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Interactive Disentanglement: Learning Concepts by Interacting with their Prototype Representations
Wolfgang Stammer, Marius Memmel, Patrick Schramowski +1
Learning visual concepts from raw images without strong supervision is a challenging task. In this work, we show the advantages of prototype representations for understanding and r…
Inferring Offensiveness In Images From Natural Language Supervision
Patrick Schramowski, Kristian Kersting
Probing or fine-tuning (large-scale) pre-trained models results in state-of-the-art performance for many NLP tasks and, more recently, even for computer vision tasks when combined…
Interactively Providing Explanations for Transformer Language Models
Felix Friedrich, Patrick Schramowski, Christopher Tauchmann +1
Transformer language models are state of the art in a multitude of NLP tasks. Despite these successes, their opaqueness remains problematic. Recent methods aiming to provide interp…
Large Pre-trained Language Models Contain Human-like Biases of What is Right and Wrong to Do
Patrick Schramowski, Cigdem Turan, Nico Andersen +2
Artificial writing is permeating our lives due to recent advances in large-scale, transformer-based language models (LMs) such as BERT, its variants, GPT-2/3, and others. Using the…
Adaptive Rational Activations to Boost Deep Reinforcement Learning
Quentin Delfosse, Patrick Schramowski, Martin Mundt +2
Latest insights from biology show that intelligence not only emerges from the connections between neurons but that individual neurons shoulder more computational responsibility tha…