1 citations · 2 across the 9 of their papers we have counts for
10 papers
SCOPD: Sparse-Context On-Policy Self-Distillation for Efficient Vision-Language Models
Ahmadreza Jeddi, Enming Zhang, Jasper Gerigk +12
Reasoning vision-language models (VLMs) process images and videos as long sequences of visual tokens, making inference expensive. Training-free token pruning reduces this cost, but…
RAPTOR: Role-Aware Private Training for Mixture-of-Experts
Duc Dm, Khai Le-Duc, Nguyen Do +18
Differentially private (DP) fine-tuning methods treat sparse Mixture-of-Experts (MoE) models as a single dense block, ignoring that shared layers see all data while experts only se…
Few-Shot-Based Modular Image-to-Video Adapter for Diffusion Models
Zhenhao Li, Shaohan Yi, Zheng Liu +7
Diffusion models (DMs) have recently achieved impressive photorealism in image and video generation. However, their application to image animation remains limited, even when traine…
RepLoRA: Reparameterizing Low-Rank Adaptation via the Perspective of Mixture of Experts
Tuan Truong, Chau Nguyen, Huy Nguyen +3
Low-rank Adaptation (LoRA) has emerged as a powerful method for fine-tuning large-scale foundation models. Despite its popularity, the theoretical understanding of LoRA has remaine…
On Zero-Initialized Attention: Optimal Prompt and Gating Factor Estimation
Nghiem T. Diep, Huy Nguyen, Chau Nguyen +5
The LLaMA-Adapter has recently emerged as an efficient fine-tuning technique for LLaMA models, leveraging zero-initialized attention to stabilize training and enhance performance.…
Adaptive Prompting for Continual Relation Extraction: A Within-Task Variance Perspective
Minh Le, Tien Ngoc Luu, An Nguyen The +5
To address catastrophic forgetting in Continual Relation Extraction (CRE), many current approaches rely on memory buffers to rehearse previously learned knowledge while acquiring n…
Revisit Visual Prompt Tuning: The Expressiveness of Prompt Experts
Minh Le, Anh Nguyen, Huy Nguyen +3
Visual Prompt Tuning (VPT) has proven effective for parameter-efficient adaptation of pre-trained vision models to downstream tasks by inserting task-specific learnable prompt toke…
Leveraging Hierarchical Taxonomies in Prompt-based Continual Learning
Quyen Tran, Hoang Phan, Minh Le +6
Humans perceive the world as a series of sequential events, which can be hierarchically organized with different levels of abstraction based on conceptual knowledge. Drawing inspir…
Revisiting Prefix-tuning: Statistical Benefits of Reparameterization among Prompts
Minh Le, Chau Nguyen, Huy Nguyen +3
Prompt-based techniques, such as prompt-tuning and prefix-tuning, have gained prominence for their efficiency in fine-tuning large pre-trained models. Despite their widespread adop…
Mixture of Experts Meets Prompt-Based Continual Learning
Minh Le, An Nguyen, Huy Nguyen +4
Exploiting the power of pre-trained models, prompt-based approaches stand out compared to other continual learning solutions in effectively preventing catastrophic forgetting, even…