Search Results for author: Lukas Muttenthaler

Found 7 papers, 4 papers with code

Set Learning for Accurate and Calibrated Models

1 code implementation5 Jul 2023 Lukas Muttenthaler, Robert A. Vandermeulen, Qiuyi Zhang, Thomas Unterthiner, Klaus-Robert Müller

Model overconfidence and poor calibration are common in machine learning and difficult to account for when applying standard empirical risk minimization.

Human alignment of neural network representations

1 code implementation2 Nov 2022 Lukas Muttenthaler, Jonas Dippel, Lorenz Linhardt, Robert A. Vandermeulen, Simon Kornblith

Linear transformations of neural network representations learned from behavioral responses from one dataset substantially improve alignment with human similarity judgments on the other two datasets.

Odd One Out

VICE: Variational Interpretable Concept Embeddings

1 code implementation2 May 2022 Lukas Muttenthaler, Charles Y. Zheng, Patrick McClure, Robert A. Vandermeulen, Martin N. Hebart, Francisco Pereira

This paper introduces Variational Interpretable Concept Embeddings (VICE), an approximate Bayesian method for embedding object concepts in a vector space using data collected from humans in a triplet odd-one-out task.

Experimental Design Object +3

Unsupervised Evaluation for Question Answering with Transformers

no code implementations EMNLP (BlackboxNLP) 2020 Lukas Muttenthaler, Isabelle Augenstein, Johannes Bjerva

We observe a consistent pattern in the answer representations, which we show can be used to automatically evaluate whether or not a predicted answer span is correct.

Question Answering

Human brain activity for machine attention

no code implementations9 Jun 2020 Lukas Muttenthaler, Nora Hollenstein, Maria Barrett

Cognitively inspired NLP leverages human-derived data to teach machines about language processing mechanisms.

Dimensionality Reduction EEG +1

Subjective Question Answering: Deciphering the inner workings of Transformers in the realm of subjectivity

1 code implementation2 Jun 2020 Lukas Muttenthaler

Hence, to answer these subjective questions, a learner must extract opinions and process sentiment for various domains, and additionally, align the knowledge extracted from a paragraph with the natural language utterances in the corresponding question, which together enhance the difficulty of a QA task.

Opinion Mining Question Answering

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