Search Results for author:
Found 7 papers, 1 papers with code
Cost-Effective Hallucination Detection for LLMs
Large language models (LLMs) can be prone to hallucinations - generating unreliable outputs that are unfaithful to their inputs, external facts or internally inconsistent.
Q-Tuning: Queue-based Prompt Tuning for Lifelong Few-shot Language Learning
This paper introduces \textbf{Q-tuning}, a novel approach for continual prompt tuning that enables the lifelong learning of a pre-trained language model.
Hierarchical Conditional Semi-Paired Image-to-Image Translation For Multi-Task Image Defect Correction On Shopping Websites
Unlike public data, another practical challenge on shopping websites is that some paired images are of low quality.
KD-FixMatch: Knowledge Distillation Siamese Neural Networks
Firstly, an outer SNN is trained using labeled and unlabeled data.
AdaSelection: Accelerating Deep Learning Training through Data Subsampling
In this paper, we introduce AdaSelection, an adaptive sub-sampling method to identify the most informative sub-samples within each minibatch to speed up the training of large-scale deep learning models without sacrificing model performance.
Text Is All You Need: Learning Language Representations for Sequential Recommendation
In this paper, we propose to model user preferences and item features as language representations that can be generalized to new items and datasets.
CMA-CLIP: Cross-Modality Attention CLIP for Image-Text Classification
In this paper, we propose the Cross-Modality Attention Contrastive Language-Image Pre-training (CMA-CLIP), a new framework which unifies two types of cross-modality attentions, sequence-wise attention and modality-wise attention, to effectively fuse information from image and text pairs.
