Search Results for author: Jan Stühmer

Found 7 papers, 3 papers with code

Connectivity Optimized Nested Graph Networks for Crystal Structures

1 code implementation27 Feb 2023 Robin Ruff, Patrick Reiser, Jan Stühmer, Pascal Friederich

Graph neural networks (GNNs) have been applied to a large variety of applications in materials science and chemistry.

graph construction

HyperInvariances: Amortizing Invariance Learning

no code implementations17 Jul 2022 Ruchika Chavhan, Henry Gouk, Jan Stühmer, Timothy Hospedales

Providing invariances in a given learning task conveys a key inductive bias that can lead to sample-efficient learning and good generalisation, if correctly specified.

Inductive Bias

Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Difference

1 code implementation CVPR 2022 Shell Xu Hu, Da Li, Jan Stühmer, Minyoung Kim, Timothy M. Hospedales

To this end, we explore few-shot learning from the perspective of neural network architecture, as well as a three stage pipeline of network updates under different data supplies, where unsupervised external data is considered for pre-training, base categories are used to simulate few-shot tasks for meta-training, and the scarcely labelled data of an novel task is taken for fine-tuning.

Few-Shot Image Classification Few-Shot Learning +1

HoloLens 2 Research Mode as a Tool for Computer Vision Research

1 code implementation25 Aug 2020 Dorin Ungureanu, Federica Bogo, Silvano Galliani, Pooja Sama, Xin Duan, Casey Meekhof, Jan Stühmer, Thomas J. Cashman, Bugra Tekin, Johannes L. Schönberger, Pawel Olszta, Marc Pollefeys

Mixed reality headsets, such as the Microsoft HoloLens 2, are powerful sensing devices with integrated compute capabilities, which makes it an ideal platform for computer vision research.

Mixed Reality

Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations

no code implementations5 Sep 2019 Jan Stühmer, Richard E. Turner, Sebastian Nowozin

Second, we demonstrate that the proposed prior encourages a disentangled latent representation which facilitates learning of disentangled representations.

Disentanglement Variational Inference

ISA-VAE: Independent Subspace Analysis with Variational Autoencoders

no code implementations ICLR 2019 Jan Stühmer, Richard Turner, Sebastian Nowozin

Extensive quantitative and qualitative experiments demonstrate that the proposed prior mitigates the trade-off introduced by modified cost functions like beta-VAE and TCVAE between reconstruction loss and disentanglement.

Disentanglement Variational Inference

Variational Inference for Data-Efficient Model Learning in POMDPs

no code implementations23 May 2018 Sebastian Tschiatschek, Kai Arulkumaran, Jan Stühmer, Katja Hofmann

In this paper we propose DELIP, an approach to model learning for POMDPs that utilizes amortized structured variational inference.

Decision Making Decision Making Under Uncertainty +1

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