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Found 6 papers, 6 papers with code
Diffusion Classifiers Understand Compositionality, but Conditions Apply
To address this, we present a comprehensive study of the discriminative capabilities of diffusion classifiers on a wide range of compositional tasks.
CLIP Behaves like a Bag-of-Words Model Cross-modally but not Uni-modally
We find that the correct attribute-object binding information is already present in individual text and image modalities.
Task-Synchronized Recurrent Neural Networks
We propose an elegant straightforward alternative approach where instead the RNN is in effect resampled in time to match the time of the data or the task at hand.
Efficient implementations of echo state network cross-validation
The second level of optimization also makes the (ii) part remain constant irrespective of large $k$, as long as the dimension of the output is low.
Localized convolutional neural networks for geospatial wind forecasting
They can be added to any convolutional layers, easily end-to-end trained, introduce minimal additional complexity, and let CNNs retain most of their benefits to the extent that they are needed.
Efficient Cross-Validation of Echo State Networks
Thus in many situations $k$-fold cross-validation of ESNs can be done for virtually the same time complexity as a simple single split validation.
