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Found 12 papers, 9 papers with code
MoDification: Mixture of Depths Made Easy
Long-context efficiency has recently become a trending topic in serving large language models (LLMs).
Vript: A Video Is Worth Thousands of Words
Vriptor is also a powerful model capable of end-to-end generation of dense and detailed captions for long videos.
Learning to Reweight for Graph Neural Network
In this paper, we study the problem of the generalization ability of GNNs in Out-Of-Distribution (OOD) settings.
Qwen Technical Report
Large language models (LLMs) have revolutionized the field of artificial intelligence, enabling natural language processing tasks that were previously thought to be exclusive to humans.
Ranked #3 on
Multi-Label Text Classification
on CC3M-TagMask
Multi-task Bioassay Pre-training for Protein-ligand Binding Affinity Prediction
By introducing multi-task pre-training to treat the prediction of different affinity labels as different tasks and classifying relative rankings between samples from the same bioassay, MBP learns robust and transferrable structural knowledge from our new ChEMBL-Dock dataset with varied and noisy labels.
Knowledge Distillation of Transformer-based Language Models Revisited
In the past few years, transformer-based pre-trained language models have achieved astounding success in both industry and academia.
Deep Unified Representation for Heterogeneous Recommendation
Recommendation system has been a widely studied task both in academia and industry.
ProtGNN: Towards Self-Explaining Graph Neural Networks
In this work, we propose Prototype Graph Neural Network (ProtGNN), which combines prototype learning with GNNs and provides a new perspective on the explanations of GNNs.
Motif-based Graph Self-Supervised Learning for Molecular Property Prediction
To bridge this gap, we propose Motif-based Graph Self-supervised Learning (MGSSL) by introducing a novel self-supervised motif generation framework for GNNs.
GraphMI: Extracting Private Graph Data from Graph Neural Networks
Then we design a graph auto-encoder module to efficiently exploit graph topology, node attributes, and target model parameters for edge inference.
ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property Prediction
Here we propose a novel framework called Active Semi-supervised Graph Neural Network (ASGN) by incorporating both labeled and unlabeled molecules.
Molecular Property Prediction: A Multilevel Quantum Interactions Modeling Perspective
In this paper, we propose a generalizable and transferable Multilevel Graph Convolutional neural Network (MGCN) for molecular property prediction.
Ranked #6 on
Graph Regression
on Lipophilicity
