4 citations · 11 across the 5 of their papers we have counts for
5 papers
An Attention-based Model for Robust Forecasting with Missing Modality
Zhitian Zhang, Wenjie Zi, Yunduz Rakhmangulova +3
Learning with missing modalities is a fundamental challenge in multimodal robot learning, as real-world robotic systems often operate in environments with incomplete sensor data. A…
Accurate Parameter-Efficient Test-Time Adaptation for Time Series Forecasting
Heitor R. Medeiros, Hossein Sharifi-Noghabi, Gabriel L. Oliveira +1
Real-world time series often exhibit a non-stationary nature, degrading the performance of pre-trained forecasting models. Test-Time Adaptation (TTA) addresses this by adjusting mo…
Training a Vision Transformer from scratch in less than 24 hours with 1 GPU
Saghar Irandoust, Thibaut Durand, Yunduz Rakhmangulova +2
Transformers have become central to recent advances in computer vision. However, training a vision Transformer (ViT) model from scratch can be resource intensive and time consuming…
PuzzleFusion: Unleashing the Power of Diffusion Models for Spatial Puzzle Solving
Sepidehsadat Hosseini, Mohammad Amin Shabani, Saghar Irandoust +1
This paper presents an end-to-end neural architecture based on Diffusion Models for spatial puzzle solving, particularly jigsaw puzzle and room arrangement tasks. In the latter tas…
Fusion-DHL: WiFi, IMU, and Floorplan Fusion for Dense History of Locations in Indoor Environments
Sachini Herath, Saghar Irandoust, Bowen Chen +3
The paper proposes a multi-modal sensor fusion algorithm that fuses WiFi, IMU, and floorplan information to infer an accurate and dense location history in indoor environments. The…