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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

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4 results for “LWTA”

Local Competition and Stochasticity for Adversarial Robustness in Deep Learningcs.LG

New deep learning model robust to adversarial attacks using stochastic LWTA units.

problem Adversarial robustness in deep learning networks.

method Introduces deep networks with stochastic LWTA activations, combining them with Bayesian non-parametric tools.

result Achieves high robustness to adversarial perturbations, outperforming state-of-the-art methods.

Adversarial robustness Deep learning Stochastic LWTA Bayesian non-parametrics Variational Bayes

2021-01-04abs ↗pdf ↗

Stochastic Local Winner-Takes-All Networks Enable Profound Adversarial Robustnesscs.LG

Stochastic LWTA networks resist adversarial attacks while maintaining accuracy.

problem Adversarial robustness of neural networks.

method Replaced ReLU with stochastic LWTA activations, trained with Variational Bayesian and PGD.

result Stochastic LWTA networks achieve state-of-the-art robustness against adversarial attacks.

Adversarial robustness Stochastic LWTA Variational Bayesian PGD training

2021-12-05abs ↗pdf ↗

Local Competition and Uncertainty for Adversarial Robustness in Deep Learningcs.LG

New model improves deep learning robustness against adversarial attacks.

problem Improving adversarial robustness of deep learning models.

method Local competition principle, LWTA nonlinearities, Bayesian non-parametrics.

result The new model achieves high robustness to adversarial perturbations on MNIST and CIFAR10 datasets.

adversarial robustness deep learning local competition LWTA Bayesian non-parametrics

2020-06-18abs ↗pdf ↗

Competing Mutual Information Constraints with Stochastic Competition-based Activations for Learning Diversified Representationscs.LG

This work tackles representation learning by introducing stochastic competition-based activations.

problem Learning diversified representations in deep learning models.

method Combining information-theoretic arguments with stochastic competition-based activations, using Stochastic Local Winner-Takes-All (LWTA) units.

result The proposed method yields significant discriminative representation learning abilities and allows for a principled investigation of intermediate network representations.

representation learning diversified representations stochastic competition information theory deep learning

2022-01-10abs ↗pdf ↗