1 citations · 2 across the 7 of their papers we have counts for
8 papers
SPEAR-Gen: Generation-Aware Pre-training for Unified Speech Representations
Xiaoyu Yang, Arthur Hinsvark, Antonios Alexos +3
Speech understanding and generation place different demands on speech representations, and existing models are typically optimised towards one capability or the other. To reduce th…
A Theory of Synaptic Neural Balance: From Local to Global Order
Pierre Baldi, Antonios Alexos, Ian Domingo +1
We develop a general theory of synaptic neural balance and how it can emerge or be enforced in neural networks. For a given regularizer, a neuron is said to be in balance if the to…
Neural Erosion: Emulating Controlled Neurodegeneration and Aging in AI Systems
Antonios Alexos, Yu-Dai Tsai, Ian Domingo +2
Creating controlled methods to simulate neurodegeneration in artificial intelligence (AI) is crucial for applications that emulate brain function decline and cognitive disorders. W…
AttentionStitch: How Attention Solves the Speech Editing Problem
Antonios Alexos, Pierre Baldi
The generation of natural and high-quality speech from text is a challenging problem in the field of natural language processing. In addition to speech generation, speech editing i…
Machine Learning-Enhanced Prediction of Surface Smoothness for Inertial Confinement Fusion Target Polishing Using Limited Data
Antonios Alexos, Junze Liu, Akash Tiwari +5
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 leve…
Structured Stochastic Gradient MCMC
Antonios Alexos, Alex Boyd, Stephan Mandt
Stochastic gradient Markov Chain Monte Carlo (SGMCMC) is considered the gold standard for Bayesian inference in large-scale models, such as Bayesian neural networks. Since practiti…
Local Competition and Stochasticity for Adversarial Robustness in Deep Learning
Konstantinos P. Panousis, Sotirios Chatzis, Antonios Alexos +1
This work addresses adversarial robustness in deep learning by considering deep networks with stochastic local winner-takes-all (LWTA) activations. This type of network units resul…
Local Competition and Uncertainty for Adversarial Robustness in Deep Learning
Antonios Alexos, Konstantinos P. Panousis, Sotirios Chatzis
This work attempts to address adversarial robustness of deep networks by means of novel learning arguments. Specifically, inspired from results in neuroscience, we propose a local…