Publications

(2022). The bittersweet lesson: data-rich models narrow the behavioural gap to human vision.

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(2022). DeepGaze vs SceneWalk: what can DNNs and biological scan path models teach each other?.

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(2022). Disentanglement and generalization under correlation shifts.

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(2022). Deepgaze iii: Modeling free-viewing human scanpaths with deep learning.

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(2022). Digital twin reveals combinatorial code of non-linear computations in the mouse primary visual cortex.

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(2022). Robust deep learning object recognition models rely on low frequency information in natural images.

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(2022). Efficient coding of natural scenes improves neural system identification.

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(2022). Semantic object-scene inconsisten.

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(2022). ImageNet-D: A new challenging robustness dataset inspired by domain adaptation.

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(2022). Diverse task-driven modeling of macaque V4 reveals functional specialization towards semantic tasks.

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(2022). A chromatic feature detector in the retina signals visual context changes.

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(2021). Partial success in closing the gap between human and machine vision.

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(2021). How Well do Feature Visualizations Support Causal Understanding of CNN Activations?.

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(2021). Contextualised meaning maps do not predict how semantic object-context inconsistencies change human gaze behaviour.

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(2021). 2.1 Decision-Based Adversarial Attacks.

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(2021). If your data distribution shifts, use self-learning.

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(2021). Object-context inconsistencies affect gaze behavior differently than predicted by contextualized meaning maps.

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(2021). New enhancements to the DeepGaze models for a better understanding of human scanpaths.

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(2021). There is no evidence that meaning maps capture semantic information relevant to gaze guidance: Reply to Henderson, Hayes, Peacock, and Rehrig (2021).

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(2021). Visual representation learning does not generalize strongly within the same domain.

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(2021). Contrastive learning inverts the data generating process.

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(2021). Learning divisive normalization in primary visual cortex.

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(2021). Semantic object-scene inconsistencies affect eye movements, but not in the way predicted by contextualized meaning maps.

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(2021). Adapting imagenet-scale models to complex distribution shifts with self-learning.

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(2021). Five points to check when comparing visual perception in humans and machines.

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(2021). State-of-the-art in human scanpath prediction.

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(2021). Pretraining boosts out-of-domain robustness for pose estimation.

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(2021). Out-of-distribution generalization of internal models is correlated with reward.

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(2021). Meaning maps and saliency models based on deep convolutional neural networks are insensitive to image meaning when predicting human fixations.

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(2021). Künstliche Intelligenz–Die dritte Welle.

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(2021). Five points to check when com.

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(2021). DeepGaze IIE: Calibrated prediction in and out-of-domain for state-of-the-art saliency modeling.

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(2021). Benchmarking unsupervised object representations for video sequences.

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(2021). A diverse task-driven characterization of early and mid-level representations of the primate ventral stream.

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(2020). Shortcut learning in deep neural networks.

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(2020). Closing the generalization gap in one-shot object detection.

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(2020). Exemplary natural images explain CNN activations better than state-of-the-art feature visualization.

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(2020). Unintended cue learning: Lessons for deep learning from experimental psychology.

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(2020). Analyzing task-specific patterns in human scanpaths.

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(2020). Exemplary natural images explain CNN activations better than feature visualizations.

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(2020). A simple way to make neural networks robust against diverse image corruptions.

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(2020). Towards nonlinear disentanglement in natural data with temporal sparse coding.

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(2020). Imagenet performance correlates with pose estimation robustness and generalization on out-of-domain data.

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(2020). Unmasking the inductive biases of unsupervised object representations for video sequences.

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(2020). Task-driven hierarchical deep neural network models of the proprioceptive pathway.

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(2020). Towards causal generative scene models via competition of experts.

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(2020). The temporal structure of the inner retina at a single glance.

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(2020). System identification with biophysical constraints: A circuit model of the inner retina.

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(2020). Rotation-invariant clustering of neuronal responses in primary visual cortex.

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(2020). Natural images are more informative for interpreting cnn activations than state-of-the-art synthetic feature visualizations.

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(2020). Measuring the importance of temporal features in video saliency.

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(2020). Improving robustness against common corruptions by covariate shift adaptation.

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(2020). Generalized Invariant Risk Minimization: relating adaptation and invariant representation learning.

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(2020). Adversarial vision challenge.

