75 citations · 318 across the 26 of their papers we have counts for
31 papers · 1 filter
GEANN: Scalable Graph Augmentations for Multi-Horizon Time Series Forecasting
Sitan Yang, Malcolm Wolff, Shankar Ramasubramanian +3
Encoder-decoder deep neural networks have been increasingly studied for multi-horizon time series forecasting, especially in real-world applications. However, to forecast accuratel…
CLOVER : Probabilistic Forecasting with Coherent Learning Objective Reparameterization
Kin G. Olivares, Geoffrey Négiar, Ruijun Ma +3
Obtaining accurate probabilistic forecasts is an operational challenge in many applications, such as energy management, climate forecasting, supply chain planning, and resource all…
End-to-end codesign of Hessian-aware quantized neural networks for FPGAs and ASICs
Javier Campos, Zhen Dong, Javier Duarte +4
We develop an end-to-end workflow for the training and implementation of co-designed neural networks (NNs) for efficient field-programmable gate array (FPGA) and application-specif…
Learning Physical Models that Can Respect Conservation Laws
Derek Hansen, Danielle C. Maddix, Shima Alizadeh +2
Recent work in scientific machine learning (SciML) has focused on incorporating partial differential equation (PDE) information into the learning process. Much of this work has foc…
Gated Recurrent Neural Networks with Weighted Time-Delay Feedback
N. Benjamin Erichson, Soon Hoe Lim, Michael W. Mahoney
In this paper, we present a novel approach to modeling long-term dependencies in sequential data by introducing a gated recurrent unit (GRU) with a weighted time-delay feedback mec…
Gradient Gating for Deep Multi-Rate Learning on Graphs
T. Konstantin Rusch, Benjamin P. Chamberlain, Michael W. Mahoney +2
We present Gradient Gating (G), a novel framework for improving the performance of Graph Neural Networks (GNNs). Our framework is based on gating the output of GNN layers with…