Jure Leskovec
Stanford University
Machine learningData miningGraphGraph (abstract data type)World Wide WebArtificial intelligenceSet (psychology)Set (abstract data type)Structure (mathematical logic)Information retrievalData scienceTask (project management)Computer scienceEmbeddingNode (networking)ScalabilitySocial mediaFeature learningCluster analysisTheoretical computer scienceSocial network
Publications 359
#1Gabriele Corso (University of Cambridge)H-Index: 4
#2Zhitao Ying (Stanford University)H-Index: 6
Last. Pietro Liò (University of Cambridge)H-Index: 57
view all 6 authors...
#1Hongyu RenH-Index: 8
#2Hanjun DaiH-Index: 27
Last. Dale SchuurmansH-Index: 57
view all 7 authors...
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: (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. Embedding-based methods solve both tasks by first computing an embedding for each entity and relation, then using ...
#3Hongyu Ren (Stanford University)H-Index: 8
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 induce a partial ordering over the entities, which needs to be modeled in order to allow for hierarchical reasoning. However, current KG embeddings can model only a single global hierarchy (single global partial ordering) and fail to model multiple heterogeneous hierarchies that exist in a single KG. Here we present ConE...
#1Sheng Wang (Stanford University)H-Index: 11
Last. Russ B. Altman (Stanford University)H-Index: 101
view all 9 authors...
Single cell technologies are rapidly generating large amounts of data that enables us to understand biological systems at single-cell resolution. However, joint analysis of datasets generated by independent labs remains challenging due to a lack of consistent terminology to describe cell types. Here, we present OnClass, an algorithm and accompanying software for automatically classifying cells into cell types that are part of the controlled vocabulary that forms the Cell Ontology. A key advantag...
#1Robert West (EPFL: École Polytechnique Fédérale de Lausanne)H-Index: 20
#2Jure Leskovec (Stanford University)H-Index: 121
Last. Christopher Potts (Stanford University)H-Index: 44
view all 3 authors...
Deceased public figures are often said to live on in collective memory. We quantify this phenomenon by tracking mentions of 2,362 public figures in English-language online news and social media (Twitter) 1 y before and after death. We measure the sharp spike and rapid decay of attention following death and model collective memory as a composition of communicative and cultural memory. Clustering reveals four patterns of postmortem memory, and regression analysis shows that boosts in media attenti...
#1Gabriele Corso (MIT: Massachusetts Institute of Technology)
#2Rex YingH-Index: 18
Last. Pietro LiòH-Index: 57
view all 6 authors...
The development of data-dependent heuristics and representations for biological sequences that reflect their evolutionary distance is critical for large-scale biological research. However, popular machine learning approaches, based on continuous Euclidean spaces, have struggled with the discrete combinatorial formulation of the edit distance that models evolution and the hierarchical relationship that characterises real-world datasets. We present Neural Distance Embeddings (NeuroSEED), a general...
#1Michihiro Yasunaga (Stanford University)H-Index: 18
#2Jure Leskovec (Stanford University)H-Index: 121
Last. Percy Liang (Stanford University)H-Index: 71
view all 3 authors...
Training a model for grammatical error correction (GEC) requires a set of labeled ungrammatical / grammatical sentence pairs, but manually annotating such pairs can be expensive. Recently, the Break-It-Fix-It (BIFI) framework has demonstrated strong results on learning to repair a broken program without any labeled examples, but this relies on a perfect critic (e.g., a compiler) that returns whether an example is valid or not, which does not exist for the GEC task. In this work, we show how to l...
#1Darian Hadjiabadi (Stanford University)H-Index: 5
#2Matthew Lovett-Barron (UCSD: University of California, San Diego)H-Index: 12
Last. Ivan Soltesz (Stanford University)H-Index: 74
view all 10 authors...
Neurological and psychiatric disorders are associated with pathological neural dynamics. The fundamental connectivity patterns of cell-cell communication networks that enable pathological dynamics to emerge remain unknown. Here, we studied epileptic circuits using a newly developed computational pipeline that leveraged single-cell calcium imaging of larval zebrafish and chronically epileptic mice, biologically constrained effective connectivity modeling, and higher-order motif-focused network an...
#1Rishi BommasaniH-Index: 3
#2Drew A. HudsonH-Index: 9
Last. Emma BrunskillH-Index: 36
view all 114 authors...
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. We call these models foundation models to underscore their critically central yet incomplete character. This report provides a thorough account of the opportunities and risks of foundation models, ranging from their capabilities (e.g., language, vision, robotics, reasoning, human interaction) and technical principles(...
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