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(2019). Correction to: Detecting distortions of peripherally presented letter stimuli under crowded conditions.

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(2019). " Detecting distortions of peripherally presented letter stimuli under crowded conditions": Correction..

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(2019). Engineering a less artificial intelligence.

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(2019). How well do deep neural networks trained on object recognition characterize the mouse visual system?.

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(2019). Inducing a human-like shape bias leads to emergent human-level distortion robustness in CNNs.

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(2019). Hole-in-the-wall: Perception of 3D shape and affordances from static images in humans and machines.

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(2019). Comparing Search Strategies of Humans and Machines in Clutter.

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(2019). Behavioural evidence for the existence of a spatiotopic free-viewing saliency map.

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(2019). Benchmarking robustness in object detection: Autonomous driving when winter is coming.

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(2019). Using DeepLabCut for 3D markerless pose estimation across species and behaviors.

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(2019). Image content is more important than Bouma’s Law for scene metamers.

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(2019). Deep convolutional models improve predictions of macaque V1 responses to natural images.

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(2019). Probing Neural Decision-Making in Behavioral Models of Scanpath Prediction.

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(2019). Comparing Humans and Deep Neural Networks on Visual Shape Judgments in Cluttered Images.

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(2019). Learning from brains how to regularize machines.

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(2019). Accurate, reliable and fast robustness evaluation.

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(2018). ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness.

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(2018). Faster processing of moving compared with flashed bars in awake macaque V1 provides a neural correlate of the flash lag illusion.

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(2018). Excessive invariance causes adversarial vulnerability.

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(2018). A rotation-equivariant convolutional neural network model of primary visual cortex.

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(2018). Extending deepgaze ii: Scanpath prediction from deep features.

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(2018). DeepLabCut: markerless pose estimation of user-defined body parts with deep learning.

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(2018). Comparing the ability of humans and DNNs to recognise closed contours in cluttered images.

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(2018). Adversarial vision challenge.

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(2018). Markerless tracking of user-defined anatomical features with deep learning.

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(2018). Attentional fluctuations induce shared variability in macaque primary visual cortex.

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(2018). One-shot segmentation in clutter.

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(2018). Scaling of information in large sensory populations.

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(2018). Towards the first adversarially robust neural network model on MNIST.

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(2018). Community-based benchmarking improves spike rate inference from two-photon calcium imaging data.

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(2018). Towards goal-driven deep neural network models to elucidate human arm proprioception.

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(2018). Trace your sources in large-scale data: one ring to find them all.

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(2018). 伯克利 AI 研究院利用反向课程学习, 改善强化学习智能体.

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(2018). Saliency benchmarking made easy: Separating models, maps and metrics.

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(2018). Salad: A toolbox for semi-supervised adaptive learning across domains.

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(2018). Multi-task generalization and adaptation between noisy digit datasets: An empirical study.

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(2018). Introduction to NIPS 2017 Competition Track.

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(2018). Generalisation in humans and deep neural networks.

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(2018). Diverse feature visualizations reveal invariances in early layers of deep neural networks.

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(2018). Consistent Saliency Benchmarking: How One Model Can Win on All Metrics.

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(2017). Guiding human gaze with convolutional neural networks.

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(2017). Neural system identification for large populations separating what and where.

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(2017). Signatures of criticality arise from random subsampling in simple population models.

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(2017). Texture and art with deep neural networks.

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(2017). A parametric texture model based on deep convolutional features closely matches texture appearance for humans.

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(2017). Towards matching peripheral appearance for arbitrary natural images using deep features.

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(2017). Of human observers and deep neural networks: A detailed psychophysical comparison.

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(2017). Mixed latent variable model of attention in V1.

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(2017). Community-based benchmarking improves spike inference from two-photon calcium imaging data.

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(2017). Foolbox: A python toolbox to benchmark the robustness of machine learning models.

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(2017). Comparing deep neural networks against humans: object recognition when the signal gets weaker.

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(2017). Detecting distortions of peripherally presented letter stimuli under crowded conditions.

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(2017). Inhibition decorrelates visual feature representations in the inner retina.

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(2017). Synthesising dynamic textures using convolutional neural networks.

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(2017). Standardizing and benchmarking data analysis for calcium imaging.

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(2017). Boosting olfactory cocktail-party performance by semi-supervised learning in mice.

