The workshop brought together experts worldwide in the field to present their latest research, discuss cutting-edge topics, and share insights into the theoretical underpinnings of machine learning. It will cover the theory and new practices on foundation models, understanding and analyzing key compone...
Shanghai Jiaotong University Talk title: Condensation in Deep Learning Abstract Bio Yang Yuan Tsinghua University Talk title: Contrastive Learning Is Spectral Clustering on Similarity Graph Abstract Bio
研讨会聚焦于视觉障碍人士需求,对answer visual questions, ground answers, recognize visual questions with multiple answer groundings, recognize objects in few-shot learning scenarios, locate objects in few-shot learning scenarios, and classify images in a zero-shot setting当前研究和应用问题进行讨论。 是否...
LearningSys 2015 : NIPS 2015 Workshop on Machine Learning Systemssameer
1.OpenSUN 3D:2nd Workshop on Open-Vocabulary 3D Scene Understanding 项目主页:https://opensun3d.github.io/ 技术方向:三维场景理解 赛道一:Open-vocabulary 3D Object Instance Search 赛道二:Open-vocabulary 3D Functionality Grounding 截止日期:2024...
Multi-Agent Autonomous Systems Meet Foundation Models: Challenges and Futures 项目主页:https://coop-intelligence.github.io/ 研讨会聚焦于 Cooperative Intelligence(集体智能)在自动驾驶和机器人应用领域所面临的挑战和机遇,探讨相关技术和应用问题。包括: ...
c CCF Transactions on High Performance Computing 1.300 Springer 2524-4922 Networking Science Springer 2076-0310 Telematics and Informatics Reports Elsevier 2772-5030 Systems Science & Control Engineering Taylor & Francis 2164-2583 International Journal of Fuzzy Logic and Intelligent Systems Korean Institute ...
Earlier this year, Apple hosted the Workshop on Machine Learning for Health. This two-day hybrid event brought together Apple and the…
Phase prediction for high-entropy alloys using generative adversarial network and active learning based on small datasets To improve the performance and interpretability of the model, domain knowledge is incorporated into the feature selection. Additionally, considering that the ... C Chen,HR Zhou,WM ...
On average, the combined estimator is usually better than any of the single base estimator because its variance is reduced. Examples: Bagging methods, Forests of randomized trees By contrast, in boosting methods, base estimators are built sequentially and one tries to reduce the bias of the ...