:Anxiang Zhang,Ankit Shah,Bhiksha Raj机构:Carnegie Mellon University Carnegie Mellon University Carnegie Mellon University备注:First two authors contributed equally链接:https://arxiv.org/abs/2103.10608【40】 Noise Modulation: Let Your Model Interpret Itself标题:噪声调制:让您的模型自行解释作者:Haoyang Li,Xinggang Wang机构:Huazhong University of Science and Technology, Wuhan, Hubei, China链接:https://arxiv.org/abs/2103.10603【41】 Cognitive simulation models for inertial confinement fusion: Combining  simulation and experimental data标题:惯性约束聚变认知仿真模型:仿真与实验数据相结合作者:K. D. Humbird,J. L. Peterson,J. Salmonson,B. K. Spears机构:Lawrence Livermore National Laboratory, East Ave, Livermore CA, USA, (Dated: , March ,)链接:https://arxiv.org/abs/2103.10590【42】 HW-NAS-Bench:Hardware-Aware Neural Architecture Search Benchmark标题:HW-NAS-BENCH:硬件感知的神经体系结构搜索基准作者:Chaojian Li,Zhongzhi Yu,Yonggan Fu,Yongan Zhang,Yang Zhao,Haoran You,Qixuan Yu,Yue Wang,Yingyan Lin机构:Rice University备注:Accepted at ICLR 2021 (Spotlight)链接:https://arxiv.org/abs/2103.10584【43】 Towards Productizing AI/ML Models: An Industry Perspective from Data  Scientists标题:走向产品化的AI/ML模型:来自数据科学家的行业视角作者:Filippo Lanubile,Fabio Calefato,Luigi Quaranta,Maddalena Amoruso,Fabio Fumarola,Michele Filannino机构:University of Bari, Prometeia, Bari,Italy, Milano, Italy备注:4 pages链接:https://arxiv.org/abs/2103.10548【44】 Data driven algorithms for limited labeled data learning标题:有限标签数据学习的数据驱动算法作者:Maria-Florina Balcan,Dravyansh Sharma机构:Carnegie Mellon University, Pittsburgh, PA , ninamfOcs. cmu. edu, March 备注:31 pages, 9 figures链接:https://arxiv.org/abs/2103.10547【45】 White Paper Machine Learning in Certified Systems标题:认证系统中的白皮书机器学习作者:Hervé Delseny,Christophe Gabreau,Adrien Gauffriau,Bernard Beaudouin,Ludovic Ponsolle,Lucian Alecu,Hugues Bonnin,Brice Beltran,Didier Duchel,Jean-Brice Ginestet,Alexandre Hervieu,Ghilaine Martinez,Sylvain Pasquet,Kevin Delmas,Claire Pagetti,Jean-Marc Gabriel,Camille Chapdelaine,Sylvaine Picard,Mathieu Damour,Cyril Cappi,Laurent Gardès,Florence De Grancey,Eric Jenn,Baptiste Lefevre,Gregory Flandin,Sébastien Gerchinovitz,Franck Mamalet,Alexandre Albore备注:113 pages, White paper链接:https://arxiv.org/abs/2103.10529【46】 S3M: Siamese Stack (Trace) Similarity Measure标题:S3M:暹罗堆栈(迹)相似性度量作者:Aleksandr Khvorov,Roman Vasiliev,George Chernishev,Irving Muller Rodrigues,Dmitrij Koznov,Nikita Povarov机构:JetBrains, ITMO University, Saint-Petersburg State University, Saint-Petersburg, Russia, Nikita povarov, Polytechnique Montreal, Montreal, Canada链接:https://arxiv.org/abs/2103.10526【47】 Generalizing Object-Centric Task-Axes Controllers using Keypoints标题:使用关键点泛化以对象为中心的任务轴控制器作者:Mohit Sharma,Oliver Kroemer备注:International Conference on Robotics and Automation (ICRA'21). For results see this https URL链接:https://arxiv.org/abs/2103.10524【48】 Pretraining the Noisy Channel Model for Task-Oriented Dialogue标题:用于任务型对话的噪声信道模型预训练作者:Qi Liu,Lei Yu,Laura Rimell,Phil Blunsom机构:DeepMind,University of Oxford备注:Accepted to TACL, pre MIT Press publication version链接:https://arxiv.org/abs/2103.10518【49】 Hidden Technical Debts for Fair Machine Learning in Financial Services标题:金融服务中公平机器学习的隐性技术债务作者:Chong Huang,Arash Nourian,Kevin Griest机构:FICO AI Research, San Jose, CA , San Rafael, CA 备注:Presented at NeurIPS 2020 Fair AI in Finance Workshop链接:https://arxiv.org/abs/2103.10510【50】 Super-convergence and Differential Privacy: Training faster with better  privacy guarantees标题:超收敛和差分隐私:训练速度更快,隐私保证更好作者:Osvald Frisk,Friedrich D?rmann,Christian Marius Lillelund,Christian Fischer Pedersen机构:Aarhus University, Denmark Aarhus University Denmark Aarhus University, Denmark Aarhus University, Denmark备注:(To