The results of training a neural network are heavily dependent on the
ar...
Machine learning is typically framed from a perspective of i.i.d., and m...
Access to pre-trained models has recently emerged as a standard across
n...
We present Queer in AI as a case study for community-led participatory d...
Probabilistic circuits (PCs) are models that allow exact and tractable
p...
What is the state of the art in continual machine learning? Although a
n...
Several families of continual learning techniques have been proposed to
...
Although a plethora of architectural variants for deep classification ha...
In this paper we analyze the classification performance of neural networ...
Current deep learning research is dominated by benchmark evaluation. A m...
We present an analysis of predictive uncertainty based out-of-distributi...
We introduce a unified probabilistic approach for deep continual learnin...
We characterize convolutional neural networks with respect to the relati...
Successful training of convolutional neural networks is often associated...