Johannes Mehrer

The paper compares nine DNN architectures to explain human inferior temporal cortex representational geometry, finding trained models outperform untrained ones and all architectures achieve similar performance after training and fitting.

This study shows that small changes in initial conditions of deep neural networks lead to significant differences in their representations, even when classification performance is similar.

This paper shows recurrent neural networks outperform feedforward models in visual recognition tasks, enabling flexible speed-accuracy trade-offs and better prediction of human reaction times.