be) Published and presented at the 55th Annual Conference on Information Sciences and Systems (CISS), 7 pages, 4 figures链接:https://arxiv.org/abs/2103.10498【51】 Naive Automated Machine Learning -- A Late Baseline for AutoML标题:朴素自动机器学习--AutoML的晚期基线作者:Felix Mohr,Marcel Wever机构:March 链接:https://arxiv.org/abs/2103.10496【52】 Recent Advances in Deep Learning Techniques for Face Recognition标题:深度学习技术在人脸识别中的研究进展作者:Md. Tahmid Hasan Fuad,Awal Ahmed Fime,Delowar Sikder,Md. Akil Raihan Iftee,Jakaria Rabbi,Mabrook S. Al-rakhami,Abdu Gumae,Ovishake Sen,Mohtasim Fuad,Md. Nazrul Islam机构:The authors are grateful to the Deanship of Scientific Research, king Saud University for funding through Vice Deanship of Scientific, Research Chairs.备注:30 pages and will be submitted to IEEE Access Journal链接:https://arxiv.org/abs/2103.10492【53】 Concentric Spherical GNN for 3D Representation Learning标题:用于三维表示学习的同心球面GNN作者:James Fox,Bo Zhao,Sivasankaran Rajamanickam,Rampi Ramprasad,Le Song机构:Georgia Institute of Technology, Sandia National Laboratories, Mohamed bin Zayed University备注:This paper has been submitted for conference review链接:https://arxiv.org/abs/2103.10484【54】 Two Timescale Hybrid Federated Learning with Cooperative D2D Local Model  Aggregations标题:合作D2D局部模型集结的双时间尺度混合联邦学习作者:Frank Po-Chen Lin,Seyyedali Hosseinalipour,Sheikh Shams Azam,Christopher G. Brinton,Nicolo Michelusi备注:This paper is currently under review for publication链接:https://arxiv.org/abs/2103.10481【55】 Reading Isn't Believing: Adversarial Attacks On Multi-Modal Neurons标题:阅读是不相信的:对多模态神经元的对抗性攻击作者:David A. Noever,Samantha E. Miller Noever机构:PeopleTec, Inc., Huntsville, Alabama, USA链接:https://arxiv.org/abs/2103.10480【56】 Unsupervised Doppler Radar-Based Activity Recognition for e-healthcare标题:基于无监督多普勒雷达的电子医疗活动识别作者:Yordanka Karayaneva,Sara Sharifzadeh,Wenda Li,Yanguo Jing,Bo Tan机构:Environment and Computing, Coventry, March 链接:https://arxiv.org/abs/2103.10478【57】 Neural Networks for Semantic Gaze Analysis in XR Settings标题:XR环境下语义凝视分析的神经网络方法作者:Lena Stubbemann,Dominik Dürrschnabel,Robert Refflinghaus机构:Virtual-reality(VR)and augmented-reality (AR)technology is increasingly combined with eye-tracking. This combination broadens, both fields and opens up new areas of application, in which visual perception and related cognitive processes can be studied in, interactive but still well controlled settings. However, performing a semantic gaze analysis of eye-tracking data from interactive, three-dimensional scenes is a resource-intense task, which so far has been an obstacle to economic use. In this paper we present a, novel approach which minimizes time and information necessary to annotate volumes of interest(VOIs) by using techniques from, object recognition. To do so, we train convolutional neural networks(CNNs)on synthetic data sets derived from virtual models using, image augmentation techniques. We evaluate our method in real and virtual environments, showing that the method can compete with备注:16 pages, 6 figures, 1 table, Accepted to: ETRA2021, ACM Symposium on Eye Tracking Research and Applications链接:https://arxiv.org/abs/2103.10451【58】 Multi-Time-Scale Input Approaches for Hourly-Scale Rainfall-Runoff  Modeling based on Recurrent Neural Networks标题:基于递归神经网络的小时尺度降雨径流模型的多时间尺度输入方法作者:Kei Ishida,Masato Kiyama,Ali Ercan,Motoki Amagasaki,Tongbi Tu,Makoto Ueda备注:11pages, 5 figures链接:https://arxiv.org/abs/2103.10932【59】 Prediction of Hydraulic Blockage at Cross Drainage Structures using  Regression Analysis标题:用回归分析法预测交叉排水构筑物的水力堵塞作者:Umair Iqbal,Johan Barthelemy,Pascal Perez,Wanqing Li备注:12 pages, 5 figures链接:https://arxiv.org/abs/2103.10930【60】 