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(2017). Methods and measurements to compare men against machines.

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(2017). Understanding low-and high-level contributions to fixation prediction.

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(2017). Predicting Fixations From Deep and Low-Level Features.

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(2017). Die Retina im Rausch der Kanäle.

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(2017). Controlling perceptual factors in neural style transfer.

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(2016). Towards matching the peripheral visual appearance of arbitrary scenes using deep convolutional neural networks.

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(2016). DeepGaze II: Reading fixations from deep features trained on object recognition.

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(2016). Large scale blind source separation.

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(2016). Reading out olfactory receptors: feedforward circuits detect odors in mixtures without demixing.

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(2016). Texture synthesis using random shallow neural networks.

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(2016). Statistical inference with the Elliptical Gamma Distribution.

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(2016). Inference and mixture modeling with the Elliptical Gamma Distribution.

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(2016). Seeking summary statistics that match peripheral visual appearance using naturalistic textures generated by Deep Neural Networks.

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(2016). Preserving color in neural artistic style transfer.

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(2016). Texture synthesis using shallow convolutional networks with random filters.

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(2016). Benchmarking spike rate inference in population calcium imaging.

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(2016). On the structure of neuronal population activity under fluctuations in attentional state.

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(2016). Supervised learning sets benchmark for robust spike rate inference from calcium imaging signals.

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(2016). Electrophysiological, transcriptomic and morphologic profiling of single neurons using Patch-seq.

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(2016). The functional diversity of retinal ganglion cells in the mouse.

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(2016). Using Deep Features to Predict Where People Look.

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(2016). Texture Modelling Using Convolutional Neural Networks.

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(2016). Image style transfer using convolutional neural networks.

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(2016). A goal-driven deep learning approach for V1 system identification.

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(2015). Information-theoretic model comparison unifies saliency metrics.

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(2015). A note on the evaluation of generative models.

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(2015). Modeling natural image statistics.

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(2015). Scaling of information in large sensory neuronal populations.

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(2015). Metamers of the ventral stream revisited.

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(2015). A neural algorithm of artistic style.

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(2015). Unexpected functional diversity among mouse retinal ganglion cell types.

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(2015). Synaptic unreliability facilitates information transmission in balanced cortical populations.

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(2015). A generative model of natural texture surrogates.

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(2015). On the structure of population activity under fluctuations in attentional state.

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(2015). Following the visual signal across the entire mouse retina: From cone calcium to ganglion cell spikes.

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(2015). Correlations and signatures of criticality in neural population models.

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(2015). Data modeling with the elliptical gamma distribution.

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(2015). Texture synthesis using convolutional neural networks.

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(2015). Generative image modeling using spatial lstms.

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(2015). A neural algorithm of artistic style. arXiv.

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(2014). Deep gaze i: Boosting saliency prediction with feature maps trained on imagenet.

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(2014). Natter: A Python Natural Image Statistics Toolbox.

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(2014). What the mouse eye tells the mouse brain: Fingerprinting the retinal ganglion cell types of the mouse retina.

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(2014). Information theoretic analysis of neural populations.

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(2014). Autonomous Learning.

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(2014). Population code in mouse V1 facilitates readout of natural scenes through increased sparseness.

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(2014). State dependence of noise correlations in macaque primary visual cortex.

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(2014). Slowness and sparseness have diverging effects on complex cell learning.

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(2014). Efficient Population Coding.

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(2014). How much signal is there in the noise?.

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(2013). Beyond GLMs: a generative mixture modeling approach to neural system identification.

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(2013). Neural Adaptation as Bayesian Inference.

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(2013). Information Coding in the Variance of Neural Activity.

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(2013). Recording the entire visual representation along the vertical pathway in the retina.

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(2013). The bipolar cell terminal as a selective spatio-temporal filter.

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(2013). Encoding of natural scene statistics in the primary visual cortex of the mouse.

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(2013). A generative model of natural images as patchworks of textures.

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(2013). Inferring decoding strategies from choice probabilities in the presence of correlated variability.

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(2013). How sensitive is the human visual system to the local statistics of natural images?.

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(2013). Spikes in mammalian bipolar cells support temporal layering of the inner retina.

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(2012). A fast and simple population code for orientation in primate V1.

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(2012). The correlation structure induced by fluctuations in attention.

