50 citations
- Polish Academy of SciencesPL6 papers
- Sorbonne UniversitéFR6 papers
- Centre National de la Recherche ScientifiqueFR4 papers
- LIP6FR3 papers
- Systems Research InstitutePL3 papers
- Centre de Recherche en Mathématiques de la DécisionFR2 papers
- École Polytechnique Fédérale de LausanneCH2 papers
- Polish Academy of LearningPL2 papers
- Stanford UniversityUS2 papers
- CERMICSFR1 paper
- École nationale des ponts et chausséesFR1 paper
- École Normale Supérieure - PSLFR1 paper
15 papers
Energy Consumption Analysis of Discrete Diffusion and Autoregressive Language Models
Boris Ruf, Marcin Detyniecki
As Large Language Models (LLMs) continue to scale, their energy consumption during inference has become a critical concern. While Autoregressive (AR) models are the industry standa…
Energy Scaling Laws for Diffusion Models: Quantifying Compute in Image Generation
Aniketh Iyengar, Jiaqi Han, Boris Ruf +3
The rapidly growing computational demands of diffusion models for image generation have raised significant concerns about energy consumption and environmental impact. While existin…
Mitigating the Carbon Footprint of Chatbots as Consumers
Boris Ruf, Marcin Detyniecki
In the context of the high energy demand of large language models (LLMs) and growing concerns about global warming, there is significant demand for actionable recommendations that…
On the Fairness ROAD: Robust Optimization for Adversarial Debiasing
Vincent Grari, Thibault Laugel, Tatsunori Hashimoto +2
In the field of algorithmic fairness, significant attention has been put on group fairness criteria, such as Demographic Parity and Equalized Odds. Nevertheless, these objectives,…
Dynamic Interpretability for Model Comparison via Decision Rules
Adam Rida, Marie-Jeanne Lesot, Xavier Renard +1
Explainable AI (XAI) methods have mostly been built to investigate and shed light on single machine learning models and are not designed to capture and explain differences between…
Achieving Diversity in Counterfactual Explanations: a Review and Discussion
Thibault Laugel, Adulam Jeyasothy, Marie-Jeanne Lesot +2
In the field of Explainable Artificial Intelligence (XAI), counterfactual examples explain to a user the predictions of a trained decision model by indicating the modifications to…