This paper proposes a distributed version of Determinant Point Processin...
Knowledge distillation is a powerful technique to compress large neural
...
In some practical learning tasks, such as traffic video analysis, the nu...
Time series probabilistic forecasting with multi-dimensional and sporadi...
Answering factual questions with temporal intent over knowledge graphs
(...
Objective: Electrocardiogram (ECG) signals commonly suffer noise
interfe...
In this paper, we provide a deep dive into the deployment of inference
a...
Deep learning recommendation models (DLRMs) are used across many
busines...
Convolutional layers in Artificial Neural Networks (ANN) treat the chann...
Digital in-line holography is commonly used to reconstruct 3D images fro...
Attention mechanism is a hot spot in deep learning field. Using channel
...
Artificial Neural Networks (ANNs) are computational models inspired by t...
Neural network based sequence-to-sequence models in an encoder-decoder
f...