74 citations · 101 across the 15 of their papers we have counts for
10 papers · 1 filter
Physics-Guided Inductive Spatiotemporal Kriging for PM2.5 with Satellite Gradient Constraints
Shuo Wang, Mengfan Teng, Yun Cheng +8
High-resolution mapping of fine particulate matter (PM2.5) is a cornerstone of sustainable urbanism but remains critically hindered by the spatial sparsity of ground monitoring net…
PCDCNet: A Surrogate Model for Air Quality Forecasting with Physical-Chemical Dynamics and Constraints
Shuo Wang, Yun Cheng, Qingye Meng +6
Air quality forecasting (AQF) is critical for public health and environmental management, yet remains challenging due to the complex interplay of emissions, meteorology, and chemic…
Forget the Data and Fine-Tuning! Just Fold the Network to Compress
Dong Wang, Haris Šikić, Lothar Thiele +1
We introduce model folding, a novel data-free model compression technique that merges structurally similar neurons across layers, significantly reducing the model size without the…
REDS: Resource-Efficient Deep Subnetworks for Dynamic Resource Constraints
Francesco Corti, Balz Maag, Joachim Schauer +2
Deep learning models deployed on edge devices frequently encounter resource variability, which arises from fluctuating energy levels, timing constraints, or prioritization of other…
Subspace-Configurable Networks
Dong Wang, Olga Saukh, Xiaoxi He +1
While the deployment of deep learning models on edge devices is increasing, these models often lack robustness when faced with dynamic changes in sensed data. This can be attribute…
REPAIR: REnormalizing Permuted Activations for Interpolation Repair
Keller Jordan, Hanie Sedghi, Olga Saukh +2
In this paper we look into the conjecture of Entezari et al. (2021) which states that if the permutation invariance of neural networks is taken into account, then there is likely n…
Studying the impact of magnitude pruning on contrastive learning methods
Francesco Corti, Rahim Entezari, Sara Hooker +2
We study the impact of different pruning techniques on the representation learned by deep neural networks trained with contrastive loss functions. Our work finds that at high spars…
The Role of Permutation Invariance in Linear Mode Connectivity of Neural Networks
Rahim Entezari, Hanie Sedghi, Olga Saukh +1
In this paper, we conjecture that if the permutation invariance of neural networks is taken into account, SGD solutions will likely have no barrier in the linear interpolation betw…
Interpretable and Transferable Models to Understand the Impact of Lockdown Measures on Local Air Quality
Johanna Einsiedler, Yun Cheng, Franz Papst +1
The COVID-19 related lockdown measures offer a unique opportunity to understand how changes in economic activity and traffic affect ambient air quality and how much pollution reduc…
Class-dependent Compression of Deep Neural Networks
Rahim Entezari, Olga Saukh
Today's deep neural networks require substantial computation resources for their training, storage, and inference, which limits their effective use on resource-constrained devices.…