10 papers
A Latent ODE Approach to Spatiotemporal Modeling of Cine Cardiac MRI
David Brüggemann, Ekaterina Krymova, Firat Özdemir +6
Cardiac magnetic resonance imaging (CMR) captures rich spatiotemporal information about ventricular structure and motion, but conventional risk models use only a few image-derived…
PriFT: Prior-Support Guided Supervised Fine-Tuning
Ke Wang, Shuangqi Li, Mathieu Salzmann +1
Supervised fine-tuning (SFT) is an efficient approach for downstream task adaptation and often serves as the initialization stage for reinforcement learning (RL), but it can show w…
OffQ: Taming Structured Outliers in LLM Quantization by Offsetting
Haoqi Wang, Lorenz K. Mueller, Jiawei Zhuang +2
Low-bit quantization has been widely adopted to accelerate the inference of large language models (LLMs) by significantly reducing computational cost and memory usage. However, act…
Earth System Foundation Model (ESFM): A unified framework for heterogeneous data integration and forecasting
Firat Ozdemir, Yun Cheng, Salman Mohebi +11
Foundation models (FMs) for the Earth system learn statistical relationships between physical variables across massive datasets to enable versatile downstream applications through…
Calibration-Reasoning Framework for Descriptive Speech Quality Assessment
Elizaveta Kostenok, Mathieu Salzmann, Milos Cernak
Explainable speech quality assessment requires moving beyond Mean Opinion Scores (MOS) to analyze underlying perceptual dimensions. To address this, we introduce a novel post-train…
Towards Self-Supervised Covariance Estimation in Deep Heteroscedastic Regression
Megh Shukla, Aziz Shameem, Mathieu Salzmann +1
Deep heteroscedastic regression models the mean and covariance of the target distribution through neural networks. The challenge arises from heteroscedasticity, which implies that…
GEM: A Generalizable Ego-Vision Multimodal World Model for Fine-Grained Ego-Motion, Object Dynamics, and Scene Composition Control
Mariam Hassan, Sebastian Stapf, Ahmad Rahimi +17
We present GEM, a Generalizable Ego-vision Multimodal world model that predicts future frames using a reference frame, sparse features, human poses, and ego-trajectories. Hence, ou…
MotionMap: Representing Multimodality in Human Pose Forecasting
Reyhaneh Hosseininejad, Megh Shukla, Saeed Saadatnejad +2
Human pose forecasting is inherently multimodal since multiple futures exist for an observed pose sequence. However, evaluating multimodality is challenging since the task is ill-p…
Source-Free Domain-Invariant Performance Prediction
Ekaterina Khramtsova, Mahsa Baktashmotlagh, Guido Zuccon +2
Accurately estimating model performance poses a significant challenge, particularly in scenarios where the source and target domains follow different data distributions. Most exist…
TIC-TAC: A Framework for Improved Covariance Estimation in Deep Heteroscedastic Regression
Megh Shukla, Mathieu Salzmann, Alexandre Alahi
Deep heteroscedastic regression involves jointly optimizing the mean and covariance of the predicted distribution using the negative log-likelihood. However, recent works show that…