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(2012). A new perceptual bias reveals suboptimal population decoding of sensory responses.

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(2012). Surprising Speed Jitter Invariance Of Pattern Matching In Random Dot Stereopsis.

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(2012). Inferring decoding strategy from choice probabilities in the presence of noise correlations.

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(2011). LNP Analysis of Primary Whisker Afferents.

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(2011). A multiscale model of natural images.

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(2011). Gravitational lensing accuracy testing 2010 (GREAT10) challenge handbook.

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(2011). The effect of noise correlations in populations of diversely tuned neurons.

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(2011). Gaussian process methods for estimating cortical maps.

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(2011). Reassessing optimal neural population codes with neurometric functions.

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(2011). Perceptual Sensitivity to Statistical Regularities in Natural Images.

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(2011). Reconstructing stimuli from the spike times of leaky integrate and fire neurons.

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(2011). Optimal Population Coding, Revisited.

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(2010). In All Likelihood, Deep Belief Is Not Enough.

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(2010). Estimating cortical maps with Gaussian process models.

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(2010). What is the Goal of Complex Cell Coding in V1?.

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(2010). New Estimate for the Redundancy of Natural Images.

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(2010). Likelihood Estimation in Deep Belief Networks.

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(2010). Results of the GREAT08 Challenge: an image analysis competition for cosmological lensing.

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(2010). Decorrelated Firing in Cortical Microcircuits.

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(2010). Implications of correlated neuronal noise in decision making circuits for physiology and behavior.

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(2010). Decorrelated neuronal firing in cortical microcircuits.

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(2010). Bayesian inference for generalized linear models for spiking neurons.

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(2009). Method and device for image compression.

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(2009). Unsupervised learning of disparity maps from stereo images.

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(2009). Neuronal decision-making with realistic spiking models.

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(2009). Inferring characteristics of stimulus encoding mechanisms using rippled noise stimuli.

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(2009). Hierarchical models of natural images.

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(2009). Characterization of the p-generalized normal distribution.

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(2009). Natural image coding in V1: how much use is orientation selectivity?.

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(2009). The effect of pairwise neural correlations on global population statistics.

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(2009). Generating spike trains with specified correlation coefficients.

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(2009). Sensory input statistics and network mechanisms in primate primary visual cortex.

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(2009). Neurometric function analysis of short-term population codes.

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(2008). Image library for unsupervised learning of depth from stereo.

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(2008). Modeling populations of spiking neurons with the Dichotomized Gaussian distribution.

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(2008). Flexible Models for Population Spike Trains.

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(2008). Towards the neural basis of the flash-lag effect.

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(2008). Pairwise Correlations and Multineuronal Firing Patterns in the Primary Visual Cortex of the Awake, Behaving Macaque.

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(2007). Studying the effects of noise correlations on population coding using a sampling method.

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(2007). The independent components of natural images are perceptually dependent.

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(2007). Unsupervised learning of a steerable basis for invariant image representations.

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(2007). Identifying temporal population codes in the retina using canonical correlation analysis.

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(2007). Estimating Population Receptive Fields in Space and Time.

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(2007). Bayesian Receptive Fields and Neural Couplings with Sparsity Prior and Error Bars.

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(2007). Bayesian inference for spiking neuron models with a sparsity prior.

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(2006). Bayesian Neural System identification: error bars, receptive fields and neural couplings.

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(2004). Dynamics of population rate codes in ensembles of neocortical neurons.

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(2004). Neural Networks Referees in 2003.

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(2003). Geheimsprache der Neuronen.

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(2003). El lenguaje de las neuronas.

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(2003). El lenguaje de las neuronas.

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(2002). Optimal short-term population coding: When Fisher information fails.

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(2002). Population coding with unreliable spikes.

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(2002). Geheimsprache der Neuronen.

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(2002). Binary tuning is optimal for neural rate coding with high temporal resolution.

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(2001). Spike-frequency adaptation: phenomenological model and experimental tests.

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(2000). Spike-Frequency Adaptation: Phenomenological.

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(1999). Brief pauses as signals for degressing synapses.

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(1998). Coherence detection with dynamic synapses.

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(0001). Unsupervised object learning via common fate.

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(0001). Deep feature matching vs spatio-temporal energy filtering for robust moving object segmentation.

(0001). Common fate based object learning in machines and humans.

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