Courtney J. Spoerer

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.

This paper compares recurrent convolutional neural networks with feedforward models for object recognition, finding recurrent networks more accurate and robust, especially under occlusion and noise.