12 accepted papers
Retrieving molecular structures from tandem mass spectra is a crucial step in rapid compound identification. Existing retrieval methods, such as traditional mass spectral library matching, suffer from limited spectral library coverage, while recent cross-modal representation learning frameworks ofte
In recommender systems, recent advances highlight the critical role of alignment and uniformity (AU) in representation learning. Specifically, AU-based methods pull positive user-item pairs closer (alignment) and spread the overall representation distribution (uniformity), typically relying on obser
3D foundation models (3DFMs) have recently transformed 3D vision, enabling joint prediction of depths, poses, and point maps directly from images. Yet their ability to reason under extreme, non-overlapping views remains largely unexplored. In this work, we study their internal representations and fi
Graph Contrastive Learning (GCL) has recently emerged as a powerful paradigm for modeling user–item interactions and learning high-quality representations in recommender systems. While existing GCL-based methods benefit from data augmentation and sampling strategies, they often overlook the inherent
Event causality identification (ECI) is a challenging task that involves predicting causal relationships between events in text. Existing prompt-learning-based methods typically concatenate in-context examples only at the input layer, this shallow integration limits the model’s ability to capture th
Low-rank adaptation (LoRA) has been developed as an efficient approach for adapting large language models (LLMs) by fine-tuning two low-rank matrices, thereby reducing the number of trainable parameters. However, prior research indicates that many of the weights in these matrices are redundant, lead…
Local causal discovery aims to identify and distinguish the direct causes and effects of a target variable from observational data. Due to the inherent incompleteness of local information, popular methods from global causal discovery often face new challenges in local causal discovery tasks, such as
Local causal discovery is crucial for revealing the causal relationships between specific variables from data. Existing local causal discovery algorithms are designed under the assumption of causal sufficiency, which states that there are no latent common causes for two or more of the observed varia…
Most existing semi-supervised community detection algorithms leverage known communities to learn community structures, subsequently identifying communities that align with these learned community structures. However, differences in community structures may render the community structures learned by…
Large Language Models (LLMs) have shown that their reasoning ability could be enhanced through approaches like Chain-of-Thought (CoT) prompting. However, these methods use single prompts for different types of questions and do not design appropriate prompts for questions with different characteristi…
Generalized zero-shot text classification aims to classify textual instances from both previously seen classes and incrementally emerging unseen classes. Most existing methods generalize poorly since the learned parameters are only optimal for seen classes rather than for both classes, and the param…
The advent of natural language understanding (NLU) benchmarks for English, such as GLUE and SuperGLUE allows new NLU models to be evaluated across a diverse set of tasks. These comprehensive benchmarks have facilitated a broad range of research and applications in natural language processing (NLP).…