Search Results for author: William L. Hamilton

Found 47 papers, 31 papers with code

Distilling Structured Knowledge for Text-Based Relational Reasoning

no code implementations EMNLP 2020 Jin Dong, Marc-Antoine Rondeau, William L. Hamilton

There is an increasing interest in developing text-based relational reasoning systems, which are capable of systematically reasoning about the relationships between entities mentioned in a text.

Contrastive Learning Knowledge Distillation +2

Edge-similarity-aware Graph Neural Networks

1 code implementation20 Sep 2021 Vincent Mallet, Carlos G. Oliver, William L. Hamilton

For instance, the 3D structure of RNA can be efficiently represented as $\textit{2. 5D graphs}$, graphs whose nodes are nucleotides and edges represent chemical interactions.

RNAglib: A Python Package for RNA 2.5D Graphs

1 code implementation9 Sep 2021 Vincent Mallet, Carlos Oliver, Jonathan Broadbent, William L. Hamilton, Jérôme Waldispühl

RNA 3D architectures are stabilized by sophisticated networks of (non-canonical) base pair interactions, which can be conveniently encoded as multi-relational graphs and efficiently exploited by graph theoretical approaches and recent progresses in machine learning techniques.

BIG-bench Machine Learning

Ego-GNNs: Exploiting Ego Structures in Graph Neural Networks

no code implementations22 Jul 2021 Dylan Sandfelder, Priyesh Vijayan, William L. Hamilton

Graph neural networks (GNNs) have achieved remarkable success as a framework for deep learning on graph-structured data.

Inductive Bias Node Classification

Rethinking Graph Transformers with Spectral Attention

1 code implementation NeurIPS 2021 Devin Kreuzer, Dominique Beaini, William L. Hamilton, Vincent Létourneau, Prudencio Tossou

Here, we present the $\textit{Spectral Attention Network}$ (SAN), which uses a learned positional encoding (LPE) that can take advantage of the full Laplacian spectrum to learn the position of each node in a given graph.

Online Adversarial Attacks

1 code implementation ICLR 2022 Andjela Mladenovic, Avishek Joey Bose, Hugo Berard, William L. Hamilton, Simon Lacoste-Julien, Pascal Vincent, Gauthier Gidel

Adversarial attacks expose important vulnerabilities of deep learning models, yet little attention has been paid to settings where data arrives as a stream.

Adversarial Attack

Exploring the Limits of Few-Shot Link Prediction in Knowledge Graphs

no code implementations EACL 2021 Dora Jambor, Komal Teru, Joelle Pineau, William L. Hamilton

Real-world knowledge graphs are often characterized by low-frequency relations - a challenge that has prompted an increasing interest in few-shot link prediction methods.

Knowledge Graphs Link Prediction

Neural representation and generation for RNA secondary structures

no code implementations ICLR 2021 Zichao Yan, William L. Hamilton, Mathieu Blanchette

Our work is concerned with the generation and targeted design of RNA, a type of genetic macromolecule that can adopt complex structures which influence their cellular activities and functions.

Drug Discovery Inductive Bias +1

GraphLog: A Benchmark for Measuring Logical Generalization in Graph Neural Networks

1 code implementation1 Jan 2021 Koustuv Sinha, Shagun Sodhani, Joelle Pineau, William L. Hamilton

In this work, we study the logical generalization capabilities of GNNs by designing a benchmark suite grounded in first-order logic.

Continual Learning Knowledge Graphs +1

TeMP: Temporal Message Passing for Temporal Knowledge Graph Completion

1 code implementation EMNLP 2020 Jiapeng Wu, Meng Cao, Jackie Chi Kit Cheung, William L. Hamilton

Our analysis also reveals important sources of variability both within and across TKG datasets, and we introduce several simple but strong baselines that outperform the prior state of the art in certain settings.

Imputation Temporal Knowledge Graph Completion

Structure Aware Negative Sampling in Knowledge Graphs

1 code implementation EMNLP 2020 Kian Ahrabian, Aarash Feizi, Yasmin Salehi, William L. Hamilton, Avishek Joey Bose

Learning low-dimensional representations for entities and relations in knowledge graphs using contrastive estimation represents a scalable and effective method for inferring connectivity patterns.

