2 citations · 2 across the 8 of their papers we have counts for
9 papers
Consistency Deep Equilibrium Models
Junchao Lin, Zenan Ling, Jingwen Xu +1
Deep Equilibrium Models (DEQs) have emerged as a powerful paradigm in deep learning, offering the ability to model infinite-depth networks with constant memory usage. However, DEQs…
Diving into Kronecker Adapters: Component Design Matters
Jiayu Bai, Danchen Yu, Zhenyu Liao +4
Kronecker adapters have emerged as a promising approach for fine-tuning large-scale models, enabling high-rank updates through tunable component structures. However, existing work…
Series-to-Series Diffusion Bridge Model
Hao Yang, Zhanbo Feng, Feng Zhou +2
Diffusion models have risen to prominence in time series forecasting, showcasing their robust capability to model complex data distributions. However, their effectiveness in determ…
IGNN-Solver: A Graph Neural Solver for Implicit Graph Neural Networks
Junchao Lin, Zenan Ling, Zhanbo Feng +6
Implicit graph neural networks (IGNNs), which exhibit strong expressive power with a single layer, have recently demonstrated remarkable performance in capturing long-range depende…
Nonstationary Sparse Spectral Permanental Process
Zicheng Sun, Yixuan Zhang, Zenan Ling +2
Existing permanental processes often impose constraints on kernel types or stationarity, limiting the model's expressiveness. To overcome these limitations, we propose a novel appr…
Deep Equilibrium Models are Almost Equivalent to Not-so-deep Explicit Models for High-dimensional Gaussian Mixtures
Zenan Ling, Longbo Li, Zhanbo Feng +4
Deep equilibrium models (DEQs), as a typical implicit neural network, have demonstrated remarkable success on various tasks. There is, however, a lack of theoretical understanding…
Mitigating Label Bias in Machine Learning: Fairness through Confident Learning
Yixuan Zhang, Boyu Li, Zenan Ling +1
Discrimination can occur when the underlying unbiased labels are overwritten by an agent with potential bias, resulting in biased datasets that unfairly harm specific groups and ca…
Revisiting Logistic-softmax Likelihood in Bayesian Meta-Learning for Few-Shot Classification
Tianjun Ke, Haoqun Cao, Zenan Ling +1
Meta-learning has demonstrated promising results in few-shot classification (FSC) by learning to solve new problems using prior knowledge. Bayesian methods are effective at charact…
Textual and Visual Prompt Fusion for Image Editing via Step-Wise Alignment
Zhanbo Feng, Zenan Ling, Xinyu Lu +6
The use of denoising diffusion models is becoming increasingly popular in the field of image editing. However, current approaches often rely on either image-guided methods, which p…