no code implementations • Findings (EMNLP) 2021 • Denis Lukovnikov, Sina Daubener, Asja Fischer
While neural networks are ubiquitous in state-of-the-art semantic parsers, it has been shown that most standard models suffer from dramatic performance losses when faced with compositionally out-of-distribution (OOD) data.
no code implementations • 20 Feb 2024 • Denis Lukovnikov, Asja Fischer
While text-to-image diffusion models can generate highquality images from textual descriptions, they generally lack fine-grained control over the visual composition of the generated images.
1 code implementation • 31 Jan 2024 • Jonas Ricker, Denis Lukovnikov, Asja Fischer
A key enabler for generating high-resolution images with low computational cost has been the development of latent diffusion models (LDMs).
no code implementations • 22 Jun 2023 • Mike Laszkiewicz, Denis Lukovnikov, Johannes Lederer, Asja Fischer
In this work, we propose a set-membership inference attack for generative models using deep image watermarking techniques.
no code implementations • 1 Jan 2021 • Denis Lukovnikov, Asja Fischer
Relational Graph Neural Networks (GNN) are a class of GNN that are capable of handling multi-relational graphs.
no code implementations • 19 Jul 2020 • Denis Lukovnikov, Jens Lehmann, Asja Fischer
Many popular variants of graph neural networks (GNNs) that are capable of handling multi-relational graphs may suffer from vanishing gradients.
no code implementations • 22 Jul 2019 • Nilesh Chakraborty, Denis Lukovnikov, Gaurav Maheshwari, Priyansh Trivedi, Jens Lehmann, Asja Fischer
Question answering has emerged as an intuitive way of querying structured data sources, and has attracted significant advancements over the years.
no code implementations • 13 Nov 2018 • Denis Lukovnikov, Nilesh Chakraborty, Jens Lehmann, Asja Fischer
Translating natural language to SQL queries for table-based question answering is a challenging problem and has received significant attention from the research community.
1 code implementation • 2 Nov 2018 • Gaurav Maheshwari, Priyansh Trivedi, Denis Lukovnikov, Nilesh Chakraborty, Asja Fischer, Jens Lehmann
In this paper, we conduct an empirical investigation of neural query graph ranking approaches for the task of complex question answering over knowledge graphs.
1 code implementation • 3 Feb 2018 • Agustinus Kristiadi, Mohammad Asif Khan, Denis Lukovnikov, Jens Lehmann, Asja Fischer
Most of the existing work on embedding (or latent feature) based knowledge graph analysis focuses mainly on the relations between entities.