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5月21日晚7:30-9:00
AI TIME特别邀请了三位优秀的讲者跟大家共同开启ICLR专场一！
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扫码关注AITIME哔哩哔哩官方账号
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链接：
★ 邀请嘉宾 ★
丁宁：清华大学计算机系三年级博士生，导师为郑海涛副教授。研究方向为自然语言处理，相关成果发表在ACL，ICLR，EMNLP，AAAI, TKDE，IJCAI等会议、期刊上（）。
报告题目：
原型关系表示学习
摘要：
Recognizing relations between entities is a pivotal task of relational learning. Learning relation representations from distantly-labeled datasets is difficult because of the abundant label noise and complicated expressions in human language. This paper aims to learn predictive, interpretable, and robust relation representations from distantly-labeled data that are effective in different settings, including supervised, distantly supervised, and few-shot learning. Instead of solely relying on the supervision from noisy labels, we propose to learn prototypes for each relation from contextual information to best explore the intrinsic semantics of relations. Prototypes are representations in the feature space abstracting the essential semantics of relations between entities in sentences. We learn prototypes based on objectives with clear geometric interpretation, where the prototypes are unit vectors uniformly dispersed in a unit ball, and statement embeddings are centered at the end of their corresponding prototype vectors on the surface of the ball. This approach allows us to learn meaningful, interpretable prototypes for the final classification. Results on several relation learning tasks show that our model significantly outperforms the previous state-of-the-art models. We further demonstrate the robustness of the encoder and the interpretability of prototypes with extensive experiments.
论文标题：
Prototypical Representation Learning for Relation Extraction
论文链接：
=aCgLmfhIy_f
谢雨桐：美国密歇根大学信息学院博士生，导师为梅俏竹教授。在此之前，她以ACM班级成员的身份在上海交通大学获得学士学位，师从俞勇教授和张伟楠教授。她的研究兴趣是针对显式和隐式的结构化数据的机器学习方法，特别是图、网络和文本等。(个人网站/)。
报告题目：
MARS：马尔可夫分子采样
在多目标药物发现中的应用
摘要：
Searching for novel molecules with desired chemical properties is crucial in drug discovery. Existing work focuses on developing neural models to generate either molecular sequences or chemical graphs. However, it remains a big challenge to find novel and diverse compounds satisfying several properties. In this paper, we propose MARS, a method for multi-objective drug molecule discovery. MARS is based on the idea of generating the chemical candidates by iteratively editing fragments of molecular graphs. To search for high-quality candidates, it employs Markov chain Monte Carlo sampling (MCMC) on molecules with an annealing scheme and an adaptive proposal. To further improve sample efficiency, MARS uses a graph neural network (GNN) to represent and select candidate edits, where the GNN is trained on-the-fly with samples from MCMC. Experiments show that MARS achieves state-of-the-art performance in various multi-objective settings where molecular bio-activity, drug-likeness, and synthesizability are considered. Remarkably, in the most challenging setting where all four objectives are simultaneously optimized, our approach outperforms previous methods significantly in comprehensive evaluations. The code is available at .
论文标题：
MARS: Markov Molecular Sampling for Multi-objective Drug Discovery
论文链接：
=kHSu4ebxFXY
王彬旭：圣路易斯华盛顿大学神经科学系三年级博士生，导师是Carlos R. Ponce教授(该实验室将于今年秋季迁往哈佛大学)。他于2018年毕业于北京大学元培学院，获得物理学学士学位。在大学期间，他接受了理论神经科学家陶乐天教授的计算神经科学培训。目前，他的研究重点是将神经网络可解释性、生成模型和优化工具与传统神经科学实验相结合，以理解在生物和人工神经网络中实现的视觉。(项目页面/
报告题目：
深度生成模型的几何分析与应用
摘要：
Generative adversarial networks (GANs) have emerged as a powerful unsupervised method to model the statistical patterns of real-world data sets, such as natural images. These networks are trained to map random inputs in their latent space to new samples representative of the learned data. However, the structure of the latent space is hard to intuit due to its high dimensionality and the non-linearity of the generator, which limits the usefulness of the models. Understanding the latent space requires a way to identify input codes for existing real-world images (inversion), and a way to identify directions with known image transformations (interpretability). Here, we use a geometric framework to address both issues simultaneously. We develop an architecture-agnostic method to compute the Riemannian metric of the image manifold created by GANs. The eigen-decomposition of the metric isolates axes that account for different levels of image variability. An empirical analysis of several pretrained GANs shows that image variation around each position is concentrated along surprisingly few major axes (the space is highly anisotropic) and the directions that create this large variation are similar at different positions in the space (the space is homogeneous). We show that many of the top eigenvectors correspond to interpretable transforms in the image space, with a substantial part of eigenspace corresponding to minor transforms which could be compressed out. This geometric understanding unifies key previous results related to GAN interpretability. We show that the use of this metric allows for more efficient optimization in the latent space (e.g. GAN inversion) and facilitates unsupervised discovery of interpretable axes. Our results illustrate that defining the geometry of the GAN image manifold can serve as a general framework for understanding GANs.
