Deep Learning
Physics-Driven ML-Based Modelling for Correcting Inverse Estimation
When deploying machine learning estimators in science and engineering (SAE) domains, it is critical to avoid failed estimations that can have disastrous consequences, e.g., in aero engine design.
Public Opinion Field Effect Fusion in Representation Learning for Trending Topics Diffusion Junliang Li1, 2, Y ajun Y ang
Trending topic diffusion and prediction analysis is an important problem and has been well studied in social networks.
Multimodal Deep Learning Model Unveils Behavioral Dynamics of V1 Activity in Freely Moving Mice
Analyzing our model using maximally activating stimuli and saliency maps, we reveal new insights into cortical function, including the prevalence of mixed selectivity for behavioral variables in mouse V1.
ASIF: Coupled Data Turns Unimodal Models to Multimodal without Training Antonio Norelli Marco Fumero
Furthermore, our model has unique properties.
Fused Gromov-Wasserstein Graph Mixup for Graph-level Classifications Xinyu Ma
GNNs rely on parameterized nonlinear transformations to process graph signals, which can be efficiently encoded and serve as crucial inputs for downstream predictions.
Data Portraits: Recording Foundation Model Training Data
Foundation models are trained on increasingly immense and opaque datasets.