no code implementations • 6 Jan 2024 • Paridhi Maheshwari, Hongyu Ren, Yanan Wang, Rok Sosic, Jure Leskovec
The results demonstrate both robustness and efficiency of TimeGraphs on a range of temporal reasoning tasks.
1 code implementation • 14 Nov 2023 • Lin Xu, Zhiyuan Hu, Daquan Zhou, Hongyu Ren, Zhen Dong, Kurt Keutzer, See Kiong Ng, Jiashi Feng
Large Language Models (LLMs) have marked a significant advancement in the field of natural language processing, demonstrating exceptional capabilities in reasoning, tool usage, and memory.
no code implementations • 12 Aug 2023 • Michael Cochez, Dimitrios Alivanistos, Erik Arakelyan, Max Berrendorf, Daniel Daza, Mikhail Galkin, Pasquale Minervini, Mathias Niepert, Hongyu Ren
We will first provide an overview of the different query types which can be supported by these methods and datasets typically used for evaluation, as well as an insight into their limitations.
no code implementations • 7 Jun 2023 • Camilo Ruiz, Hongyu Ren, Kexin Huang, Jure Leskovec
However, for tabular datasets with extremely high $d$-dimensional features but limited $n$ samples (i. e. $d \gg n$), machine learning models struggle to achieve strong performance due to the risk of overfitting.
no code implementations • NeurIPS 2023 • Qian Huang, Hongyu Ren, Peng Chen, Gregor Kržmanc, Daniel Zeng, Percy Liang, Jure Leskovec
In-context learning is the ability of a pretrained model to adapt to novel and diverse downstream tasks by conditioning on prompt examples, without optimizing any parameters.
1 code implementation • 26 Mar 2023 • Hongyu Ren, Mikhail Galkin, Michael Cochez, Zhaocheng Zhu, Jure Leskovec
Extending the idea of graph databases (graph DBs), NGDB consists of a Neural Graph Storage and a Neural Graph Engine.
1 code implementation • 16 Nov 2022 • Percy Liang, Rishi Bommasani, Tony Lee, Dimitris Tsipras, Dilara Soylu, Michihiro Yasunaga, Yian Zhang, Deepak Narayanan, Yuhuai Wu, Ananya Kumar, Benjamin Newman, Binhang Yuan, Bobby Yan, Ce Zhang, Christian Cosgrove, Christopher D. Manning, Christopher Ré, Diana Acosta-Navas, Drew A. Hudson, Eric Zelikman, Esin Durmus, Faisal Ladhak, Frieda Rong, Hongyu Ren, Huaxiu Yao, Jue Wang, Keshav Santhanam, Laurel Orr, Lucia Zheng, Mert Yuksekgonul, Mirac Suzgun, Nathan Kim, Neel Guha, Niladri Chatterji, Omar Khattab, Peter Henderson, Qian Huang, Ryan Chi, Sang Michael Xie, Shibani Santurkar, Surya Ganguli, Tatsunori Hashimoto, Thomas Icard, Tianyi Zhang, Vishrav Chaudhary, William Wang, Xuechen Li, Yifan Mai, Yuhui Zhang, Yuta Koreeda
We present Holistic Evaluation of Language Models (HELM) to improve the transparency of language models.
1 code implementation • 17 Oct 2022 • Michihiro Yasunaga, Antoine Bosselut, Hongyu Ren, Xikun Zhang, Christopher D Manning, Percy Liang, Jure Leskovec
Pretraining a language model (LM) on text has been shown to help various downstream NLP tasks.
Ranked #1 on Riddle Sense on RiddleSense
1 code implementation • 13 Oct 2022 • Qian Huang, Hongyu Ren, Jure Leskovec
Our pretrained model can then be directly applied to target few-shot tasks on without the need for training few-shot tasks.
1 code implementation • 13 Oct 2022 • Mikhail Galkin, Zhaocheng Zhu, Hongyu Ren, Jian Tang
Exploring the efficiency--effectiveness trade-off, we find the inductive relational structure representation method generally achieves higher performance, while the inductive node representation method is able to answer complex queries in the inference-only regime without any training on queries and scales to graphs of millions of nodes.
1 code implementation • 4 Oct 2022 • Xiaomeng Li, Hongyu Ren, Huifeng Yao, Ziwei Liu
In this paper, we propose TripleE, and the main idea is to encourage the network to focus on training on subsets (learning with replay) and enlarge the data space in learning on subsets.
no code implementations • ICCV 2023 • Yanan Wang, Michihiro Yasunaga, Hongyu Ren, Shinya Wada, Jure Leskovec
Visual question answering (VQA) requires systems to perform concept-level reasoning by unifying unstructured (e. g., the context in question and answer; "QA context") and structured (e. g., knowledge graph for the QA context and scene; "concept graph") multimodal knowledge.
1 code implementation • 21 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.
