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Found 6 papers, 3 papers with code
Flexible Channel Dimensions for Differentiable Architecture Search
Finding optimal channel dimensions (i. e., the number of filters in DNN layers) is essential to design DNNs that perform well under computational resource constraints.
SequeL: A Continual Learning Library in PyTorch and JAX
Continual Learning is an important and challenging problem in machine learning, where models must adapt to a continuous stream of new data without forgetting previously acquired knowledge.
Pareto Manifold Learning: Tackling multiple tasks via ensembles of single-task models
In Multi-Task Learning (MTL), tasks may compete and limit the performance achieved on each other, rather than guiding the optimization to a solution, superior to all its single-task trained counterparts.
U-Boost NAS: Utilization-Boosted Differentiable Neural Architecture Search
Optimizing resource utilization in target platforms is key to achieving high performance during DNN inference.
Hardware Aware Neural Architecture Search
Image Classification
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The Theoretical Expressiveness of Maxpooling
Over the decade since deep neural networks became state of the art image classifiers there has been a tendency towards less use of max pooling: the function that takes the largest of nearby pixels in an image.
Advances in the training, pruning and enforcement of shape constraints of Morphological Neural Networks using Tropical Algebra
In this paper we study an emerging class of neural networks based on the morphological operators of dilation and erosion.
