13 accepted papers
Large Language Models (LLMs) have achieved remarkable success across reasoning and knowledge-intensive tasks, yet their static pretraining leaves them unable to handle rapidly evolving or domain-specific knowledge. Retrieval-Augmented Generation (RAG) addresses this by grounding LLM outputs in dynam
Deploying Vision Transformers (ViTs) in real-world multi-task learning remains challenging due to their massive computational costs and the difficulty of pruning shared backbones without harming task performance. Single-task pruning often causes destructive interference by discarding weights critica
Few-shot learning (FSL) aims to classify unseen examples (query set) into labeled data (support set) through low-dimensional embeddings. However, the diversity and unpredictability of environments and capture devices make FSL more challenging in real-world applications. In this paper, we propose Dua…
The rise of voice conversion (VC), i.e., audio deepfakes, poses serious risks. While many detections have been developed, current methods focus on identifying artifacts in deepfake samples. As deepfake technology advances, the question arises: can these methods detect future deepfakes that may conta…
To achieve better performance and greater fairness in Federated Learning (FL), much of the existing research has centered on individual clients, using domain adaptation techniques and redesigned aggregation schemes to counteract client data heterogeneity. However, an overlooked scenario exists where…
Reinforcement learning (RL) has achieved impressive performance in various domains. However, most RL frameworks oversimplify the problem by assuming a fixed-yet-known environment and often have difficulty being generalized to real-world scenarios. In this paper, we address a new challenge with a mor…
Quantization is an effective approach for memory cost reduction by compressing networks to lower bits. However, existing quantization processes learned only from the current data tend to suffer from forgetting catastrophe on streaming data, i.e., significant performance decrement on old task data af…
While graph neural networks (GNNs) have achieved notable success in various graph mining tasks, conventional GNNs only model the pairwise correlation in 1-hop neighbors without considering the long-term relations and the high-order patterns, thus limiting their performances. Recently, several works…
The evaluation of participant contribution in federated learning (FL) has recently gained significant attention due to its applicability in various domains, such as incentive mechanisms, robustness enhancement, and client selection. Previous approaches have predominantly relied on the widely adopted…
Quantization is an efficient network compression approach to reduce the inference time. However, existing approaches ignored the distribution difference between training and testing data, thereby inducing a large quantization error in inference. To address this issue, we propose a new quantization s…
Quantization compresses models to low bits for efficient inferences which has received increasing attentions. However, existing approaches focused on balanced datasets, while imbalanced data is pervasive in the real world. Therefore, in this study, we investigate the realistic problem, quantization…
Due to the great success of image-to-image (Img2Img) translation GANs, many applications with ethics issues arise, e.g., DeepFake and DeepNude, presenting a challenging problem to prevent the misuse of these techniques. In this work, we tackle the problem by a new adversarial attack scheme, namely t…
When exposed to an item in a recommender system, a user may consume it (known as success exposure) or neglect it (known as failure exposure). The recently proposed methods that consider both success and failure exposure merely regard failure exposure as a constant prior, thus being capable of neithe…