Search Results for author: Ivan Ustyuzhaninov

Found 10 papers, 6 papers with code

Rotation-invariant clustering of neuronal responses in primary visual cortex

no code implementations ICLR 2020 Ivan Ustyuzhaninov, Santiago A. Cadena, Emmanouil Froudarakis, Paul G. Fahey, Edgar Y. Walker, Erick Cobos, Jacob Reimer, Fabian H. Sinz, Andreas S. Tolias, Matthias Bethge, Alexander S. Ecker

Similar to a convolutional neural network (CNN), the mammalian retina encodes visual information into several dozen nonlinear feature maps, each formed by one ganglion cell type that tiles the visual space in an approximately shift-equivariant manner.

Clustering Open-Ended Question Answering

Towards causal generative scene models via competition of experts

no code implementations27 Apr 2020 Julius von Kügelgen, Ivan Ustyuzhaninov, Peter Gehler, Matthias Bethge, Bernhard Schölkopf

Learning how to model complex scenes in a modular way with recombinable components is a pre-requisite for higher-order reasoning and acting in the physical world.

Inductive Bias

Compositional uncertainty in deep Gaussian processes

1 code implementation17 Sep 2019 Ivan Ustyuzhaninov, Ieva Kazlauskaite, Markus Kaiser, Erik Bodin, Neill D. F. Campbell, Carl Henrik Ek

Similarly, deep Gaussian processes (DGPs) should allow us to compute a posterior distribution of compositions of multiple functions giving rise to the observations.

Bayesian Inference Gaussian Processes +1

Accurate, reliable and fast robustness evaluation

1 code implementation NeurIPS 2019 Wieland Brendel, Jonas Rauber, Matthias Kümmerer, Ivan Ustyuzhaninov, Matthias Bethge

We here develop a new set of gradient-based adversarial attacks which (a) are more reliable in the face of gradient-masking than other gradient-based attacks, (b) perform better and are more query efficient than current state-of-the-art gradient-based attacks, (c) can be flexibly adapted to a wide range of adversarial criteria and (d) require virtually no hyperparameter tuning.

Monotonic Gaussian Process Flow

1 code implementation30 May 2019 Ivan Ustyuzhaninov, Ieva Kazlauskaite, Carl Henrik Ek, Neill D. F. Campbell

We propose a new framework for imposing monotonicity constraints in a Bayesian nonparametric setting based on numerical solutions of stochastic differential equations.

Gaussian Processes Time Series +1

One-Shot Instance Segmentation

3 code implementations28 Nov 2018 Claudio Michaelis, Ivan Ustyuzhaninov, Matthias Bethge, Alexander S. Ecker

We demonstrate empirical results on MS Coco highlighting challenges of the one-shot setting: while transferring knowledge about instance segmentation to novel object categories works very well, targeting the detection network towards the reference category appears to be more difficult.

Few-Shot Object Detection One-Shot Instance Segmentation +2

Sequence Alignment with Dirichlet Process Mixtures

no code implementations26 Nov 2018 Ieva Kazlauskaite, Ivan Ustyuzhaninov, Carl Henrik Ek, Neill D. F. Campbell

We present a probabilistic model for unsupervised alignment of high-dimensional time-warped sequences based on the Dirichlet Process Mixture Model (DPMM).

Gaussian Processes

One-shot Texture Segmentation

4 code implementations7 Jul 2018 Ivan Ustyuzhaninov, Claudio Michaelis, Wieland Brendel, Matthias Bethge

We introduce one-shot texture segmentation: the task of segmenting an input image containing multiple textures given a patch of a reference texture.

Texture Synthesis Using Shallow Convolutional Networks with Random Filters

no code implementations31 May 2016 Ivan Ustyuzhaninov, Wieland Brendel, Leon A. Gatys, Matthias Bethge

The current state of the art in parametric texture synthesis relies on the multi-layer feature space of deep CNNs that were trained on natural images.

Texture Synthesis

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