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Found 8 papers, 1 papers with code
Learning Transferable Time Series Classifier with Cross-Domain Pre-training from Language Model
To address this challenge, we propose CrossTimeNet, a novel cross-domain SSL learning framework to learn transferable knowledge from various domains to largely benefit the target downstream task.
Unified Language-Vision Pretraining in LLM with Dynamic Discrete Visual Tokenization
Specifically, we introduce a well-designed visual tokenizer to translate the non-linguistic image into a sequence of discrete tokens like a foreign language that LLM can read.
Self-Supervised Interest Transfer Network via Prototypical Contrastive Learning for Recommendation
In this paper, we propose a cross-domain recommendation method: Self-supervised Interest Transfer Network (SITN), which can effectively transfer invariant knowledge between domains via prototypical contrastive learning.
Scenario-Adaptive and Self-Supervised Model for Multi-Scenario Personalized Recommendation
we summarize three practical challenges which are not well solved for multi-scenario modeling: (1) Lacking of fine-grained and decoupled information transfer controls among multiple scenarios.
Enhancing Sequential Recommendation with Graph Contrastive Learning
Specifically, GCL4SR employs a Weighted Item Transition Graph (WITG), built based on interaction sequences of all users, to provide global context information for each interaction and weaken the noise information in the sequence data.
Understanding Chinese Video and Language via Contrastive Multimodal Pre-Training
The pre-trained neural models have recently achieved impressive performances in understanding multimodal content.
Pre-training Graph Transformer with Multimodal Side Information for Recommendation
Side information of items, e. g., images and text description, has shown to be effective in contributing to accurate recommendations.
Comparative Deep Learning of Hybrid Representations for Image Recommendations
In many image-related tasks, learning expressive and discriminative representations of images is essential, and deep learning has been studied for automating the learning of such representations.
