Search Results for author: Klaus Greff

Found 18 papers, 11 papers with code

Scene Representation Transformer: Geometry-Free Novel View Synthesis Through Set-Latent Scene Representations

no code implementations25 Nov 2021 Mehdi S. M. Sajjadi, Henning Meyer, Etienne Pot, Urs Bergmann, Klaus Greff, Noha Radwan, Suhani Vora, Mario Lucic, Daniel Duckworth, Alexey Dosovitskiy, Jakob Uszkoreit, Thomas Funkhouser, Andrea Tagliasacchi

In this work, we propose the Scene Representation Transformer (SRT), a method which processes posed or unposed RGB images of a new area, infers a "set-latent scene representation", and synthesises novel views, all in a single feed-forward pass.

Novel View Synthesis Semantic Segmentation

Conditional Object-Centric Learning from Video

no code implementations ICLR 2022 Thomas Kipf, Gamaleldin F. Elsayed, Aravindh Mahendran, Austin Stone, Sara Sabour, Georg Heigold, Rico Jonschkowski, Alexey Dosovitskiy, Klaus Greff

Object-centric representations are a promising path toward more systematic generalization by providing flexible abstractions upon which compositional world models can be built.

Instance Segmentation Optical Flow Estimation +2

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.

Learning Object-Centric Video Models by Contrasting Sets

no code implementations20 Nov 2020 Sindy Löwe, Klaus Greff, Rico Jonschkowski, Alexey Dosovitskiy, Thomas Kipf

We address this problem by introducing a global, set-based contrastive loss: instead of contrasting individual slot representations against one another, we aggregate the representations and contrast the joined sets against one another.

Future prediction Self-Supervised Learning

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

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.

Highway and Residual Networks learn Unrolled Iterative Estimation

no code implementations22 Dec 2016 Klaus Greff, Rupesh K. Srivastava, Jürgen Schmidhuber

We demonstrate that this viewpoint directly leads to the construction of Highway and Residual networks.

Tagger: Deep Unsupervised Perceptual Grouping

2 code implementations NeurIPS 2016 Klaus Greff, Antti Rasmus, Mathias Berglund, Tele Hotloo Hao, Jürgen Schmidhuber, Harri Valpola

We present a framework for efficient perceptual inference that explicitly reasons about the segmentation of its inputs and features.

General Classification

Scalable Gradient-Based Tuning of Continuous Regularization Hyperparameters

1 code implementation20 Nov 2015 Jelena Luketina, Mathias Berglund, Klaus Greff, Tapani Raiko

Hyperparameter selection generally relies on running multiple full training trials, with selection based on validation set performance.

Hyperparameter Optimization

Binding via Reconstruction Clustering

1 code implementation19 Nov 2015 Klaus Greff, Rupesh Kumar Srivastava, Jürgen Schmidhuber

Disentangled distributed representations of data are desirable for machine learning, since they are more expressive and can generalize from fewer examples.

Denoising Representation Learning

Training Very Deep Networks

4 code implementations NeurIPS 2015 Rupesh Kumar Srivastava, Klaus Greff, Jürgen Schmidhuber

Theoretical and empirical evidence indicates that the depth of neural networks is crucial for their success.

Image Classification

Highway Networks

3 code implementations3 May 2015 Rupesh Kumar Srivastava, Klaus Greff, Jürgen Schmidhuber

There is plenty of theoretical and empirical evidence that depth of neural networks is a crucial ingredient for their success.

Language Modelling

LSTM: A Search Space Odyssey

12 code implementations13 Mar 2015 Klaus Greff, Rupesh Kumar Srivastava, Jan Koutník, Bas R. Steunebrink, Jürgen Schmidhuber

Several variants of the Long Short-Term Memory (LSTM) architecture for recurrent neural networks have been proposed since its inception in 1995.

Handwriting Recognition Music Modeling +1

A Clockwork RNN

5 code implementations14 Feb 2014 Jan Koutník, Klaus Greff, Faustino Gomez, Jürgen Schmidhuber

Sequence prediction and classification are ubiquitous and challenging problems in machine learning that can require identifying complex dependencies between temporally distant inputs.

General Classification

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