no code implementations • 30 Mar 2024 • Jinwei Yao, Kaiqi Chen, Kexun Zhang, Jiaxuan You, Binhang Yuan, Zeke Wang, Tao Lin
Decoding using tree search can greatly enhance the inference quality for transformer-based Large Language Models (LLMs).
no code implementations • 26 Feb 2024 • Jupinder Parmar, Shrimai Prabhumoye, Joseph Jennings, Mostofa Patwary, Sandeep Subramanian, Dan Su, Chen Zhu, Deepak Narayanan, Aastha Jhunjhunwala, Ayush Dattagupta, Vibhu Jawa, Jiwei Liu, Ameya Mahabaleshwarkar, Osvald Nitski, Annika Brundyn, James Maki, Miguel Martinez, Jiaxuan You, John Kamalu, Patrick Legresley, Denys Fridman, Jared Casper, Ashwath Aithal, Oleksii Kuchaiev, Mohammad Shoeybi, Jonathan Cohen, Bryan Catanzaro
We introduce Nemotron-4 15B, a 15-billion-parameter large multilingual language model trained on 8 trillion text tokens.
no code implementations • 22 Feb 2024 • Rex Ying, Tianyu Fu, Andrew Wang, Jiaxuan You, Yu Wang, Jure Leskovec
SPMiner combines graph neural networks, order embedding space, and an efficient search strategy to identify network subgraph patterns that appear most frequently in the target graph.
no code implementations • 7 Dec 2023 • Matthias Fey, Weihua Hu, Kexin Huang, Jan Eric Lenssen, Rishabh Ranjan, Joshua Robinson, Rex Ying, Jiaxuan You, Jure Leskovec
The core idea is to view relational databases as a temporal, heterogeneous graph, with a node for each row in each table, and edges specified by primary-foreign key links.
no code implementations • 25 Oct 2023 • Zizhao Zhang, Yi Yang, Lutong Zou, He Wen, Tao Feng, Jiaxuan You
Benefiting from high-quality datasets and standardized evaluation metrics, machine learning (ML) has achieved sustained progress and widespread applications.
1 code implementation • ICLR 2022 • Kaidi Cao, Jiaxuan You, Jure Leskovec
Here we introduce a novel relational multi-task learning setting where we leverage data point labels from auxiliary tasks to make more accurate predictions on the new task.
1 code implementation • 14 Mar 2023 • Kaidi Cao, Jiaxuan You, Jiaju Liu, Jure Leskovec
Experiments demonstrate that (i) our proposed task embedding can be computed efficiently, and that tasks with similar embeddings have similar best-performing architectures; (ii) AutoTransfer significantly improves search efficiency with the transferred design priors, reducing the number of explored architectures by an order of magnitude.
1 code implementation • 21 Oct 2022 • Shirley Wu, Jiaxuan You, Jure Leskovec, Rex Ying
FALCON features 1) a task-agnostic module, which performs message passing on the design graph via a Graph Neural Network (GNN), and 2) a task-specific module, which conducts label propagation of the known model performance information on the design graph.
2 code implementations • 15 Aug 2022 • Jiaxuan You, Tianyu Du, Jure Leskovec
Finally, we propose a scalable and efficient training approach for dynamic GNNs via incremental training and meta-learning.
1 code implementation • 30 Mar 2022 • Tim Poštuvan, Jiaxuan You, Mohammadreza Banaei, Rémi Lebret, Jure Leskovec
To mitigate these limitations, we propose Adaptive Grid Search (AdaGrid), which dynamically adjusts the edge message ratio during training.
2 code implementations • 16 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.
1 code implementation • 25 Jan 2021 • Jiaxuan You, Jonathan Gomes-Selman, Rex Ying, Jure Leskovec
However, the expressive power of existing GNNs is upper-bounded by the 1-Weisfeiler-Lehman (1-WL) graph isomorphism test, which means GNNs that are not able to predict node clustering coefficients and shortest path distances, and cannot differentiate between different d-regular graphs.
