Search Results for author: Daniel Braun

Found 25 papers, 8 papers with code

Clause Topic Classification in German and English Standard Form Contracts

no code implementations ECNLP (ACL) 2022 Daniel Braun, Florian Matthes

So-called standard form contracts, i. e. contracts that are drafted unilaterally by one party, like terms and conditions of online shops or terms of services of social networks, are cornerstones of our modern economy.

Classification Topic Classification

NLP for Consumer Protection: Battling Illegal Clauses in German Terms and Conditions in Online Shopping

no code implementations ACL (NLP4PosImpact) 2021 Daniel Braun, Florian Matthes

Government and non-government organisations (NGOs) for consumer protection battle such terms on behalf of consumers, who often hesitate to take on legal actions themselves.

Structured Extraction of Terms and Conditions from German and English Online Shops

1 code implementation ECNLP (ACL) 2022 Tobias Schamel, Daniel Braun, Florian Matthes

While content extraction, in general, is a well-researched field and many open source libraries are available, our evaluation shows, that existing solutions cannot extract Terms and Conditions in sufficient quality, mainly because of their special structure.

Efficient Black-Box Adversarial Attacks on Neural Text Detectors

no code implementations3 Nov 2023 Vitalii Fishchuk, Daniel Braun

Neural text detectors are models trained to detect whether a given text was generated by a language model or written by a human.

Language Modelling Prompt Engineering

Visual Validation versus Visual Estimation: A Study on the Average Value in Scatterplots

no code implementations18 Jul 2023 Daniel Braun, Ashley Suh, Remco Chang, Michael Gleicher, Tatiana von Landesberger

We investigate the ability of individuals to visually validate statistical models in terms of their fit to the data.

valid

Reclaiming the Horizon: Novel Visualization Designs for Time-Series Data with Large Value Ranges

no code implementations18 Jul 2023 Daniel Braun, Rita Borgo, Max Sondag, Tatiana von Landesberger

It focuses on four main tasks commonly employed in the analysis of time-series and large value ranges visualization: identification, discrimination, estimation, and trend detection.

Time Series

Investigating Conversational Search Behavior For Domain Exploration

1 code implementation10 Jan 2023 Phillip Schneider, Anum Afzal, Juraj Vladika, Daniel Braun, Florian Matthes

Conversational search has evolved as a new information retrieval paradigm, marking a shift from traditional search systems towards interactive dialogues with intelligent search agents.

Conversational Search Information Retrieval +1

Evaluating Unsupervised Text Classification: Zero-shot and Similarity-based Approaches

2 code implementations29 Nov 2022 Tim Schopf, Daniel Braun, Florian Matthes

Text classification of unseen classes is a challenging Natural Language Processing task and is mainly attempted using two different types of approaches.

 Ranked #1 on Unsupervised Text Classification on AG News (F1-score metric)

text-classification Unsupervised Text Classification +1

Lbl2Vec: An Embedding-Based Approach for Unsupervised Document Retrieval on Predefined Topics

1 code implementation12 Oct 2022 Tim Schopf, Daniel Braun, Florian Matthes

When successively retrieving documents on different predefined topics from publicly available and commonly used datasets, we achieved an average area under the receiver operating characteristic curve value of 0. 95 on one dataset and 0. 92 on another.

Document Classification Retrieval +2

N-QGN: Navigation Map from a Monocular Camera using Quadtree Generating Networks

no code implementations24 Feb 2022 Daniel Braun, Olivier Morel, Pascal Vasseur, Cédric Demonceaux

Monocular depth estimation has been a popular area of research for several years, especially since self-supervised networks have shown increasingly good results in bridging the gap with supervised and stereo methods.

3D Reconstruction Autonomous Navigation +1

Neural-Network Heuristics for Adaptive Bayesian Quantum Estimation

no code implementations4 Mar 2020 Lukas J. Fiderer, Jonas Schuff, Daniel Braun

Quantum metrology promises unprecedented measurement precision but suffers in practice from the limited availability of resources such as the number of probes, their coherence time, or non-classical quantum states.

Reinforcement Learning (RL)

SimpleNLG-DE: Adapting SimpleNLG 4 to German

1 code implementation WS 2019 Daniel Braun, Kira Klimt, Daniela Schneider, Florian Matthes

SimpleNLG is a popular open source surface realiser for the English language.

Improving the dynamics of quantum sensors with reinforcement learning

no code implementations22 Aug 2019 Jonas Schuff, Lukas J. Fiderer, Daniel Braun

Recently proposed quantum-chaotic sensors achieve quantum enhancements in measurement precision by applying nonlinear control pulses to the dynamics of the quantum sensor while using classical initial states that are easy to prepare.

Position reinforcement-learning +1

Trade--off relations for operation entropy of complementary quantum channels

no code implementations9 Aug 2019 Jakub Czartowski, Daniel Braun, Karol Życzkowski

The entropy of a quantum operation, defined as the von Neumann entropy of the corresponding Choi-Jamio{\l}kowski state, characterizes the coupling of the principal system with the environment.

Quantum Physics

Hierarchical State Abstractions for Decision-Making Problems with Computational Constraints

no code implementations22 Oct 2017 Daniel T. Larsson, Daniel Braun, Panagiotis Tsiotras

In this semi-tutorial paper, we first review the information-theoretic approach to account for the computational costs incurred during the search for optimal actions in a sequential decision-making problem.

Decision Making

A Nonparametric Conjugate Prior Distribution for the Maximizing Argument of a Noisy Function

no code implementations NeurIPS 2012 Pedro Ortega, Jordi Grau-Moya, Tim Genewein, David Balduzzi, Daniel Braun

We propose a novel Bayesian approach to solve stochastic optimization problems that involve finding extrema of noisy, nonlinear functions.

Stochastic Optimization

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