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14
2021-04
人工智能学院2021年系列学术活动——美国康涅狄格大学梁冠男博士学术报告
人工智能学院2021年系列学术活动——美国康涅狄格大学梁冠男博士学术报告 报告题目：非凸稀疏学习问题的有效算法Efficient Algorithms for Nonconvex Sparse Learning Problems报告简介：Sparse learning plays important roles in the fields of statistical learning, machine learning (ML) and signal processing. Learning with high dimensional data often relies on the sparsity-driven regularization. Solving a spa...
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25
2021-03
人工智能学院2021年系列学术活动——大连理工大学杨鑫教授学术报告
报告题目：场景的智能理解与交互计算报告内容：围绕场景内容的智能理解与交互计算，探究真实世界场景内容表征、建模、感知与交互之间的关联，探讨智能体如何提高对于动态非结构场景的环境预测能力和行为决策能力，实现“人-机-环境”的协同学习与计算，并汇报所取得的工作进展。 报告人介绍：杨鑫，大连理工大学计算机学院教授，博士生导师，学校学科建设办公室副主任。本科毕业于吉林大学计算机学院，博士毕业于浙江大学计算机...
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14
2020-12
人工智能学院2020年系列学术活动——英国伯明翰大学高一星博士学术报告
Title: User Modelling for Personalised Dressing Assistance by Humanoid Robots报告题目：基于人型机器人提供个性化穿衣帮助的用户模型方法Abstract: Assistive humanoid robots in home environments are steadily increasing in popularity. Due to significant variabilities in human behaviour, as well as physical characteristics and individual preferences, personalising assistance poses a challenging prob...
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08
2020-12
人工智能学院2020年系列学术活动——南方科技大学姚新教授学术报告
Xin YaoGuangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation (Great-Brain)Department of Computer Science and EngineeringSouthern University of Science and Technology (SUSTech)Shenzhen, China Evolutionary computation has been an important area for artificial intellogence for more than 40 years. Yet we do not hear it much from the media. In this talk, we will explai...
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06
2020-01
人工智能学院2020年系列学术活动（第四场）——悉尼大学苏道毕力格博士后研究员学术报告
报告题目：人工智能和机器人技术在精准农业上的应用报告摘要：农业自动化，机器人化，人工智能化会在极大程度地降低人力成本，提高肥料和其他农药利用率，降低对环境的破坏和确保食品的安全方面都将带来一场革命化性的改变。农业机器人通过基于深度神经网络的人工智能感知系统，可以完成自主犁地，播种，精准施肥和喷射农药，除草，健康程度和病虫害监测，丰收预测，收割等一系列任务。此报告重点讲述人工智能方法在农业机器...
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03
2020-01
人工智能学院2020年系列学术活动（第三场）——北京交通大学于剑教授学术报告
报告题目：基于认知的机器学习公理化报告内容：在大数据时代，因应用需求的驱动，大量新机器学习方法不断产生。 这些新算法理论依据各异，彼此之间的关系极其复杂，对学习算法的使用者要求极高。但是， 儿童的学习能力虽高， 却不能掌握现今机器学习的理论。 是否能够提出一套符合人类认知的机器学习理论，是当前一个亟待解决的问题。本次报告试图提出一个统一基于认知的机器学习公理化框架， 其基本假设是： 归哪类，像哪类...
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03
2020-01
人工智能学院2020年系列学术活动（第二场）——澳大利亚蒙纳士大学刘铭博士学术报告
报告题目：Learning Deep Neural Networks for Hard Natural Language Problems报告摘要：Deep learning has revolutionized the way that many tasks are tackled in Natural Language Processing. The talk will cover some recent advances in deep learning for NLP, including embeddings, encoder-decoder architecture, attentions and language modelling. Then, I will introduce some of the research work in Mo...
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30
2019-12
人工智能学院2020年系列学术活动（第一场）——新加坡管理大学Amy研究员学术报告
One-Class Order Embedding Learning for Dependency Relation Prediction Abstract: Most of today's representation learning techniques aim to learn entity representations that are semantics-preserving, i.e., semantically similar entities are mapped into a nearby area in the semantic embedding space. In this study, we aim to learn order-preserving representations for entities, i.e., antisymmetri...
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26
2019-12
澳大利亚国立大学杜岚博士学术报告
报告题目：Multi-label Learning with/without Zero-shot Classes报告内容：Multi-label learning refers to the problem of learning to assign a subset of relevant labels to each object, drawn from a large set of candidate labels. Each object is thus associated with a binary label vector, which denotes the presence/absence of each of the candidate labels. In real-world application, such as image/do...
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21
2019-12
美国哥伦比亚大学陈博源博士学术报告
报告题目：Machine Theory of Behavior and Mind 报告摘要：We have seen impressive results from Machine Learning that enable machines to recognize objects very accurately, translate between multiple languages, and manipulate various objects with high success rate. However, there is still a gap to build machines that can operate in unconstrained environment. Humans are able to understand the goa...
