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Rich Rodriguez Gives WVU Offensive Line An Honest Review After Week 1 Win

The West Virginia Mountaineers got off to a slow start in Morgantown against Robert Morris last week. In the first half, WVU looked sloppy, but as they got comfortable, the Mountaineers started to ...
IEEE

Improved 3-D LSTM: A Video Prediction Approach to Long Sequence Load Forecasting

Abstract: Power load forecasting is the foundation of maintaining power grid stability, and can assist in decision-making to reduce operating costs. Fine-grained long sequence load forecasting ...
Frontiers

A hybrid VMD-LSTM-SVR model for landslide prediction

Landslides are one of the most prevalent natural geological disasters, causing significant economic losses, damaging public environments, and posing severe threats to human lives. Landslide ...
Scientific Research Publishing

Hybrid PINN-LSTM Model for River Temperature Prediction: A Physics-Informed Deep Learning Approach ()

This study proposes a hybrid modeling approach that integrates a Physics Informed Neural Network (PINN) and a long short-term memory (LSTM) network to predict river water temperature in a defined ...
InfoWorld

Running PyTorch on an Arm Copilot+ PC

When Microsoft launched its Copilot+ PC range almost a year ago, it announced that it would deliver the Copilot Runtime, a set of tools to help developers take advantage of the devices’ built-in AI ...
GitHub

BUG: SHAP explanations do not sum up to the model's output in PyTorch LSTM model

I built an LSTM time series prediction model using PyTorch and utilized SHAP to assess the impact of different features on the model's forecasting results. I ...
IEEE

AI-Driven Energy Optimization: Household Power Consumption Prediction With LSTM Networks and PyTorch-Ray Tune in Smart IoT Systems

Abstract: Energy optimization is critical in smart home systems and IoT networks, necessitating innovative models that reduce energy use. This research presents two Long Short-Term Memory (LSTM) ...
GitHub

Convert LSTM quantized by QAT to onnx

torch.onnx.errors.SymbolicValueError: ONNX symbolic expected the output of `%2212 : Tensor = onnx::Squeeze(%2186, %2211), scope: SimpleLSTMNet::/torch.ao.nn.quantized ...