Deep Label Fusion: A 3D End-to-End Hybrid Multi-Atlas Segmentation and  Deep Learning Pipeline标题:深度标签融合:一种端到端的三维混合多地图集分割和深度学习流水线作者:Long Xie,Laura E. M. Wisse,Jiancong Wang,Sadhana Ravikumar,Trevor Glenn,Anica Luther,Sydney Lim,David A. Wolk,Paul A. Yushkevich机构: Penn Image Computing and Science Laboratory(CSL), Univer-, Lund University, Lund, Sweden,  Penn Memory Center, University of Pennsylvania, Philadelphia, USA备注:12 pages paper accepted by the international conference of Information Processing in Medical Imaging (IPMI) 2021链接:https://arxiv.org/abs/2103.10892【61】 Universal Trading for Order Execution with Oracle Policy Distillation标题:使用Oracle策略蒸馏实现订单执行的通用交易作者:Yuchen Fang,Kan Ren,Weiqing Liu,Dong Zhou,Weinan Zhang,Jiang Bian,Yong Yu,Tie-Yan Liu机构: Shanghai Jiao Tong University,Microsoft Research备注:Accepted in AAAI 2021, the code and the supplementary materials are in this https URL链接:https://arxiv.org/abs/2103.10860【62】 Sparse Algorithms for Markovian Gaussian Processes标题:马尔可夫高斯过程的稀疏算法作者:William J. Wilkinson,Arno Solin,Vincent Adam机构:Aalto University, Secondmind.ai, The Alan Turing Institute备注:Appearing in the 24th International Conference on Artificial Intelligence and Statistics (AISTATS) 2021链接:https://arxiv.org/abs/2103.10710【63】 Cluster-to-Conquer: A Framework for End-to-End Multi-Instance Learning  for Whole Slide Image Classification标题:整幅幻灯片图像分类的端到端多实例学习框架--聚类到征服(Cluster-to-Conquer)作者:Yash Sharma,Aman Shrivastava,Lubaina Ehsan,Christopher A. Moskaluk,Sana Syed,Donald E. Brown机构:CAM,PQVIRGINIA.EDU,  University of Virginia, Charlottesville, Virginia, USA, Editors: Under Review for MIDL 备注:Submitted to MIDL, 2021 - this https URL链接:https://arxiv.org/abs/2103.10626【64】 Towards a Dimension-Free Understanding of Adaptive Linear Control标题:对自适应线性控制的无量纲理解作者:Juan C. Perdomo,Max Simchowitz,Alekh Agarwal,Peter Bartlett机构:University of California, Berkeley University of California, Berkeley, Microsoft Research, March 链接:https://arxiv.org/abs/2103.10620【65】 Inductive Inference in Supervised Classification标题:监督分类中的归纳推理作者:Ali Amiryousefi机构:University of Helsinki, FI-, Finland, Supervisor: Prof. Jukka Corander链接:https://arxiv.org/abs/2103.10549【66】 UNETR: Transformers for 3D Medical Image Segmentation标题:UNETR:用于三维医学图像分割的转换器作者:Ali Hatamizadeh,Dong Yang,Holger Roth,Daguang Xu机构:NVIDIA, Santa Clara, CA, USA备注:11 pages, 2 figures链接:https://arxiv.org/abs/2103.10504【67】 Dynamic Kernel Matching for Non-conforming Data: A Case Study of T-cell  Receptor Datasets标题:非一致性数据的动态核匹配:以T细胞受体数据集为例作者:Jared Ostmeyer,Scott Christley,Lindsay Cowell机构:University of Texas Southwestern Medical Center, Dallas, Texas, United States of America链接:https://arxiv.org/abs/2103.10472【68】 Cellcounter: a deep learning framework for high-fidelity spatial  localization of neurons标题:细胞计数器:一种用于神经元高保真空间定位的深度学习框架作者:Tamal Batabyal,Aijaz Ahmad Naik,Daniel Weller,Jaideep Kapur机构:University of Virginia, Charlottesville-, Virginia, United States备注:Submitted to a journal链接:https://arxiv.org/abs/2103.10462【69】 MARS: Markov Molecular Sampling for Multi-objective Drug Discovery标题:MARS:用于多目标药物发现的马尔可夫分子抽样作者:Yutong Xie,Chence Shi,Hao Zhou,Yuwei Yang,Weinan Zhang,Yong Yu,Lei Li机构:ByteDance AI Lab, Shanghai, China, University of Michigan, Ann Arbor, MI, USA, AMontreal Institute of Learning Algorithms, Montreal, Canada, Shanghai Jiao Tong University, China备注:ICLR 2021链接:https://arxiv.org/abs/2103.10432机器翻译,仅供参考点击微信群二维码访问www.arxivdaily.com,获取带摘要及更多学科的学术速递。 转载请注明出处:mian, 陈福黎|这一碗酸爽可口的冬阴功面,让你“咝”~啊~~ :http://www.720weixin.com/marketing/405700.html
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