Profile

Stephan Alaniz is a post-doctoral researcher in the Explainable Machine Learning group, led by Prof. Zeynep Akata. His Ph.D was partially done at the University of Amsterdam, the Max Planck Institute for Informatics and the University of Tübingen, supervised by Zeynep Akata and Bernt Schiele. His research focuses on explainable AI and multi-modal learning with vision and language.

Publications

In-Context Impersonation Reveals Large Language Models' Strengths and Biases
Leonard Salewski, Stephan Alaniz, Isabel Rio-Torto, Eric Schulz and Zeynep Akata
NeurIPS 2023 (spotlight)

Iterative Superquadric Recomposition of 3D Objects from Multiple Views
Stephan Alaniz, Massimiliano Mancini, and Zeynep Akata
ICCV 2023

PDiscoNet: Semantically consistent part discovery for fine-grained recognition
Robert van der Klis, Stephan Alaniz, Massimiliano Mancini, Cassio Dantas, Dino Ienco, Zeynep Akata, Diego Marcos
ICCV 2023

DeViL: Decoding Vision features into Language
Meghal Dani*, Isabel Rio-Torto*, Stephan Alaniz, and Zeynep Akata
GCPR 2023 (oral)

Abstracting Sketches through Simple Primitives
Stephan Alaniz, Massimiliano Mancini, Anjan Dutta, Diego Marcos, Zeynep Akata
ECCV 2022

Compositional Mixture Representations for Vision and Text
Stephan Alaniz, Marco Federici, Zeynep Akata
CVPR-W 2022 (L3D-IVU)

Semantic Image Synthesis with Semantically Coupled VQ-Model
Stephan Alaniz*, Thomas Hummel*, Zeynep Akata
ICLR-W 2022 (DGM4HSD)

Learning Decision Trees Recurrently Through Communication
Stephan Alaniz, Diego Marcos, Bernt Schiele, Zeynep Akata
CVPR 2021

Modeling Conceptual Understanding in Image Reference Games
Rodolfo Corona*, Stephan Alaniz*, Zeynep Akata
NeurIPS 2019

Deep Reinforcement Learning with Model Learning and Monte Carlo Tree Search in Minecraft
Stephan Alaniz
RLDM 2017

* denotes equal contribution