1 code implementation • 28 Oct 2021 • Hongyu Ren, Hanjun Dai, Bo Dai, Xinyun Chen, Denny Zhou, Jure Leskovec, Dale Schuurmans
There are two important reasoning tasks on KGs: (1) single-hop knowledge graph completion, which involves predicting individual links in the KG; and (2), multi-hop reasoning, where the goal is to predict which KG entities satisfy a given logical query.
1 code implementation • NeurIPS 2021 • Yushi Bai, Rex Ying, Hongyu Ren, Jure Leskovec
Here we present ConE (Cone Embedding), a KG embedding model that is able to simultaneously model multiple hierarchical as well as non-hierarchical relations in a knowledge graph.
Ranked #1 on Ancestor-descendant prediction on WN18RR
Ancestor-descendant prediction Knowledge Graph Completion +2
no code implementations • ICLR 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.
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.
2 code implementations • NeurIPS 2021 • Hongyu Ren, Hanjun Dai, Zihang Dai, Mengjiao Yang, Jure Leskovec, Dale Schuurmans, Bo Dai
However, the key limitation of transformers is their quadratic memory and time complexity $\mathcal{O}(L^2)$ with respect to the sequence length in attention layers, which restricts application in extremely long sequences.
Ranked #2 on Language Modelling on Wiki-40B
4 code implementations • NAACL 2021 • Michihiro Yasunaga, Hongyu Ren, Antoine Bosselut, Percy Liang, Jure Leskovec
The problem of answering questions using knowledge from pre-trained language models (LMs) and knowledge graphs (KGs) presents two challenges: given a QA context (question and answer choice), methods need to (i) identify relevant knowledge from large KGs, and (ii) perform joint reasoning over the QA context and KG.
Ranked #2 on Riddle Sense on RiddleSense
6 code implementations • 17 Mar 2021 • Weihua Hu, Matthias Fey, Hongyu Ren, Maho Nakata, Yuxiao Dong, Jure Leskovec
Enabling effective and efficient machine learning (ML) over large-scale graph data (e. g., graphs with billions of edges) can have a great impact on both industrial and scientific applications.
Ranked #1 on Knowledge Graphs on WikiKG90M-LSC
1 code implementation • NeurIPS 2020 • Tailin Wu, Hongyu Ren, Pan Li, Jure Leskovec
We design two sampling algorithms for structural regularization and instantiate the GIB principle with two new models: GIB-Cat and GIB-Bern, and demonstrate the benefits by evaluating the resilience to adversarial attacks.
7 code implementations • NeurIPS 2020 • Hongyu Ren, Jure Leskovec
Logical operations are performed in the embedding space by neural operators over the probabilistic embeddings.
Ranked #5 on Complex Query Answering on NELL-995
1 code implementation • 17 Aug 2020 • Hongyu Ren, Yuke Zhu, Jure Leskovec, Anima Anandkumar, Animesh Garg
We propose a variational inference framework OCEAN to perform online task inference for compositional tasks.
20 code implementations • NeurIPS 2020 • Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, Jure Leskovec
We present the Open Graph Benchmark (OGB), a diverse set of challenging and realistic benchmark datasets to facilitate scalable, robust, and reproducible graph machine learning (ML) research.
Ranked #1 on Link Property Prediction on ogbl-citation2
4 code implementations • 17 Feb 2020 • Hongwei Wang, Hongyu Ren, Jure Leskovec
Specifically, two kinds of neighborhood topology are modeled for a given entity pair under the relational message passing framework: (1) Relational context, which captures the relation types of edges adjacent to the given entity pair; (2) Relational paths, which characterize the relative position between the given two entities in the knowledge graph.
6 code implementations • ICLR 2020 • Hongyu Ren, Weihua Hu, Jure Leskovec
Our main insight is that queries can be embedded as boxes (i. e., hyper-rectangles), where a set of points inside the box corresponds to a set of answer entities of the query.
Ranked #4 on Complex Query Answering on FB15k-237
2 code implementations • NeurIPS 2018 • Shengjia Zhao, Hongyu Ren, Arianna Yuan, Jiaming Song, Noah Goodman, Stefano Ermon
In high dimensional settings, density estimation algorithms rely crucially on their inductive bias.
1 code implementation • NeurIPS 2018 • Jiaming Song, Hongyu Ren, Dorsa Sadigh, Stefano Ermon
Imitation learning algorithms can be used to learn a policy from expert demonstrations without access to a reward signal.
1 code implementation • 27 May 2018 • Hongyu Ren, Russell Stewart, Jiaming Song, Volodymyr Kuleshov, Stefano Ermon
Constraint-based learning reduces the burden of collecting labels by having users specify general properties of structured outputs, such as constraints imposed by physical laws.
no code implementations • 14 Dec 2017 • Hongyu Ren, Diqi Chen, Yizhou Wang
The evaluator predicts perceptual score by extracting feature representations from the distorted and restored patches to measure GoR.