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STRIDE: Single-video based Temporally Continuous Occlusion Robust 3D Pose Estimation
This challenge arises because these models struggle to generalize beyond their training datasets, and the variety of occlusions is hard to capture in the training data.
Causal Inference Using LLM-Guided Discovery
At the core of causal inference lies the challenge of determining reliable causal graphs solely based on observational data.
Building a Winning Team: Selecting Source Model Ensembles using a Submodular Transferability Estimation Approach
Estimating the transferability of publicly available pretrained models to a target task has assumed an important place for transfer learning tasks in recent years.
On Counterfactual Data Augmentation Under Confounding
Counterfactual data augmentation has recently emerged as a method to mitigate confounding biases in the training data.
Towards Learning and Explaining Indirect Causal Effects in Neural Networks
Recently, there has been a growing interest in learning and explaining causal effects within Neural Network (NN) models.
Towards Estimating Transferability using Hard Subsets
As transfer learning techniques are increasingly used to transfer knowledge from the source model to the target task, it becomes important to quantify which source models are suitable for a given target task without performing computationally expensive fine tuning.
Go with the Flow: the distribution of information processing in multi-path networks
The architectures of convolution neural networks (CNN) have a great impact on the predictive performance and efficiency of the model.
