10 accepted papers
Existing Parameter-Efficient Fine-Tuning (PEFT) methods are fundamentally constrained by a static allocation paradigm, which overlooks the model's evolving optimization priorities during training. To address this, we introduce Dynamic Adaptive Fine-tuning (DAF), a novel framework that periodically e…
In the field of safe navigation for mobile robots, control barrier functions (CBFs) have garnered significant attention due to their ability to transform complex safety constraints into real-time solvable optimization problems. In this letter, we propose a novel Lyapunov-based CBF framework. It offe
In the field of safe navigation for mobile robots, control barrier functions (CBFs) have garnered significant attention due to their ability to transform complex safety constraints into real-time solvable optimization problems. In this letter, we propose a novel Lyapunov-based CBF framework. It offe…
Generative Recommendation (GR) has emerged as a new paradigm in recommender systems. This approach relies on quantized representations to discretize item features, modeling users’ historical interactions as sequences of discrete tokens. Based on these tokenized sequences, GR predicts the next item b
Generative Recommendation (GR) has emerged as a promising paradigm by formulating item recommendation as a sequence-to-sequence generation task over item identifiers. Recent studies have incorporated multimodal signals to provide richer token-level evidence for generation. However, existing approach…
Text-conditioned image generation has made rapid progress, yet rendering images with long-form text remains challenging due to the limitations of existing datasets, which predominantly focus on short and simple text. We introduce TextAtlas5M, a large-scale dataset designed to evaluate long-text rend…
Multi-modal Knowledge Graph Completion (KGC), which aims to enrich knowledge graph embeddings by incorporating images and text as supplementary information alongside triplets, is an significant task in learning KGs. Existing multi-modal KGC methods mainly focus on modalitylevel fusion, neglecting th…
Generative Retrieval (GR) introduces a new information retrieval paradigm that directly generates unique document identifiers (DocIDs). The key challenge of GR lies in creating effective yet discrete DocIDs that preserve semantic relevance for similar documents while differentiating dissimilar ones.…
Knowledge Graph Embedding (KGE) aims to project entities and relations into a low-dimensional space, so as to enable Knowledge Graphs (KGs) to be effectively used by downstream AI tasks. Most existing KGs (e.g. Wikidata) suffer from the data imbalance issue, i.e., the occurrence frequencies vary sig…
Cross-Lingual Information Retrieval (CLIR) aims to rank the documents written in a language different from the user’s query. The intrinsic gap between different languages is an essential challenge for CLIR. In this paper, we introduce the multilingual knowledge graph (KG) to the CLIR task due to the…