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Found 7 papers, 4 papers with code
NFL: Robust Learned Index via Distribution Transformation
Based on the characteristics of the transformed keys, we propose a robust After-Flow Learned Index (AFLI).
A Fast Transformer-based General-Purpose Lossless Compressor
To ease this problem, this paper targets on cutting down the execution time of deep-learning-based compressors.
Weight Rescaling: Effective and Robust Regularization for Deep Neural Networks with Batch Normalization
On the one hand, when the non-adaptive learning rate e. g. SGD with momentum is used, the effective learning rate continues to increase even after the initial training stage, which leads to an overfitting effect in many neural architectures.
Variational Nested Dropout
Nested dropout is a variant of dropout operation that is able to order network parameters or features based on the pre-defined importance during training.
Improve Generalization and Robustness of Neural Networks via Weight Scale Shifting Invariant Regularizations
The derived regularizer is an upper bound for the input gradient of the network so minimizing the improved regularizer also benefits the adversarial robustness.
Accelerating Monte Carlo Bayesian Inference via Approximating Predictive Uncertainty over the Simplex
In this work, assuming that the exact posterior or a decent approximation is obtained, we propose a generic framework to approximate the output probability distribution induced by model posterior with a parameterized model and in an amortized fashion.
Accelerating Monte Carlo Bayesian Inference via Approximating Predictive Uncertainty over Simplex
In this work, assuming that the exact posterior or a decent approximation is obtained, we propose a generic framework to approximate the output probability distribution induced by model posterior with a parameterized model and in an amortized fashion.
