73 citations · 234 across the 15 of their papers we have counts for
16 papers
YARD: Y-Architecture Register Decoding for Efficient Hallucination Mitigation in Large Vision-Language Models
Ting Chen, Geng Li, Guohao Chen +5
Contrastive decoding (CD) seeks to mitigate hallucinations in Large Vision-Language Models (LVLMs) by contrasting the output distributions of a standard model and a visually degrad…
Rethinking Hebbian Principle: Low-Dimensional Structural Projection for Unsupervised Learning
Shikuang Deng, Jiayuan Zhang, Yuhang Wu +2
Hebbian learning is a biological principle that intuitively describes how neurons adapt their connections through repeated stimuli. However, when applied to machine learning, it su…
Denoising Autoregressive Representation Learning
Yazhe Li, Jorg Bornschein, Ting Chen
In this paper, we explore a new generative approach for learning visual representations. Our method, DARL, employs a decoder-only Transformer to predict image patches autoregressiv…
On the Importance of Noise Scheduling for Diffusion Models
Ting Chen
We empirically study the effect of noise scheduling strategies for denoising diffusion generative models. There are three findings: (1) the noise scheduling is crucial for the perf…
Understanding and Improving Robustness of Vision Transformers through Patch-based Negative Augmentation
Yao Qin, Chiyuan Zhang, Ting Chen +3
We investigate the robustness of vision transformers (ViTs) through the lens of their special patch-based architectural structure, i.e., they process an image as a sequence of imag…
Improved Transformer for High-Resolution GANs
Long Zhao, Zizhao Zhang, Ting Chen +2
Attention-based models, exemplified by the Transformer, can effectively model long range dependency, but suffer from the quadratic complexity of self-attention operation, making th…