8 citations · 23 across the 6 of their papers we have counts for
6 papers · 1 filter
The Weight Is Over - Interactive Diffusion on Consumer GPUs
Frieder Ganz, Maximilian Müller
On-device inference is booming, but the momentum is almost all in language models. Diffusion pipelines are memory hungry, latency-sensitive, and require orchestrating an embedder,…
Verifying the Causes of Adversarial Examples
Honglin Li, Yifei Fan, Frieder Ganz +2
The robustness of neural networks is challenged by adversarial examples that contain almost imperceptible perturbations to inputs, which mislead a classifier to incorrect outputs i…
Continual Learning Using Multi-view Task Conditional Neural Networks
Honglin Li, Payam Barnaghi, Shirin Enshaeifar +1
Conventional deep learning models have limited capacity in learning multiple tasks sequentially. The issue of forgetting the previously learned tasks in continual learning is known…
Continual Learning Using Bayesian Neural Networks
HongLin Li, Payam Barnaghi, Shirin Enshaeifar +1
Continual learning models allow to learn and adapt to new changes and tasks over time. However, in continual and sequential learning scenarios in which the models are trained using…
Continual Learning in Deep Neural Network by Using a Kalman Optimiser
Honglin Li, Shirin Enshaeifar, Frieder Ganz +1
Learning and adapting to new distributions or learning new tasks sequentially without forgetting the previously learned knowledge is a challenging phenomenon in continual learning…
Kalman Filter Modifier for Neural Networks in Non-stationary Environments
Honglin Li, Frieder Ganz, Shirin Enshaeifar +1
Learning in a non-stationary environment is an inevitable problem when applying machine learning algorithm to real world environment. Learning new tasks without forgetting the prev…