research
∙
07/14/2023
DistTGL: Distributed Memory-Based Temporal Graph Neural Network Training
Memory-based Temporal Graph Neural Networks are powerful tools in dynami...
share
research
∙
07/14/2023
HYTREL: Hypergraph-enhanced Tabular Data Representation Learning
Language models pretrained on large collections of tabular data have dem...
research
∙
06/06/2023
Large Language Models of Code Fail at Completing Code with Potential Bugs
Large language models of code (Code-LLMs) have recently brought tremendo...
research
∙
06/01/2023
Better Context Makes Better Code Language Models: A Case Study on Function Call Argument Completion
Pretrained code language models have enabled great progress towards prog...
research
∙
05/10/2023
XTab: Cross-table Pretraining for Tabular Transformers
The success of self-supervised learning in computer vision and natural l...
research
∙
04/20/2023
Train Your Own GNN Teacher: Graph-Aware Distillation on Textual Graphs
How can we learn effective node representations on textual graphs? Graph...
research
∙
02/13/2023
STREET: A Multi-Task Structured Reasoning and Explanation Benchmark
We introduce STREET, a unified multi-task and multi-domain natural langu...
research
∙
02/02/2023
Multimodal Chain-of-Thought Reasoning in Language Models
Large language models (LLMs) have shown impressive performance on comple...
research
∙
12/10/2022
SMILE: Scaling Mixture-of-Experts with Efficient Bi-level Routing
The mixture of Expert (MoE) parallelism is a recent advancement that sca...
research
∙
10/24/2022
ReaRev: Adaptive Reasoning for Question Answering over Knowledge Graphs
Knowledge Graph Question Answering (KGQA) involves retrieving entities a...
research
∙
10/14/2022
Injecting Domain Knowledge from Empirical Interatomic Potentials to Neural Networks for Predicting Material Properties
For decades, atomistic modeling has played a crucial role in predicting ...
research
∙
09/30/2022
Predicting Cellular Responses with Variational Causal Inference and Refined Relational Information
Predicting the responses of a cell under perturbations may bring importa...
research
∙
09/30/2022
Differentially Private Optimization on Large Model at Small Cost
Differentially private (DP) optimization is the standard paradigm to lea...
research
∙
09/30/2022
Differentially Private Bias-Term only Fine-tuning of Foundation Models
We study the problem of differentially private (DP) fine-tuning of large...
research
∙
09/13/2022
Variational Causal Inference
Estimating an individual's potential outcomes under counterfactual treat...
research
∙
06/22/2022
Efficient and effective training of language and graph neural network models
Can we combine heterogenous graph structure with text to learn high-qual...
research
∙
06/21/2022
Nimble GNN Embedding with Tensor-Train Decomposition
This paper describes a new method for representing embedding tables of g...
research
∙
06/14/2022
Automatic Clipping: Differentially Private Deep Learning Made Easier and Stronger
Per-example gradient clipping is a key algorithmic step that enables pra...
research
∙
06/09/2022
ScatterSample: Diversified Label Sampling for Data Efficient Graph Neural Network Learning
What target labels are most effective for graph neural network (GNN) tra...
research
∙
04/30/2022
MiCS: Near-linear Scaling for Training Gigantic Model on Public Cloud
Existing general purpose frameworks for gigantic model training, i.e., m...
research
∙
04/23/2022
Exploring the Role of Task Transferability in Large-Scale Multi-Task Learning
Recent work has found that multi-task training with a large number of di...
research
∙
04/04/2022
Coarse-to-Fine Sparse Sequential Recommendation
Sequential recommendation aims to model dynamic user behavior from histo...
research
∙
03/28/2022
TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphs
Many real world graphs contain time domain information. Temporal Graph N...
research
∙
03/18/2022
Learning Personalized Item-to-Item Recommendation Metric via Implicit Feedback
This paper studies the item-to-item recommendation problem in recommende...
research
∙
01/22/2022
Joint Learning of Hierarchical Community Structure and Node Representations: An Unsupervised Approach
Graph representation learning has demonstrated improved performance in t...
