Making good decisions requires updating beliefs according to new evidence. This is a
dynamical process that is prone to biases: in some cases, beliefs become entrenched and …

The Bayesian brain hypothesis is one of the most influential ideas in neuroscience. However,
unstated differences in how Bayesian ideas are operationalized make it difficult to draw …

Decision-making is often hierarchical and approximate in nature: decisions are not being
made based on actual observations, but on intermediate variables that themselves have to be …

Sensory processing is often characterized as implementing probabilistic inference: networks
of neurons compute posterior beliefs over unobserved causes given the sensory inputs. …

With the recent trend of applying machine learning in every aspect of human life, it is important
to incorporate fairness into the core of the predictive algorithms. We address the problem …

We introduce a unifying generalization of the Lovász theta function, and the associated
geometric embedding, for graphs with weights on both nodes and edges. We show how it can be …

To develop a complete understanding of visual perception, it is important to investigate the
system at all three levels proposed by Marr: computational goal of the visual system, algorithm…

Collecting new information about the outside world is a key aspect of brain function. In the
context of vision, we move our eyes multiple times per second to accumulate evidence about a …

Deep learning architectures with huge number of parameters are often compressed using
pruning techniques. Two classes of pruning techniques are node pruning and edge pruning. A …

Unbiased data collection is essential to guaranteeing fairness in artificial intelligence models.
Implicit bias, a form of behavioral conditioning that leads us to attribute predetermined …