Search Results for author: Sjoerd van Steenkiste

Found 20 papers, 7 papers with code

Exploring through Random Curiosity with General Value Functions

no code implementations18 Nov 2022 Aditya Ramesh, Louis Kirsch, Sjoerd van Steenkiste, Jürgen Schmidhuber

Furthermore, RC-GVF significantly outperforms previous methods in the absence of ground-truth episodic counts in the partially observable MiniGrid environments.

Efficient Exploration

Object Scene Representation Transformer

no code implementations14 Jun 2022 Mehdi S. M. Sajjadi, Daniel Duckworth, Aravindh Mahendran, Sjoerd van Steenkiste, Filip Pavetić, Mario Lučić, Leonidas J. Guibas, Klaus Greff, Thomas Kipf

A compositional understanding of the world in terms of objects and their geometry in 3D space is considered a cornerstone of human cognition.

Novel View Synthesis Representation Learning

Unsupervised Learning of Temporal Abstractions with Slot-based Transformers

1 code implementation25 Mar 2022 Anand Gopalakrishnan, Kazuki Irie, Jürgen Schmidhuber, Sjoerd van Steenkiste

The discovery of reusable sub-routines simplifies decision-making and planning in complex reinforcement learning problems.

Decision Making

Test-time adaptation with slot-centric models

no code implementations21 Mar 2022 Mihir Prabhudesai, Anirudh Goyal, Sujoy Paul, Sjoerd van Steenkiste, Mehdi S. M. Sajjadi, Gaurav Aggarwal, Thomas Kipf, Deepak Pathak, Katerina Fragkiadaki

Current supervised visual detectors, though impressive within their training distribution, often fail to segment out-of-distribution scenes into their constituent entities.

Image Classification Image Segmentation +6

On the Binding Problem in Artificial Neural Networks

no code implementations9 Dec 2020 Klaus Greff, Sjoerd van Steenkiste, Jürgen Schmidhuber

Contemporary neural networks still fall short of human-level generalization, which extends far beyond our direct experiences.

Hierarchical Relational Inference

no code implementations7 Oct 2020 Aleksandar Stanić, Sjoerd van Steenkiste, Jürgen Schmidhuber

Common-sense physical reasoning in the real world requires learning about the interactions of objects and their dynamics.

Common Sense Reasoning

Are Neural Nets Modular? Inspecting Functional Modularity Through Differentiable Weight Masks

1 code implementation ICLR 2021 Róbert Csordás, Sjoerd van Steenkiste, Jürgen Schmidhuber

Neural networks (NNs) whose subnetworks implement reusable functions are expected to offer numerous advantages, including compositionality through efficient recombination of functional building blocks, interpretability, preventing catastrophic interference, etc.

Systematic Generalization

A Perspective on Objects and Systematic Generalization in Model-Based RL

no code implementations3 Jun 2019 Sjoerd van Steenkiste, Klaus Greff, Jürgen Schmidhuber

In order to meet the diverse challenges in solving many real-world problems, an intelligent agent has to be able to dynamically construct a model of its environment.

Systematic Generalization

A Case for Object Compositionality in Deep Generative Models of Images

no code implementations ICLR 2019 Sjoerd van Steenkiste, Karol Kurach, Sylvain Gelly

In this work we propose to structure the generator of a GAN to consider objects and their relations explicitly, and generate images by means of composition.

FVD: A new Metric for Video Generation

no code implementations ICLR Workshop DeepGenStruct 2019 Thomas Unterthiner, Sjoerd van Steenkiste, Karol Kurach, Raphaël Marinier, Marcin Michalski, Sylvain Gelly

While recent generative models of video have had some success, current progress is hampered by the lack of qualitative metrics that consider visual quality, temporal coherence, and diversity of samples.

Representation Learning Video Generation

Towards Accurate Generative Models of Video: A New Metric & Challenges

2 code implementations3 Dec 2018 Thomas Unterthiner, Sjoerd van Steenkiste, Karol Kurach, Raphael Marinier, Marcin Michalski, Sylvain Gelly

To this extent we propose Fr\'{e}chet Video Distance (FVD), a new metric for generative models of video, and StarCraft 2 Videos (SCV), a benchmark of game play from custom starcraft 2 scenarios that challenge the current capabilities of generative models of video.

Representation Learning Starcraft +1

Investigating Object Compositionality in Generative Adversarial Networks

no code implementations ICLR 2019 Sjoerd van Steenkiste, Karol Kurach, Jürgen Schmidhuber, Sylvain Gelly

We present a minimal modification of a standard generator to incorporate this inductive bias and find that it reliably learns to generate images as compositions of objects.

Image Generation Inductive Bias +3

Neural Expectation Maximization

1 code implementation NeurIPS 2017 Klaus Greff, Sjoerd van Steenkiste, Jürgen Schmidhuber

Many real world tasks such as reasoning and physical interaction require identification and manipulation of conceptual entities.

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