论文标题：
A Geometric Analysis of Deep Generative Image Models and Its Applications
论文链接：
.06006 ;
=GH7QRzUDdXG
直播结束后我们会邀请讲者在微信群中与大家答疑交流，请添加“AI TIME小助手（微信号：AITIME_HY）”，回复“phd2”，将拉您进“PhD交流群”！
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合作媒体：学术头条
『今日视频推荐』
AI TIME欢迎AI领域学者投稿，期待大家剖析学科历史发展和前沿技术。针对热门话题，我们将邀请专家一起论道。同时，我们也长期招募优质的撰稿人，顶级的平台需要顶级的你，
请将简历等信息发至yun.he@aminer.cn！
微信联系:AITIME_HY
AI TIME是清华大学计算机系一群关注人工智能发展，并有思想情怀的青年学者们创办的圈子,旨在发扬科学思辨精神，邀请各界人士对人工智能理论、算法、场景、应用的本质问题进行探索，加强思想碰撞，打造一个知识分享的聚集地。
更多资讯请扫码关注
我知道你在看哟
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直播预告
点击蓝字
关注我们
AI TIME欢迎每一位AI爱好者的加入！
5月21日晚7:30-9:00
AI TIME特别邀请了三位优秀的讲者跟大家共同开启ICLR专场一！
哔哩哔哩直播通道
扫码关注AITIME哔哩哔哩官方账号
观看直播
链接：
★ 邀请嘉宾 ★
丁宁：清华大学计算机系三年级博士生，导师为郑海涛副教授。研究方向为自然语言处理，相关成果发表在ACL，ICLR，EMNLP，AAAI, TKDE，IJCAI等会议、期刊上（）。
报告题目：
原型关系表示学习
摘要：
Recognizing relations between entities is a pivotal task of relational learning. Learning relation representations from distantly-labeled datasets is difficult because of the abundant label noise and complicated expressions in human language. This paper aims to learn predictive, interpretable, and robust relation representations from distantly-labeled data that are effective in different settings, including supervised, distantly supervised, and few-shot learning. Instead of solely relying on the supervision from noisy labels, we propose to learn prototypes for each relation from contextual information to best explore the intrinsic semantics of relations. Prototypes are representations in the feature space abstracting the essential semantics of relations between entities in sentences. We learn prototypes based on objectives with clear geometric interpretation, where the prototypes are unit vectors uniformly dispersed in a unit ball, and statement embeddings are centered at the end of their corresponding prototype vectors on the surface of the ball. This approach allows us to learn meaningful, interpretable prototypes for the final classification. Results on several relation learning tasks show that our model significantly outperforms the previous state-of-the-art models. We further demonstrate the robustness of the encoder and the interpretability of prototypes with extensive experiments.