research
∙
12/31/2021
Distributed Hybrid CPU and GPU training for Graph Neural Networks on Billion-Scale Graphs
Graph neural networks (GNN) have shown great success in learning from gr...
research
∙
12/10/2021
TempoQR: Temporal Question Reasoning over Knowledge Graphs
Knowledge Graph Question Answering (KGQA) involves retrieving facts from...
research
∙
11/27/2021
Benchmarking Accuracy and Generalizability of Four Graph Neural Networks Using Large In Vitro ADME Datasets from Different Chemical Spaces
In this work, we benchmark a variety of single- and multi-task graph neu...
research
∙
10/15/2021
Meta-learning via Language Model In-context Tuning
The goal of meta-learning is to learn to adapt to a new task with only a...
research
∙
10/12/2021
Scalable Consistency Training for Graph Neural Networks via Self-Ensemble Self-Distillation
Consistency training is a popular method to improve deep learning models...
research
∙
09/23/2021
Distiller: A Systematic Study of Model Distillation Methods in Natural Language Processing
We aim to identify how different components in the KD pipeline affect th...
research
∙
09/14/2021
HeMI: Multi-view Embedding in Heterogeneous Graphs
Many real-world graphs involve different types of nodes and relations be...
research
∙
09/13/2021
FaiREO: User Group Fairness for Equality of Opportunity in Course Recommendation
Course selection is challenging for students in higher educational insti...
research
∙
09/09/2021
Trust your neighbors: A comprehensive survey of neighborhood-based methods for recommender systems
Collaborative recommendation approaches based on nearest-neighbors are s...
research
∙
08/31/2021
Position-based Hash Embeddings For Scaling Graph Neural Networks
Graph Neural Networks (GNNs) bring the power of deep representation lear...
research
∙
06/27/2021
DGL-LifeSci: An Open-Source Toolkit for Deep Learning on Graphs in Life Science
Graph neural networks (GNNs) constitute a class of deep learning methods...
research
∙
05/03/2021
Schema-Aware Deep Graph Convolutional Networks for Heterogeneous Graphs
Graph convolutional network (GCN) based approaches have achieved signifi...
research
∙
03/04/2021
Universal Representation for Code
Learning from source code usually requires a large amount of labeled dat...
research
∙
03/04/2021
IACN: Influence-aware and Attention-based Co-evolutionary Network for Recommendation
Recommending relevant items to users is a crucial task on online communi...
research
∙
01/19/2021
Learning over Families of Sets – Hypergraph Representation Learning for Higher Order Tasks
Graph representation learning has made major strides over the past decad...
research
∙
01/16/2021
An Empirical Comparison of Deep Learning Models for Knowledge Tracing on Large-Scale Dataset
Knowledge tracing (KT) is the problem of modeling each student's mastery...
research
∙
01/10/2021
Learning Student Interest Trajectory for MOOCThread Recommendation
In recent years, Massive Open Online Courses (MOOCs) have witnessed imme...
research
∙
12/15/2020
Distant-Supervised Slot-Filling for E-Commerce Queries
Slot-filling refers to the task of annotating individual terms in a quer...
research
∙
11/23/2020
FERN: Fair Team Formation for Mutually Beneficial Collaborative Learning
Automated Team Formation is becoming increasingly important for a pletho...
research
∙
10/11/2020
DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs
Graph neural networks (GNN) have shown great success in learning from gr...
research
∙
09/26/2020
Heterogeneous Molecular Graph Neural Networks for Predicting Molecule Properties
As they carry great potential for modeling complex interactions, graph n...
research
∙
09/15/2020
Graph InfoClust: Leveraging cluster-level node information for unsupervised graph representation learning
Unsupervised (or self-supervised) graph representation learning is essen...
research
∙
07/24/2020
COVID-19 Knowledge Graph: Accelerating Information Retrieval and Discovery for Scientific Literature
The coronavirus disease (COVID-19) has claimed the lives of over 350,000...
research
∙
07/20/2020
PanRep: Universal node embeddings for heterogeneous graphs
Learning unsupervised node embeddings facilitates several downstream tas...
research
∙
07/20/2020
