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Found 13 papers, 5 papers with code
High-Frequency-aware Hierarchical Contrastive Selective Coding for Representation Learning on Text-attributed Graphs
Moreover, we show that existing contrastive objective learns the low-frequency component of the augmentation graph and propose a high-frequency component (HFC)-aware contrastive learning objective that makes the learned embeddings more distinctive.
Inductive Graph Alignment Prompt: Bridging the Gap between Graph Pre-training and Inductive Fine-tuning From Spectral Perspective
Based on the insight of graph pre-training, we propose to bridge the graph signal gap and the graph structure gap with learnable prompts in the spectral space.
Beyond Pixels: Exploring Human-Readable SVG Generation for Simple Images with Vision Language Models
Our method focuses on producing SVGs that are both accurate and simple, aligning with human readability and understanding.
Exploring Recommendation Capabilities of GPT-4V(ision): A Preliminary Case Study
Large Multimodal Models (LMMs) have demonstrated impressive performance across various vision and language tasks, yet their potential applications in recommendation tasks with visual assistance remain unexplored.
TransGNN: Harnessing the Collaborative Power of Transformers and Graph Neural Networks for Recommender Systems
Graph Neural Networks (GNNs) have emerged as promising solutions for collaborative filtering (CF) through the modeling of user-item interaction graphs.
Foundation Model-oriented Robustness: Robust Image Model Evaluation with Pretrained Models
Machine learning has demonstrated remarkable performance over finite datasets, yet whether the scores over the fixed benchmarks can sufficiently indicate the model's performance in the real world is still in discussion.
Continual Learning on Dynamic Graphs via Parameter Isolation
Dynamic graph learning methods commonly suffer from the catastrophic forgetting problem, where knowledge learned for previous graphs is overwritten by updates for new graphs.
A Survey on Incremental Update for Neural Recommender Systems
In this article, we offer a systematic survey of incremental update for neural recommender systems.
Efficiently Leveraging Multi-level User Intent for Session-based Recommendation via Atten-Mixer Network
Based on this observation, we intuitively propose to remove the GNN propagation part, while the readout module will take on more responsibility in the model reasoning process.
Evolutionary Preference Learning via Graph Nested GRU ODE for Session-based Recommendation
Session-based recommendation (SBR) aims to predict the user next action based on the ongoing sessions.
AdaMCT: Adaptive Mixture of CNN-Transformer for Sequential Recommendation
Sequential recommendation (SR) aims to model users dynamic preferences from a series of interactions.
Improving Sequential Recommendations via Bidirectional Temporal Data Augmentation with Pre-training
Our approach leverages bidirectional temporal augmentation and knowledge-enhanced fine-tuning to synthesize authentic pseudo-prior items that \emph{retain user preferences and capture deeper item semantic correlations}, thus boosting the model's expressive power.
Word Shape Matters: Robust Machine Translation with Visual Embedding
Neural machine translation has achieved remarkable empirical performance over standard benchmark datasets, yet recent evidence suggests that the models can still fail easily dealing with substandard inputs such as misspelled words, To overcome this issue, we introduce a new encoding heuristic of the input symbols for character-level NLP models: it encodes the shape of each character through the images depicting the letters when printed.
