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Found 4 papers, 1 papers with code
Simulated Overparameterization
Building upon this framework, we present a novel, architecture agnostic algorithm called "majority kernels", which seamlessly integrates with predominant architectures, including Transformer models.
Deep Fusion: Efficient Network Training via Pre-trained Initializations
Finally, we validate our theoretical framework, which guides the optimal use of Deep Fusion, showing that with carefully optimized training dynamics, it significantly reduces both training time and resource consumption.
EnergyNet: Energy-based Adaptive Structural Learning of Artificial Neural Network Architectures
We present E NERGY N ET , a new framework for analyzing and building artificial neural network architectures.
AdaNet: Adaptive Structural Learning of Artificial Neural Networks
We present new algorithms for adaptively learning artificial neural networks.
