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Found 8 papers, 1 papers with code
A Theory of Synaptic Neural Balance: From Local to Global Order
However, gradient descent on the regularized error function ought to converge to a balanced state, and thus network balance can be used to assess learning progress.
Neural Erosion: Emulating Controlled Neurodegeneration and Aging in AI Systems
Creating controlled methods to simulate neurodegeneration in artificial intelligence (AI) is crucial for applications that emulate brain function decline and cognitive disorders.
AttentionStitch: How Attention Solves the Speech Editing Problem
In addition to speech generation, speech editing is also a crucial task, which requires the seamless and unnoticeable integration of edited speech into synthesized speech.
Machine Learning-Enhanced Prediction of Surface Smoothness for Inertial Confinement Fusion Target Polishing Using Limited Data
In Inertial Confinement Fusion (ICF) process, roughly a 2mm spherical shell made of high density carbon is used as target for laser beams, which compress and heat it to energy levels needed for high fusion yield.
Structured Stochastic Gradient MCMC: a hybrid VI and MCMC approach
Unfortunately, VI makes strong assumptions on both the factorization and functional form of the posterior.
Structured Stochastic Gradient MCMC
Since practitioners face speed versus accuracy tradeoffs in these models, variational inference (VI) is often the preferable option.
Local Competition and Stochasticity for Adversarial Robustness in Deep Learning
The main operating principle of the introduced units lies on stochastic arguments, as the network performs posterior sampling over competing units to select the winner.
Local Competition and Uncertainty for Adversarial Robustness in Deep Learning
This work attempts to address adversarial robustness of deep networks by means of novel learning arguments.