2 code implementations • NeurIPS 2020 • Jiaxuan You, Rex Ying, Jure Leskovec
However, current research focuses on proposing and evaluating specific architectural designs of GNNs, as opposed to studying the more general design space of GNNs that consists of a Cartesian product of different design dimensions, such as the number of layers or the type of the aggregation function.
1 code implementation • NeurIPS 2020 • Jiaxuan You, Xiaobai Ma, Daisy Yi Ding, Mykel Kochenderfer, Jure Leskovec
GRAPE tackles the missing data problem using a graph representation, where the observations and features are viewed as two types of nodes in a bipartite graph, and the observed feature values as edges.
1 code implementation • 19 Sep 2020 • Bin Wang, Guangtao Wang, Jing Huang, Jiaxuan You, Jure Leskovec, C. -C. Jay Kuo
Here, we propose to study the inductive learning setting for CKG completion where unseen entities may present at test time.
3 code implementations • ICML 2020 • Jiaxuan You, Jure Leskovec, Kaiming He, Saining Xie
Neural networks are often represented as graphs of connections between neurons.
no code implementations • 6 Jul 2020 • Rex, Ying, Zhaoyu Lou, Jiaxuan You, Chengtao Wen, Arquimedes Canedo, Jure Leskovec
Subgraph matching is the problem of determining the presence and location(s) of a given query graph in a large target graph.
1 code implementation • NeurIPS 2019 • Jiaxuan You, Haoze Wu, Clark Barrett, Raghuram Ramanujan, Jure Leskovec
The Boolean Satisfiability (SAT) problem is the canonical NP-complete problem and is fundamental to computer science, with a wide array of applications in planning, verification, and theorem proving.
2 code implementations • 11 Jun 2019 • Jiaxuan You, Rex Ying, Jure Leskovec
Learning node embeddings that capture a node's position within the broader graph structure is crucial for many prediction tasks on graphs.
no code implementations • 9 Jun 2019 • Zhihao Jia, Sina Lin, Rex Ying, Jiaxuan You, Jure Leskovec, Alex Aiken
Graph Neural Networks (GNNs) are based on repeated aggregations of information across nodes' neighbors in a graph.
no code implementations • 8 Apr 2019 • Jiaxuan You, Yichen Wang, Aditya Pal, Pong Eksombatchai, Chuck Rosenberg, Jure Leskovec
Recommender systems that can learn from cross-session data to dynamically predict the next item a user will choose are crucial for online platforms.
10 code implementations • NeurIPS 2019 • Rex Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, Jure Leskovec
We formulate GNNExplainer as an optimization task that maximizes the mutual information between a GNN's prediction and distribution of possible subgraph structures.
BIG-bench Machine Learning Explainable artificial intelligence +2
14 code implementations • NeurIPS 2018 • Rex Ying, Jiaxuan You, Christopher Morris, Xiang Ren, William L. Hamilton, Jure Leskovec
Recently, graph neural networks (GNNs) have revolutionized the field of graph representation learning through effectively learned node embeddings, and achieved state-of-the-art results in tasks such as node classification and link prediction.
Ranked #1 on Graph Classification on REDDIT-MULTI-12K
2 code implementations • NeurIPS 2018 • Jiaxuan You, Bowen Liu, Rex Ying, Vijay Pande, Jure Leskovec
Generating novel graph structures that optimize given objectives while obeying some given underlying rules is fundamental for chemistry, biology and social science research.
3 code implementations • ICML 2018 • Jiaxuan You, Rex Ying, Xiang Ren, William L. Hamilton, Jure Leskovec
Modeling and generating graphs is fundamental for studying networks in biology, engineering, and social sciences.
1 code implementation • AAAI 2017 2017 • Jiaxuan You, Xiaocheng Li, Melvin Low, David Lobell, Stefano Ermon
Agricultural monitoring, especially in developing countries, can help prevent famine and support humanitarian efforts.