Search Results for author: Andreas Stephan

Found 8 papers, 6 papers with code

WeaNF”:" Weak Supervision with Normalizing Flows

no code implementations RepL4NLP (ACL) 2022 Andreas Stephan, Benjamin Roth

In this work, we explore a novel direction of generative modeling for weak supervision”:" Instead of modeling the output of the annotation process (the labeling function matches), we generatively model the input-side data distributions (the feature space) covered by labeling functions.

Counterfactual Reasoning with Knowledge Graph Embeddings

1 code implementation11 Mar 2024 Lena Zellinger, Andreas Stephan, Benjamin Roth

We further observe that KGEs adapted with COULDD solidly detect plausible counterfactual changes to the graph that follow these patterns.

counterfactual Counterfactual Reasoning +2

Text-Guided Image Clustering

1 code implementation5 Feb 2024 Andreas Stephan, Lukas Miklautz, Kevin Sidak, Jan Philip Wahle, Bela Gipp, Claudia Plant, Benjamin Roth

We, therefore, propose Text-Guided Image Clustering, i. e., generating text using image captioning and visual question-answering (VQA) models and subsequently clustering the generated text.

Clustering Image Captioning +3

Weaker Than You Think: A Critical Look at Weakly Supervised Learning

1 code implementation27 May 2023 Dawei Zhu, Xiaoyu Shen, Marius Mosbach, Andreas Stephan, Dietrich Klakow

In this paper, we revisit the setup of these approaches and find that the benefits brought by these approaches are significantly overestimated.

Weakly-supervised Learning

SepLL: Separating Latent Class Labels from Weak Supervision Noise

1 code implementation25 Oct 2022 Andreas Stephan, Vasiliki Kougia, Benjamin Roth

In this work, we provide a method for learning from weak labels by separating two types of complementary information associated with the labeling functions: information related to the target label and information specific to one labeling function only.

text-classification Text Classification +1

WeaNF: Weak Supervision with Normalizing Flows

2 code implementations28 Apr 2022 Andreas Stephan, Benjamin Roth

In this work, we explore a novel direction of generative modeling for weak supervision: Instead of modeling the output of the annotation process (the labeling function matches), we generatively model the input-side data distributions (the feature space) covered by labeling functions.

Knodle: Modular Weakly Supervised Learning with PyTorch

1 code implementation ACL (RepL4NLP) 2021 Anastasiia Sedova, Andreas Stephan, Marina Speranskaya, Benjamin Roth

Strategies for improving the training and prediction quality of weakly supervised machine learning models vary in how much they are tailored to a specific task or integrated with a specific model architecture.

Benchmarking BIG-bench Machine Learning +3

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