Contrastive Learning Knowledge Graphs

VeRNAl: Mining RNA Structures for Fuzzy Base Pairing Network Motifs

2 code implementations1 Sep 2020 Carlos Oliver, Vincent Mallet, Pericles Philippopoulos, William L. Hamilton, Jerome Waldispuhl

State of the art methods solve special cases of the motif problem by constraining the structural variability in occurrences of a motif, and narrowing the substructure search space.

Clustering Graph Representation Learning

Adversarial Example Games

1 code implementation NeurIPS 2020 Avishek Joey Bose, Gauthier Gidel, Hugo Berard, Andre Cianflone, Pascal Vincent, Simon Lacoste-Julien, William L. Hamilton

We introduce Adversarial Example Games (AEG), a framework that models the crafting of adversarial examples as a min-max game between a generator of attacks and a classifier.

Learning an Unreferenced Metric for Online Dialogue Evaluation

1 code implementation ACL 2020 Koustuv Sinha, Prasanna Parthasarathi, Jasmine Wang, Ryan Lowe, William L. Hamilton, Joelle Pineau

Evaluating the quality of a dialogue interaction between two agents is a difficult task, especially in open-domain chit-chat style dialogue.

Dialogue Evaluation

Evaluating Logical Generalization in Graph Neural Networks

1 code implementation ICML Workshop LifelongML 2020 Koustuv Sinha, Shagun Sodhani, Joelle Pineau, William L. Hamilton

Recent research has highlighted the role of relational inductive biases in building learning agents that can generalize and reason in a compositional manner.

Continual Learning Knowledge Graphs +2

Latent Variable Modelling with Hyperbolic Normalizing Flows

1 code implementation ICML 2020 Avishek Joey Bose, Ariella Smofsky, Renjie Liao, Prakash Panangaden, William L. Hamilton

One effective solution is the use of normalizing flows \cut{defined on Euclidean spaces} to construct flexible posterior distributions.

Density Estimation Variational Inference

Structural Inductive Biases in Emergent Communication

no code implementations4 Feb 2020 Agnieszka Słowik, Abhinav Gupta, William L. Hamilton, Mateja Jamnik, Sean B. Holden, Christopher Pal

In order to communicate, humans flatten a complex representation of ideas and their attributes into a single word or a sentence.

Representation Learning

Towards Graph Representation Learning in Emergent Communication

no code implementations24 Jan 2020 Agnieszka Słowik, Abhinav Gupta, William L. Hamilton, Mateja Jamnik, Sean B. Holden

Recent findings in neuroscience suggest that the human brain represents information in a geometric structure (for instance, through conceptual spaces).

Graph Representation Learning

Inductive Relation Prediction by Subgraph Reasoning

8 code implementations ICML 2020 Komal K. Teru, Etienne Denis, William L. Hamilton

The dominant paradigm for relation prediction in knowledge graphs involves learning and operating on latent representations (i. e., embeddings) of entities and relations.

Inductive Bias Inductive knowledge graph completion +4

CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from Text

5 code implementations IJCNLP 2019 Koustuv Sinha, Shagun Sodhani, Jin Dong, Joelle Pineau, William L. Hamilton

The recent success of natural language understanding (NLU) systems has been troubled by results highlighting the failure of these models to generalize in a systematic and robust way.

Inductive logic programming Natural Language Understanding +2

Neural Transfer Learning for Cry-based Diagnosis of Perinatal Asphyxia

no code implementations24 Jun 2019 Charles C. Onu, Jonathan Lebensold, William L. Hamilton, Doina Precup

Despite continuing medical advances, the rate of newborn morbidity and mortality globally remains high, with over 6 million casualties every year.

Transfer Learning

Generalizable Adversarial Attacks with Latent Variable Perturbation Modelling

no code implementations26 May 2019 Avishek Joey Bose, Andre Cianflone, William L. Hamilton

Adversarial attacks on deep neural networks traditionally rely on a constrained optimization paradigm, where an optimization procedure is used to obtain a single adversarial perturbation for a given input example.

