Deep Learning
Human-Object Interaction Detection Collaborated with Large Relation-driven Diffusion Models Liulei Li
Figure 1: Existing solutions utilize mere linguistic knowledge (a).
NeuroBOL T: Resting-state EEG-to-fMRI Synthesis with Multi-dimensional Feature Mapping Yamin Li
Functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) are the most commonly utilized non-invasive neuroimaging techniques, providing crucial insights into brain functionality.
Generalizing Weather Forecast to Fine-grained Temporal Scales via Physics-AI Hybrid Modeling
However, most data-driven weather forecasting models are black-box systems that focus on learning data mapping rather than fine-grained physical evolution in the time dimension. Consequently, the limitations in the temporal scale of datasets prevent these models from forecasting at finer time scales.
Detecting Brittle Decisions for Free: Leveraging Margin Consistency in Deep Robust Classifiers
First, we establish that margin consistency is a necessary and sufficient condition to use a model's logit margin as a score for identifying non-robust samples.
On conditional diffusion models for PDE simulations
In particular, we focus on diffusion models that are either trained in a conditional manner, or conditioned after unconditional training.