6 accepted papers
Large Language Model (LLM) agents have shown significant potential in automated optimization modeling for mathematical problems. However, real-world problems are still challenging due to their knowledge-intensive nature. Existing methods, constrained by static parametric knowledge, often lack the do…
Out-of-distribution (OOD) detection is critical for ensuring the reliability of deep learning models in open-world applications. While post-hoc methods are favored for their efficiency and ease of deployment, existing approaches often underexploit the rich information embedded in the model’s logits…
In this paper, we propose a novel modular network framework, called Scalable Modular Network (SMN), which enables adaptive learning capability and supports integration of new modules after pre-training for better adaptation. This adaptive capability comes from a novel design of router within SMN, na…
Pre-trained vision-language models (e.g., CLIP) have shown powerful zero-shot transfer capabilities. But they still struggle with domain shifts and typically require labeled data to adapt to downstream tasks, which could be costly. In this work, we aim to leverage unlabeled data that naturally span…
Few-shot learning (FSL) aims to learn novel tasks with very few labeled samples by leveraging experience from \emph{related} training tasks. In this paper, we try to understand FSL by exploring two key questions: (1) How to quantify the relationship between \emph{ training} and \emph{novel}…
Learning graph structure for graph neural networks (GNNs) is crucial to facilitate the GNN-based downstream learning tasks. It is challenging due to the non-differentiable discrete graph structure and lack of ground-truth. In this paper, we address these problems and propose a novel graph structure…