Export 585 results:
Adaptive Coding for Dynamic Sensory Inference. eLife (In Press).
Comparing human and monkey neural circuits for processing social scenes. Organization for Computational Neurosciences - CNS 2018 (In Press). at <http://www.cnsorg.org/cns-2018>
Development of automated interictal spike detector. 40th International Conference of the IEEE Engineering in Medicine and Biology Society - EMBC 2018 (In Press). at <https://embc.embs.org/2018/>
Image interpretation above and below the object level. Proceedings of the Royal Society: Interface Focus (In Press).
Learning scene gist with convolutional neural networks to improve object recognition. 2018 52nd Annual Conference on Information Sciences and Systems (CISS) (In Press). doi:10.1109/CISS.2018.8362305
Lucky or clever? From changed expectations to attributions of responsibility. Cognition (In Press).
Minimal videos: Trade-off between spatial and temporal information in human and machine vision. Cognition (In Press).
A neural network trained to predict future video frames mimics critical properties of biological neuronal responses and perception. Nature Machine Learning (In Press).
The ability to predict actions of others from distributed cues is still developing in children. PsyArXiv Preprints (2020). doi:10.31234/osf.io/pu3tf
Beyond the feedforward sweep: feedback computations in the visual cortex. The Year in Cognitive Neuroscience (2020). doi:10.1111/nyas.14320
Can Deep Learning Recognize Subtle Human Activities?. CVPR 2020 (2020).
Complexity Control by Gradient Descent in Deep Networks. Nature Communications 11, (2020).
Efficient inverse graphics in biological face processing. Science Advances 6, eaax5979 (2020).
Putting visual object recognition in context. CVPR 2020 (2020).
Scale and translation-invariance for novel objects in human vision. Scientific Reports 10, (2020).
Are topographic deep convolutional neural networks better models of the ventral visual stream?. Conference on Cognitive Computational Neuroscience (2019).
Beating SGD Saturation with Tail-Averaging and Minibatching. Neural Information Processing Systems (NeurIPS 2019) (2019).
Biologically-Plausible Learning Algorithms Can Scale to Large Datasets. International Conference on Learning Representations (2019).
Biologically-plausible learning algorithms can scale to large datasets. ICLR 2019 (2019).