Search Results for author:
Found 6 papers, 3 papers with code
Elucidating the Exposure Bias in Diffusion Models
In this paper, we systematically investigate the exposure bias problem in diffusion models by first analytically modelling the sampling distribution, based on which we then attribute the prediction error at each sampling step as the root cause of the exposure bias issue.
Ranked #9 on
Image Generation
on CIFAR-10
Fully-attentive and interpretable: vision and video vision transformers for pain detection
Vision transformers are a top-performing architecture in computer vision, with little research on their use for pain detection.
Synthetic Expressions are Better Than Real for Learning to Detect Facial Actions
Critical obstacles in training classifiers to detect facial actions are the limited sizes of annotated video databases and the relatively low frequencies of occurrence of many actions.
Modeling Brain Networks with Artificial Neural Networks
We observe that both undirected and directed brain networks surpass the performances of the network models used in the fMRI literature.
Encoding the Local Connectivity Patterns of fMRI for Cognitive State Classification
In this work, we propose a novel framework to encode the local connectivity patterns of brain, using Fisher Vectors (FV), Vector of Locally Aggregated Descriptors (VLAD) and Bag-of-Words (BoW) methods.
Hierarchical Multi-resolution Mesh Networks for Brain Decoding
We propose a new framework, called Hierarchical Multi-resolution Mesh Networks (HMMNs), which establishes a set of brain networks at multiple time resolutions of fMRI signal to represent the underlying cognitive process.
