Search Results for author: Xikun Zhang

Found 12 papers, 4 papers with code

On Dropping Clusters to Regularize Graph Convolutional Neural Networks

no code implementations ECCV 2020 Xikun Zhang, Chang Xu, DaCheng Tao

Dropout has been widely adopted to regularize graph convolutional networks (GCNs) by randomly zeroing entries of the node feature vectors and obtains promising performance on various tasks.

Action Recognition Skeleton Based Action Recognition

GreaseLM: Graph REASoning Enhanced Language Models for Question Answering

1 code implementation21 Jan 2022 Xikun Zhang, Antoine Bosselut, Michihiro Yasunaga, Hongyu Ren, Percy Liang, Christopher D. Manning, Jure Leskovec

Answering complex questions about textual narratives requires reasoning over both stated context and the world knowledge that underlies it.

Knowledge Graphs Open-Ended Question Answering

Hierarchical Prototype Networks for Continual Graph Representation Learning

no code implementations30 Nov 2021 Xikun Zhang, Dongjin Song, DaCheng Tao

Despite significant advances in graph representation learning, little attention has been paid to the more practical continual learning scenario in which new categories of nodes (e. g., new research areas in citation networks, or new types of products in co-purchasing networks) and their associated edges are continuously emerging, causing catastrophic forgetting on previous categories.

Continual Learning Graph Representation Learning

On the Opportunities and Risks of Foundation Models

3 code implementations16 Aug 2021 Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S. Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, Erik Brynjolfsson, Shyamal Buch, Dallas Card, Rodrigo Castellon, Niladri Chatterji, Annie Chen, Kathleen Creel, Jared Quincy Davis, Dora Demszky, Chris Donahue, Moussa Doumbouya, Esin Durmus, Stefano Ermon, John Etchemendy, Kawin Ethayarajh, Li Fei-Fei, Chelsea Finn, Trevor Gale, Lauren Gillespie, Karan Goel, Noah Goodman, Shelby Grossman, Neel Guha, Tatsunori Hashimoto, Peter Henderson, John Hewitt, Daniel E. Ho, Jenny Hong, Kyle Hsu, Jing Huang, Thomas Icard, Saahil Jain, Dan Jurafsky, Pratyusha Kalluri, Siddharth Karamcheti, Geoff Keeling, Fereshte Khani, Omar Khattab, Pang Wei Koh, Mark Krass, Ranjay Krishna, Rohith Kuditipudi, Ananya Kumar, Faisal Ladhak, Mina Lee, Tony Lee, Jure Leskovec, Isabelle Levent, Xiang Lisa Li, Xuechen Li, Tengyu Ma, Ali Malik, Christopher D. Manning, Suvir Mirchandani, Eric Mitchell, Zanele Munyikwa, Suraj Nair, Avanika Narayan, Deepak Narayanan, Ben Newman, Allen Nie, Juan Carlos Niebles, Hamed Nilforoshan, Julian Nyarko, Giray Ogut, Laurel Orr, Isabel Papadimitriou, Joon Sung Park, Chris Piech, Eva Portelance, Christopher Potts, aditi raghunathan, Rob Reich, Hongyu Ren, Frieda Rong, Yusuf Roohani, Camilo Ruiz, Jack Ryan, Christopher Ré, Dorsa Sadigh, Shiori Sagawa, Keshav Santhanam, Andy Shih, Krishnan Srinivasan, Alex Tamkin, Rohan Taori, Armin W. Thomas, Florian Tramèr, Rose E. Wang, William Wang, Bohan Wu, Jiajun Wu, Yuhuai Wu, Sang Michael Xie, Michihiro Yasunaga, Jiaxuan You, Matei Zaharia, Michael Zhang, Tianyi Zhang, Xikun Zhang, Yuhui Zhang, Lucia Zheng, Kaitlyn Zhou, Percy Liang

AI is undergoing a paradigm shift with the rise of models (e. g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.

Transfer Learning

Hierarchical Prototype Network for Continual Graph Representation Learning

no code implementations NeurIPS 2021 Xikun Zhang, Dongjin Song, DaCheng Tao

The key challenge is to incorporate the feature and topological information of new nodes in a continuous and effective manner such that performance over existing nodes is uninterrupted.

Continual Learning Graph Representation Learning

Do Language Embeddings Capture Scales?

no code implementations EMNLP (BlackboxNLP) 2020 Xikun Zhang, Deepak Ramachandran, Ian Tenney, Yanai Elazar, Dan Roth

Pretrained Language Models (LMs) have been shown to possess significant linguistic, common sense, and factual knowledge.

Common Sense Reasoning

Context Aware Graph Convolution for Skeleton-Based Action Recognition

no code implementations CVPR 2020 Xikun Zhang, Chang Xu, Dacheng Tao

Extensive experiments on two real-world datasets demonstrate the importance of context information and the effectiveness of the proposed CA-GCN in skeleton based action recognition.

Action Recognition Skeleton Based Action Recognition +1

Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks

1 code implementation3 Aug 2019 Srijan Kumar, Xikun Zhang, Jure Leskovec

However, existing dynamic embedding methods generate embeddings only when users take actions and do not explicitly model the future trajectory of the user/item in the embedding space.

Representation Learning

Learning Dynamic Embeddings from Temporal Interactions

no code implementations6 Dec 2018 Srijan Kumar, Xikun Zhang, Jure Leskovec

Here we present JODIE, a coupled recurrent model to jointly learn the dynamic embeddings of users and items from a sequence of user-item interactions.

Representation Learning

Graph Edge Convolutional Neural Networks for Skeleton Based Action Recognition

no code implementations16 May 2018 Xikun Zhang, Chang Xu, Xinmei Tian, DaCheng Tao

Considering the complementarity between graph node convolution and graph edge convolution, we additionally construct two hybrid neural networks to combine graph node convolutional neural network and graph edge convolutional neural network using shared intermediate layers.

Action Recognition Pose Estimation +2

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