2.3k citations · 3.8k across the 29 of their papers we have counts for
29 papers
TimeGraphs: Graph-based Temporal Reasoning
Paridhi Maheshwari, Hongyu Ren, Yanan Wang +2
Many real-world systems exhibit temporal, dynamic behaviors, which are captured as time series of complex agent interactions. To perform temporal reasoning, current methods primari…
Approximate Answering of Graph Queries
Michael Cochez, Dimitrios Alivanistos, Erik Arakelyan +6
Knowledge graphs (KGs) are inherently incomplete because of incomplete world knowledge and bias in what is the input to the KG. Additionally, world knowledge constantly expands and…
Enabling tabular deep learning when with an auxiliary knowledge graph
Camilo Ruiz, Hongyu Ren, Kexin Huang +1
Machine learning models exhibit strong performance on datasets with abundant labeled samples. However, for tabular datasets with extremely high -dimensional features but limited…
PRODIGY: Enabling In-context Learning Over Graphs
Qian Huang, Hongyu Ren, Peng Chen +4
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. While…
Neural Graph Reasoning: Complex Logical Query Answering Meets Graph Databases
Hongyu Ren, Mikhail Galkin, Michael Cochez +2
Complex logical query answering (CLQA) is a recently emerged task of graph machine learning that goes beyond simple one-hop link prediction and solves a far more complex task of mu…
Inductive Logical Query Answering in Knowledge Graphs
Mikhail Galkin, Zhaocheng Zhu, Hongyu Ren +1
Formulating and answering logical queries is a standard communication interface for knowledge graphs (KGs). Alleviating the notorious incompleteness of real-world KGs, neural metho…
Holistic Evaluation of Language Models
Percy Liang, Rishi Bommasani, Tony Lee +47
Language models (LMs) are becoming the foundation for almost all major language technologies, but their capabilities, limitations, and risks are not well understood. We present Hol…
Deep Bidirectional Language-Knowledge Graph Pretraining
Michihiro Yasunaga, Antoine Bosselut, Hongyu Ren +4
Pretraining a language model (LM) on text has been shown to help various downstream NLP tasks. Recent works show that a knowledge graph (KG) can complement text data, offering stru…
Few-shot Relational Reasoning via Connection Subgraph Pretraining
Qian Huang, Hongyu Ren, Jure Leskovec
Few-shot knowledge graph (KG) completion task aims to perform inductive reasoning over the KG: given only a few support triplets of a new relation (e.g., (chop,,…
TripleE: Easy Domain Generalization via Episodic Replay
Xiaomeng Li, Hongyu Ren, Huifeng Yao +1
Learning how to generalize the model to unseen domains is an important area of research. In this paper, we propose TripleE, and the main idea is to encourage the network to focus o…
Leveraging Large (Visual) Language Models for Robot 3D Scene Understanding
William Chen, Siyi Hu, Rajat Talak +1
Abstract semantic 3D scene understanding is a problem of critical importance in robotics. As robots still lack the common-sense knowledge about household objects and locations of a…
VQA-GNN: Reasoning with Multimodal Knowledge via Graph Neural Networks for Visual Question Answering
Yanan Wang, Michihiro Yasunaga, Hongyu Ren +2
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…
GreaseLM: Graph REASoning Enhanced Language Models for Question Answering
Xikun Zhang, Antoine Bosselut, Michihiro Yasunaga +4
Answering complex questions about textual narratives requires reasoning over both stated context and the world knowledge that underlies it. However, pretrained language models (LM)…
SMORE: Knowledge Graph Completion and Multi-hop Reasoning in Massive Knowledge Graphs
Hongyu Ren, Hanjun Dai, Bo Dai +4
Knowledge graphs (KGs) capture knowledge in the form of head--relation--tail triples and are a crucial component in many AI systems. There are two important reasoning tasks on KGs:…
Modeling Heterogeneous Hierarchies with Relation-specific Hyperbolic Cones
Yushi Bai, Rex Ying, Hongyu Ren +1
Hierarchical relations are prevalent and indispensable for organizing human knowledge captured by a knowledge graph (KG). The key property of hierarchical relations is that they in…
On the Opportunities and Risks of Foundation Models
Rishi Bommasani, Drew A. Hudson, Ehsan Adeli +111
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.…
Combiner: Full Attention Transformer with Sparse Computation Cost
Hongyu Ren, Hanjun Dai, Zihang Dai +4
Transformers provide a class of expressive architectures that are extremely effective for sequence modeling. However, the key limitation of transformers is their quadratic memory a…
QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering
Michihiro Yasunaga, Hongyu Ren, Antoine Bosselut +2
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 answe…
OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs
Weihua Hu, Matthias Fey, Hongyu Ren +3
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 a…
Graph Information Bottleneck
Tailin Wu, Hongyu Ren, Pan Li +1
Representation learning of graph-structured data is challenging because both graph structure and node features carry important information. Graph Neural Networks (GNNs) provide an…
Beta Embeddings for Multi-Hop Logical Reasoning in Knowledge Graphs
Hongyu Ren, Jure Leskovec
One of the fundamental problems in Artificial Intelligence is to perform complex multi-hop logical reasoning over the facts captured by a knowledge graph (KG). This problem is chal…
OCEAN: Online Task Inference for Compositional Tasks with Context Adaptation
Hongyu Ren, Yuke Zhu, Jure Leskovec +2
Real-world tasks often exhibit a compositional structure that contains a sequence of simpler sub-tasks. For instance, opening a door requires reaching, grasping, rotating, and pull…
Open Graph Benchmark: Datasets for Machine Learning on Graphs
Weihua Hu, Matthias Fey, Marinka Zitnik +5
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…
Query2box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddings
Hongyu Ren, Weihua Hu, Jure Leskovec
Answering complex logical queries on large-scale incomplete knowledge graphs (KGs) is a fundamental yet challenging task. Recently, a promising approach to this problem has been to…