Thanh-Toan Do

Gradient-Aligned Calibration for Post-Training Quantization of Diffusion Models

Learning to Defer on Anonymously Annotated Data

Identifiability in Noisy Label Learning: A Multinomial Mixture Modelling Approach

Sharpness-Aware Minimization in Logit Space Efficiently Enhances Direct Preference Optimization

Align-SAM: Seeking Flatter Minima for Better Cross-Subset Alignment

Beyond Uniformity: Sample and Frequency Meta Weighting for Post-Training Quantization of Diffusion Models

AEON: Adaptive Estimation of Instance-Dependent In-Distribution and Out-of-Distribution Label Noise for Robust Learning

Layer-Wise High-Impact Parameter Ratio Optimization in Post-Training Quantization for Large Language Models

Adaptive Layer-Wise Transformations for Post-Training Quantization of Large Language Models

Rethinking Output Alignment for 1-bit Post-Training Quantization of Large Language Models

Probabilistic Learning to Defer: Handling Missing Expert Annotations and Controlling Workload Distribution

Geometry-Aware Collaborative Multi-Solutions Optimizer for Model Fine-Tuning with Parameter Efficiency

Zero-shot Mixed Precision Quantization via Joint Optimization of Data Generation and Bit Allocation

Multi-Perspective Data Augmentation for Few-shot Object Detection

Rethinking Attentions in Zero-Shot Real Image Editing

ConceptFlow: Unified Framework for Personalized Image Generation

Unified Perspectives on Signal-to-Noise Diffusion Models

Understanding Domain Generalization: A View of Necessity and Sufficiency

Agnostic Sharpness-Aware Minimization

Unified Deep Discrete Representation Learning Framework