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Found 31 papers, 12 papers with code
GLEMOS: Benchmark for Instantaneous Graph Learning Model Selection
The choice of a graph learning (GL) model (i. e., a GL algorithm and its hyperparameter settings) has a significant impact on the performance of downstream tasks.
SciCapenter: Supporting Caption Composition for Scientific Figures with Machine-Generated Captions and Ratings
Crafting effective captions for figures is important.
Forward Learning of Graph Neural Networks
To address these limitations, the forward-forward algorithm (FF) was recently proposed as an alternative to BP in the image classification domain, which trains NNs by performing two forward passes over positive and negative data.
Continuous Treatment Effects with Surrogate Outcomes
In this paper, we study the role of surrogates in estimating continuous treatment effects and propose a doubly robust method to efficiently incorporate surrogates in the analysis, which uses both labeled and unlabeled data and does not suffer from the above selection bias problem.
Augment before You Try: Knowledge-Enhanced Table Question Answering via Table Expansion
Table question answering is a popular task that assesses a model's ability to understand and interact with structured data.
Leveraging Graph Diffusion Models for Network Refinement Tasks
Most real-world networks are noisy and incomplete samples from an unknown target distribution.
GPT-4 as an Effective Zero-Shot Evaluator for Scientific Figure Captions
We first constructed SCICAP-EVAL, a human evaluation dataset that contains human judgments for 3, 600 scientific figure captions, both original and machine-made, for 600 arXiv figures.
Summaries as Captions: Generating Figure Captions for Scientific Documents with Automated Text Summarization
Prior work often treated figure caption generation as a vision-to-language task.
Graph Learning with Localized Neighborhood Fairness
Learning fair graph representations for downstream applications is becoming increasingly important, but existing work has mostly focused on improving fairness at the global level by either modifying the graph structure or objective function without taking into account the local neighborhood of a node.
MetaGL: Evaluation-Free Selection of Graph Learning Models via Meta-Learning
In this work, we develop the first meta-learning approach for evaluation-free graph learning model selection, called MetaGL, which utilizes the prior performances of existing methods on various benchmark graph datasets to automatically select an effective model for the new graph, without any model training or evaluations.
CGC: Contrastive Graph Clustering for Community Detection and Tracking
Especially, deep graph clustering (DGC) methods have successfully extended deep clustering to graph-structured data by learning node representations and cluster assignments in a joint optimization framework.
Neural Point Process for Learning Spatiotemporal Event Dynamics
The key construction of our approach is the nonparametric space-time intensity function, governed by a latent process.
Automatic Unsupervised Outlier Model Selection
Given an unsupervised outlier detection task on a new dataset, how can we automatically select a good outlier detection algorithm and its hyperparameter(s) (collectively called a model)?
Automatic Forecasting via Meta-Learning
In this work, we develop techniques for fast automatic selection of the best forecasting model for a new unseen time-series dataset, without having to first train (or evaluate) all the models on the new time-series data to select the best one.
Asymptotics of Ridge Regression in Convolutional Models
We show the double descent phenomenon in our experiments for convolutional models and show that our theoretical results match the experiments.
Machine Unlearning via Algorithmic Stability
Our key contribution is the design of corresponding efficient unlearning algorithms, which are based on constructing a (maximal) coupling of Markov chains for the noisy SGD procedure.
Neural Point Process for Forecasting Spatiotemporal Events
To the best of our knowledge, this is the first neural point process model that can jointly predict both the space and time of events.
Learning Contextualized Knowledge Graph Structures for Commonsense Reasoning
Recently, neural-symbolic architectures have achieved success on commonsense reasoning through effectively encoding relational structures retrieved from external knowledge graphs (KGs) and obtained state-of-the-art results in tasks such as (commonsense) question answering and natural language inference.
Learning Contextualized Knowledge Structures for Commonsense Reasoning
Recently, knowledge graph (KG) augmented models have achieved noteworthy success on various commonsense reasoning tasks.
Learning to Deceive Knowledge Graph Augmented Models via Targeted Perturbation
Knowledge graphs (KGs) have helped neural models improve performance on various knowledge-intensive tasks, like question answering and item recommendation.
Reinforcement Learning-based N-ary Cross-Sentence Relation Extraction
In this paper, we relax this strong assumption by a weaker distant supervision assumption to address the second issue and propose a novel sentence distribution estimator model to address the first problem.
Clustering-based Unsupervised Generative Relation Extraction
To address this issue, we propose a Clustering-based Unsupervised generative Relation Extraction (CURE) framework that leverages an "Encoder-Decoder" architecture to perform self-supervised learning so the encoder can extract relation information.
Adjusting for Confounders with Text: Challenges and an Empirical Evaluation Framework for Causal Inference
Across 648 experiments and two datasets, we evaluate every commonly used causal inference method and identify their strengths and weaknesses to inform social media researchers seeking to use such methods, and guide future improvements.
Inferring Individual Level Causal Models from Graph-based Relational Time Series
We propose causal inference models for this problem that leverage both the graph topology and time-series to accurately estimate local causal effects of nodes.
Deep Relational Factorization Machines
Factorization Machines (FMs) is an important supervised learning approach due to its unique ability to capture feature interactions when dealing with high-dimensional sparse data.
Figure Captioning with Reasoning and Sequence-Level Training
In this work, we investigate the problem of figure captioning where the goal is to automatically generate a natural language description of the figure.
node2bits: Compact Time- and Attribute-aware Node Representations for User Stitching
Identity stitching, the task of identifying and matching various online references (e. g., sessions over different devices and timespans) to the same user in real-world web services, is crucial for personalization and recommendations.
Latent Network Summarization: Bridging Network Embedding and Summarization
Motivated by the computational and storage challenges that dense embeddings pose, we introduce the problem of latent network summarization that aims to learn a compact, latent representation of the graph structure with dimensionality that is independent of the input graph size (i. e., #nodes and #edges), while retaining the ability to derive node representations on the fly.
Social and Information Networks
Graph Classification using Structural Attention
Graph classification is a problem with practical applications in many different domains.
Ranked #1 on
Graph Classification
on NCI33
Learning Role-based Graph Embeddings
Random walks are at the heart of many existing network embedding methods.
Deep Graph Attention Model
Graph classification is a problem with practical applications in many different domains.
