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Found 8 papers, 3 papers with code
Efficient Long Sequence Modeling via State Space Augmented Transformer
Specifically, we augment a SSM into the bottom layer of SPADE, and we employ efficient local attention methods for the other layers.
Representation Learning for Clustering via Building Consensus
Recent advances in deep clustering and unsupervised representation learning are based on the idea that different views of an input image (generated through data augmentation techniques) must be close in the representation space (exemplar consistency), and/or similar images must have similar cluster assignments (population consistency).
Ranked #3 on
Image Clustering
on ImageNet-10
Semantic Hashing with Locality Sensitive Embeddings
Semantic hashing methods have been explored for learning transformations into binary vector spaces.
Consensus Clustering With Unsupervised Representation Learning
Recent advances in deep clustering and unsupervised representation learning are based on the idea that different views of an input image (generated through data augmentation techniques) must either be closer in the representation space, or have a similar cluster assignment.
Self-Supervised Contextual Bandits in Computer Vision
In the usual self-supervision, we learn implicit labels from the training data for a secondary task.
Data Transformation Insights in Self-supervision with Clustering Tasks
We show faster convergence rate with valid transformations for convex as well as certain family of non-convex objectives along with the proof of convergence to the original set of optima.
A Unified Batch Online Learning Framework for Click Prediction
Through extensive experiments, we demonstrate the utility of of our OL framework; how the two OL schemes relate to each other and how they trade-off between the new and historical data.
Modeling and Simultaneously Removing Bias via Adversarial Neural Networks
In real world systems, the predictions of deployed Machine Learned models affect the training data available to build subsequent models.