论文标题：
Prototypical Representation Learning for Relation Extraction
论文链接：
=aCgLmfhIy_f
谢雨桐：美国密歇根大学信息学院博士生，导师为梅俏竹教授。在此之前，她以ACM班级成员的身份在上海交通大学获得学士学位，师从俞勇教授和张伟楠教授。她的研究兴趣是针对显式和隐式的结构化数据的机器学习方法，特别是图、网络和文本等。(个人网站/)。
报告题目：
MARS：马尔可夫分子采样
在多目标药物发现中的应用
摘要：
Searching for novel molecules with desired chemical properties is crucial in drug discovery. Existing work focuses on developing neural models to generate either molecular sequences or chemical graphs. However, it remains a big challenge to find novel and diverse compounds satisfying several properties. In this paper, we propose MARS, a method for multi-objective drug molecule discovery. MARS is based on the idea of generating the chemical candidates by iteratively editing fragments of molecular graphs. To search for high-quality candidates, it employs Markov chain Monte Carlo sampling (MCMC) on molecules with an annealing scheme and an adaptive proposal. To further improve sample efficiency, MARS uses a graph neural network (GNN) to represent and select candidate edits, where the GNN is trained on-the-fly with samples from MCMC. Experiments show that MARS achieves state-of-the-art performance in various multi-objective settings where molecular bio-activity, drug-likeness, and synthesizability are considered. Remarkably, in the most challenging setting where all four objectives are simultaneously optimized, our approach outperforms previous methods significantly in comprehensive evaluations. The code is available at .
论文标题：
MARS: Markov Molecular Sampling for Multi-objective Drug Discovery
论文链接：
=kHSu4ebxFXY
王彬旭：圣路易斯华盛顿大学神经科学系三年级博士生，导师是Carlos R. Ponce教授(该实验室将于今年秋季迁往哈佛大学)。他于2018年毕业于北京大学元培学院，获得物理学学士学位。在大学期间，他接受了理论神经科学家陶乐天教授的计算神经科学培训。目前，他的研究重点是将神经网络可解释性、生成模型和优化工具与传统神经科学实验相结合，以理解在生物和人工神经网络中实现的视觉。(项目页面/
报告题目：
深度生成模型的几何分析与应用
摘要：
Generative adversarial networks (GANs) have emerged as a powerful unsupervised method to model the statistical patterns of real-world data sets, such as natural images. These networks are trained to map random inputs in their latent space to new samples representative of the learned data. However, the structure of the latent space is hard to intuit due to its high dimensionality and the non-linearity of the generator, which limits the usefulness of the models. Understanding the latent space requires a way to identify input codes for existing real-world images (inversion), and a way to identify directions with known image transformations (interpretability). Here, we use a geometric framework to address both issues simultaneously. We develop an architecture-agnostic method to compute the Riemannian metric of the image manifold created by GANs. The eigen-decomposition of the metric isolates axes that account for different levels of image variability. An empirical analysis of several pretrained GANs shows that image variation around each position is concentrated along surprisingly few major axes (the space is highly anisotropic) and the directions that create this large variation are similar at different positions in the space (the space is homogeneous). We show that many of the top eigenvectors correspond to interpretable transforms in the image space, with a substantial part of eigenspace corresponding to minor transforms which could be compressed out. This geometric understanding unifies key previous results related to GAN interpretability. We show that the use of this metric allows for more efficient optimization in the latent space (e.g. GAN inversion) and facilitates unsupervised discovery of interpretable axes. Our results illustrate that defining the geometry of the GAN image manifold can serve as a general framework for understanding GANs.
论文标题：
A Geometric Analysis of Deep Generative Image Models and Its Applications
论文链接：
.06006 ;
=GH7QRzUDdXG
直播结束后我们会邀请讲者在微信群中与大家答疑交流，请添加“AI TIME小助手（微信号：AITIME_HY）”，回复“phd2”，将拉您进“PhD交流群”！
AI TIME微信小助手
主 办：AI TIME 、AMiner
合作伙伴：智谱·AI、中国工程院知领直播、学堂在线、学术头条、biendata、数据派、 Ever链动、机器学习算法与自然语言处理
合作媒体：学术头条
『今日视频推荐』
AI TIME欢迎AI领域学者投稿，期待大家剖析学科历史发展和前沿技术。针对热门话题，我们将邀请专家一起论道。同时，我们也长期招募优质的撰稿人，顶级的平台需要顶级的你，
请将简历等信息发至yun.he@aminer.cn！
微信联系:AITIME_HY
AI TIME是清华大学计算机系一群关注人工智能发展，并有思想情怀的青年学者们创办的圈子,旨在发扬科学思辨精神，邀请各界人士对人工智能理论、算法、场景、应用的本质问题进行探索，加强思想碰撞，打造一个知识分享的聚集地。
更多资讯请扫码关注
我知道你在看哟
点击 阅读原文 了解更多精彩