Compositional Fairness Constraints for Graph Embeddings

1 code implementation25 May 2019 Avishek Joey Bose, William L. Hamilton

Learning high-quality node embeddings is a key building block for machine learning models that operate on graph data, such as social networks and recommender systems.

Fairness Graph Embedding +1

Compositional Language Understanding with Text-based Relational Reasoning

2 code implementations7 Nov 2018 Koustuv Sinha, Shagun Sodhani, William L. Hamilton, Joelle Pineau

Neural networks for natural language reasoning have largely focused on extractive, fact-based question-answering (QA) and common-sense inference.

Common Sense Reasoning Inductive Bias +3

Deep Graph Infomax

11 code implementations ICLR 2019 Petar Veličković, William Fedus, William L. Hamilton, Pietro Liò, Yoshua Bengio, R. Devon Hjelm

We present Deep Graph Infomax (DGI), a general approach for learning node representations within graph-structured data in an unsupervised manner.

General Classification Node Classification

Hierarchical Graph Representation Learning with Differentiable Pooling

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.

General Classification Graph Classification +3

Graph Convolutional Neural Networks for Web-Scale Recommender Systems

5 code implementations6 Jun 2018 Rex Ying, Ruining He, Kai-Feng Chen, Pong Eksombatchai, William L. Hamilton, Jure Leskovec

We develop a data-efficient Graph Convolutional Network (GCN) algorithm PinSage, which combines efficient random walks and graph convolutions to generate embeddings of nodes (i. e., items) that incorporate both graph structure as well as node feature information.

Recommendation Systems

Embedding Logical Queries on Knowledge Graphs

5 code implementations NeurIPS 2018 William L. Hamilton, Payal Bajaj, Marinka Zitnik, Dan Jurafsky, Jure Leskovec

Learning low-dimensional embeddings of knowledge graphs is a powerful approach used to predict unobserved or missing edges between entities.

Complex Query Answering

Community Interaction and Conflict on the Web

no code implementations9 Mar 2018 Srijan Kumar, William L. Hamilton, Jure Leskovec, Dan Jurafsky

Here we study intercommunity interactions across 36, 000 communities on Reddit, examining cases where users of one community are mobilized by negative sentiment to comment in another community.

GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models

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.

Graph Generation

Representation Learning on Graphs: Methods and Applications

no code implementations17 Sep 2017 William L. Hamilton, Rex Ying, Jure Leskovec

Machine learning on graphs is an important and ubiquitous task with applications ranging from drug design to friendship recommendation in social networks.

BIG-bench Machine Learning Dimensionality Reduction +1

Inductive Representation Learning on Large Graphs

17 code implementations NeurIPS 2017 William L. Hamilton, Rex Ying, Jure Leskovec

Low-dimensional embeddings of nodes in large graphs have proved extremely useful in a variety of prediction tasks, from content recommendation to identifying protein functions.

Graph Classification Graph Regression +5

Community Identity and User Engagement in a Multi-Community Landscape

no code implementations26 May 2017 Justine Zhang, William L. Hamilton, Cristian Danescu-Niculescu-Mizil, Dan Jurafsky, Jure Leskovec

To this end we introduce a quantitative, language-based typology reflecting two key aspects of a community's identity: how distinctive, and how temporally dynamic it is.

Loyalty in Online Communities

1 code implementation9 Mar 2017 William L. Hamilton, Justine Zhang, Cristian Danescu-Niculescu-Mizil, Dan Jurafsky, Jure Leskovec

In this paper we operationalize loyalty as a user-community relation: users loyal to a community consistently prefer it over all others; loyal communities retain their loyal users over time.

Cultural Shift or Linguistic Drift? Comparing Two Computational Measures of Semantic Change

no code implementations EMNLP 2016 William L. Hamilton, Jure Leskovec, Dan Jurafsky

Words shift in meaning for many reasons, including cultural factors like new technologies and regular linguistic processes like subjectification.

Cultural Vocal Bursts Intensity Prediction

Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change

4 code implementations ACL 2016 William L. Hamilton, Jure Leskovec, Dan Jurafsky

Understanding how words change their meanings over time is key to models of language and cultural evolution, but historical data on meaning is scarce, making theories hard to develop and test.

Diachronic Word Embeddings Word Embeddings

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