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Found 8 papers, 2 papers with code
On the Statistical and Information Theoretical Characteristics of DNN Representations
It has been common to argue or imply that a regularizer can be used to alter a statistical property of a hidden layer's representation and thus improve generalization or performance of deep networks.
OTTER: Improving Zero-Shot Classification via Optimal Transport
Popular zero-shot models suffer due to artifacts inherited from pretraining.
Multimodal Data Curation via Object Detection and Filter Ensembles
We propose an approach for curating multimodal data that we used for our entry in the 2023 DataComp competition filtering track.
Zero-Shot Robustification of Zero-Shot Models
Additionally, we demonstrate that RoboShot is compatible with a variety of pretrained and language models and propose a way to further boost performance with a zero-shot adaptation variant.
Mitigating Source Bias for Fairer Weak Supervision
Weak supervision enables efficient development of training sets by reducing the need for ground truth labels.
Universalizing Weak Supervision
We apply this technique to important problems previously not tackled by WS frameworks including learning to rank, regression, and learning in hyperbolic space.
Subtask Gated Networks for Non-Intrusive Load Monitoring
In this work, we focus on the idea that appliances have on/off states, and develop a deep network for further performance improvements.
DNN Representations as Codewords: Manipulating Statistical Properties via Penalty Regularization
Performance of Deep Neural Network (DNN) heavily depends on the characteristics of hidden layer representations.
