Search Results for author: Oliver Deussen

Found 22 papers, 11 papers with code

Is that really a question? Going beyond factoid questions in NLP

1 code implementation IWCS (ACL) 2021 Aikaterini-Lida Kalouli, Rebecca Kehlbeck, Rita Sevastjanova, Oliver Deussen, Daniel Keim, Miriam Butt

Research in NLP has mainly focused on factoid questions, with the goal of finding quick and reliable ways of matching a query to an answer.

3D-MuPPET: 3D Multi-Pigeon Pose Estimation and Tracking

1 code implementation29 Aug 2023 Urs Waldmann, Alex Hoi Hang Chan, Hemal Naik, Máté Nagy, Iain D. Couzin, Oliver Deussen, Bastian Goldluecke, Fumihiro Kano

To the best of our knowledge we are the first to present a framework for 2D/3D animal posture and trajectory tracking that works in both indoor and outdoor environments for up to 10 individuals.

Pose Estimation

ProSpect: Prompt Spectrum for Attribute-Aware Personalization of Diffusion Models

3 code implementations25 May 2023 Yuxin Zhang, WeiMing Dong, Fan Tang, Nisha Huang, Haibin Huang, Chongyang Ma, Tong-Yee Lee, Oliver Deussen, Changsheng Xu

We apply ProSpect in various personalized attribute-aware image generation applications, such as image-guided or text-driven manipulations of materials, style, and layout, achieving previously unattainable results from a single image input without fine-tuning the diffusion models.

Attribute Disentanglement +1

ISAAC Newton: Input-based Approximate Curvature for Newton's Method

1 code implementation1 May 2023 Felix Petersen, Tobias Sutter, Christian Borgelt, Dongsung Huh, Hilde Kuehne, Yuekai Sun, Oliver Deussen

We present ISAAC (Input-baSed ApproximAte Curvature), a novel method that conditions the gradient using selected second-order information and has an asymptotically vanishing computational overhead, assuming a batch size smaller than the number of neurons.

Second-order methods

Deep Differentiable Logic Gate Networks

1 code implementation15 Oct 2022 Felix Petersen, Christian Borgelt, Hilde Kuehne, Oliver Deussen

Recently, research has increasingly focused on developing efficient neural network architectures.

Efficient Neural Network

Differentiable Top-k Classification Learning

1 code implementation15 Jun 2022 Felix Petersen, Hilde Kuehne, Christian Borgelt, Oliver Deussen

In this work, we relax this assumption and optimize the model for multiple k simultaneously instead of using a single k. Leveraging recent advances in differentiable sorting and ranking, we propose a differentiable top-k cross-entropy classification loss.

General Classification Image Classification

Monotonic Differentiable Sorting Networks

1 code implementation ICLR 2022 Felix Petersen, Christian Borgelt, Hilde Kuehne, Oliver Deussen

We introduce a family of sigmoid functions and prove that they produce differentiable sorting networks that are monotonic.

Style Agnostic 3D Reconstruction via Adversarial Style Transfer

no code implementations20 Oct 2021 Felix Petersen, Bastian Goldluecke, Oliver Deussen, Hilde Kuehne

Recently introduced differentiable renderers can be leveraged to learn the 3D geometry of objects from 2D images, but those approaches require additional supervision to enable the renderer to produce an output that can be compared to the input image.

3D Object Reconstruction 3D Reconstruction +3

Learning with Algorithmic Supervision via Continuous Relaxations

1 code implementation NeurIPS 2021 Felix Petersen, Christian Borgelt, Hilde Kuehne, Oliver Deussen

The integration of algorithmic components into neural architectures has gained increased attention recently, as it allows training neural networks with new forms of supervision such as ordering constraints or silhouettes instead of using ground truth labels.

Propagating Distributions through Neural Networks

no code implementations29 Sep 2021 Felix Petersen, Christian Borgelt, Mikhail Yurochkin, Hilde Kuehne, Oliver Deussen

We propose a new approach to propagating probability distributions through neural networks.


A Sampling-Free Approximation of Gaussian Variational Auto-Encoders

no code implementations29 Sep 2021 Felix Petersen, Christian Borgelt, Hilde Kuehne, Oliver Deussen

We propose a sampling-free approximate formulation of Gaussian variational auto-encoders.

Differentiable Sorting Networks for Scalable Sorting and Ranking Supervision

1 code implementation9 May 2021 Felix Petersen, Christian Borgelt, Hilde Kuehne, Oliver Deussen

Sorting and ranking supervision is a method for training neural networks end-to-end based on ordering constraints.

Shape-driven Coordinate Ordering for Star Glyph Sets via Reinforcement Learning

no code implementations3 Mar 2021 Ruizhen Hu, Bin Chen, Juzhan Xu, Oliver van Kaick, Oliver Deussen, Hui Huang

Given a set of star glyphs associated to multiple class labels, we propose to use shape context descriptors to measure the perceptual distance between pairs of glyphs, and use the derived silhouette coefficient to measure the perception of class separability within the entire set.

Perceptual Distance reinforcement-learning +1

Procedural Urban Forestry

no code implementations12 Aug 2020 Till Niese, Sören Pirk, Matthias Albrecht, Bedrich Benes, Oliver Deussen

The placement of vegetation plays a central role in the realism of virtual scenes.

$C^\infty$ Smooth Algorithmic Neural Networks for Solving Inverse Problems

no code implementations NeurIPS Workshop Deep_Invers 2019 Felix Petersen, Christian Borgelt, Oliver Deussen

Artificial neural networks revolutionized many areas of computer science in recent years since they provide solutions to a number of previously unsolved problems.

Semantic Concept Spaces: Guided Topic Model Refinement using Word-Embedding Projections

no code implementations1 Aug 2019 Mennatallah El-Assady, Rebecca Kehlbeck, Christopher Collins, Daniel Keim, Oliver Deussen

We present a framework that allows users to incorporate the semantics of their domain knowledge for topic model refinement while remaining model-agnostic.

Decision Making

AlgoNet: $C^\infty$ Smooth Algorithmic Neural Networks

no code implementations16 May 2019 Felix Petersen, Christian Borgelt, Oliver Deussen

These networks integrate smooth versions of classic algorithms into the topology of neural networks.

Uncertainty-Aware Principal Component Analysis

1 code implementation3 May 2019 Jochen Görtler, Thilo Spinner, Dirk Streeb, Daniel Weiskopf, Oliver Deussen

We present a technique to perform dimensionality reduction on data that is subject to uncertainty.

Dimensionality Reduction

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