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Found 13 papers, 3 papers with code
Efficient Tuning and Inference for Large Language Models on Textual Graphs
In this paper, we propose ENGINE, a parameter- and memory-efficient fine-tuning method for textual graphs with an LLM encoder.
GraphControl: Adding Conditional Control to Universal Graph Pre-trained Models for Graph Domain Transfer Learning
These pre-trained models can be applied to various downstream Web applications, saving training time and improving downstream (target) performance.
MARIO: Model Agnostic Recipe for Improving OOD Generalization of Graph Contrastive Learning
In this work, we investigate the problem of out-of-distribution (OOD) generalization for unsupervised learning methods on graph data.
Structure-Aware Group Discrimination with Adaptive-View Graph Encoder: A Fast Graph Contrastive Learning Framework
Albeit having gained significant progress lately, large-scale graph representation learning remains expensive to train and deploy for two main reasons: (i) the repetitive computation of multi-hop message passing and non-linearity in graph neural networks (GNNs); (ii) the computational cost of complex pairwise contrastive learning loss.
Relational Graph Learning for Grounded Video Description Generation
Such a setting can help explain the decisions of captioning models and prevents the model from hallucinating object words in its description.
Towards Communication-Efficient and Privacy-Preserving Federated Representation Learning
This paper investigates the feasibility of federated representation learning under the constraints of communication cost and privacy protection.
On the Efficacy of Small Self-Supervised Contrastive Models without Distillation Signals
It is a consensus that small models perform quite poorly under the paradigm of self-supervised contrastive learning.
Revisiting Catastrophic Forgetting in Class Incremental Learning
After investigating existing strategies, we observe that there is a lack of study on how to prevent the inter-phase confusion.
CIL: Contrastive Instance Learning Framework for Distantly Supervised Relation Extraction
The journey of reducing noise from distant supervision (DS) generated training data has been started since the DS was first introduced into the relation extraction (RE) task.
Run Away From your Teacher: a New Self-Supervised Approach Solving the Puzzle of BYOL
Recently, a newly proposed self-supervised framework Bootstrap Your Own Latent (BYOL) seriously challenges the necessity of negative samples in contrastive-based learning frameworks.
Run Away From your Teacher: Understanding BYOL by a Novel Self-Supervised Approach
Recently, a newly proposed self-supervised framework Bootstrap Your Own Latent (BYOL) seriously challenges the necessity of negative samples in contrastive learning frameworks.
Unsupervised Reinforcement Learning of Transferable Meta-Skills for Embodied Navigation
Visual navigation is a task of training an embodied agent by intelligently navigating to a target object (e. g., television) using only visual observations.
Informative Visual Storytelling with Cross-modal Rules
To solve this problem, we propose a method to mine the cross-modal rules to help the model infer these informative concepts given certain visual input.
Ranked #11 on
Visual Storytelling
on VIST
