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Model-Agnostic Graph Regularization for Few-Shot Learning
In many domains, relationships between categories are encoded in the kno...
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Driver2vec: Driver Identification from Automotive Data
With increasing focus on privacy protection, alternative methods to iden...
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Open-World Semi-Supervised Learning
Supervised and semi-supervised learning methods have been traditionally ...
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Identity-aware Graph Neural Networks
Message passing Graph Neural Networks (GNNs) provide a powerful modeling...
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Inductive Representation Learning in Temporal Networks via Causal Anonymous Walks
Temporal networks serve as abstractions of many real-world dynamic syste...
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WILDS: A Benchmark of in-the-Wild Distribution Shifts
Distribution shifts can cause significant degradation in a broad range o...
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Design Space for Graph Neural Networks
The rapid evolution of Graph Neural Networks (GNNs) has led to a growing...
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Coresets for Robust Training of Neural Networks against Noisy Labels
Modern neural networks have the capacity to overfit noisy labels frequen...
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F-FADE: Frequency Factorization for Anomaly Detection in Edge Streams
Edge streams are commonly used to capture interactions in dynamic networ...
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Handling Missing Data with Graph Representation Learning
Machine learning with missing data has been approached in two different ...
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Graph Information Bottleneck
Representation learning of graph-structured data is challenging because ...
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Beta Embeddings for Multi-Hop Logical Reasoning in Knowledge Graphs
One of the fundamental problems in Artificial Intelligence is to perform...
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Direct Multi-hop Attention based Graph Neural Network
Introducing self-attention mechanism in graph neural networks (GNNs) ach...
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Inductive Learning on Commonsense Knowledge Graph Completion
Commonsense knowledge graph (CKG) is a special type of knowledge graph (...
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Distance Encoding – Design Provably More Powerful Graph Neural Networks for Structural Representation Learning
Learning structural representations of node sets from graph-structured d...
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OCEAN: Online Task Inference for Compositional Tasks with Context Adaptation
Real-world tasks often exhibit a compositional structure that contains a...
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Concept Learners for Generalizable Few-Shot Learning
Developing algorithms that are able to generalize to a novel task given ...
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Graph Structure of Neural Networks
Neural networks are often represented as graphs of connections between n...
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PinnerSage: Multi-Modal User Embedding Framework for Recommendations at Pinterest
Latent user representations are widely adopted in the tech industry for ...
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Neural Subgraph Matching
Subgraph matching is the problem of determining the presence and locatio...
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Improving Query Safety at Pinterest
Query recommendations in search engines is a double edged sword, with un...
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M2P2: Multimodal Persuasion Prediction using Adaptive Fusion
Identifying persuasive speakers in an adversarial environment is a criti...
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Open Graph Benchmark: Datasets for Machine Learning on Graphs
We present the Open Graph Benchmark (OGB), a diverse set of challenging ...
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Learning to Simulate Complex Physics with Graph Networks
Here we present a general framework for learning simulation, and provide...
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Entity Context and Relational Paths for Knowledge Graph Completion
Knowledge graph completion aims to predict missing relations between ent...
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Unifying Graph Convolutional Neural Networks and Label Propagation
Label Propagation (LPA) and Graph Convolutional Neural Networks (GCN) ar...
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Query2box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddings
Answering complex logical queries on large-scale incomplete knowledge gr...
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G2SAT: Learning to Generate SAT Formulas
The Boolean Satisfiability (SAT) problem is the canonical NP-complete pr...
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Hyperbolic Graph Convolutional Neural Networks
Graph convolutional neural networks (GCNs) embed nodes in a graph into E...
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Improving Graph Attention Networks with Large Margin-based Constraints
Graph Attention Networks (GATs) are the state-of-the-art neural architec...
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Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks
Modeling sequential interactions between users and items/products is cru...
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Selection Via Proxy: Efficient Data Selection For Deep Learning
Data selection methods such as active learning and core-set selection ar...
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Position-aware Graph Neural Networks
Learning node embeddings that capture a node's position within the broad...
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Redundancy-Free Computation Graphs for Graph Neural Networks
Graph Neural Networks (GNNs) are based on repeated aggregations of infor...
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Data Sketching for Faster Training of Machine Learning Models
Many machine learning problems reduce to the problem of minimizing an ex...
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Pre-training Graph Neural Networks
Many applications of machine learning in science and medicine, including...
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Knowledge Graph Convolutional Networks for Recommender Systems with Label Smoothness Regularization
Knowledge graphs capture interlinked information between entities and th...
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Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems
Knowledge graphs capture structured information and relations between a ...
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Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommendation
Knowledge graphs capture structured information and relations between a ...
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Hierarchical Temporal Convolutional Networks for Dynamic Recommender Systems
Recommender systems that can learn from cross-session data to dynamicall...
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Goal-setting And Achievement In Activity Tracking Apps: A Case Study Of MyFitnessPal
Activity tracking apps often make use of goals as one of their core moti...
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GNN Explainer: A Tool for Post-hoc Explanation of Graph Neural Networks
Graph Neural Networks (GNNs) are a powerful tool for machine learning on...
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Learning Dynamic Embeddings from Temporal Interactions
Modeling a sequence of interactions between users and items (e.g., produ...
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Predicting pregnancy using large-scale data from a women's health tracking mobile application
Predicting pregnancy has been a fundamental problem in women's health fo...
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Complete the Look: Scene-based Complementary Product Recommendation
Modeling fashion compatibility is challenging due to its complexity and ...
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Offline Behaviors of Online Friends
In this work we analyze traces of mobility and co-location among a group...
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How Powerful are Graph Neural Networks?
Graph Neural Networks (GNNs) for representation learning of graphs broad...
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CASC: Context-Aware Segmentation and Clustering for Motif Discovery in Noisy Time Series Data
Complex systems, such as airplanes, cars, or financial markets, produce ...
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Inferring Multi-Dimensional Rates of Aging from Cross-Sectional Data
Modeling how individuals evolve over time is a fundamental problem in th...
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Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities
New technologies have enabled the investigation of biology and human hea...
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