Contrastive learning has emerged as a cornerstone in recent achievements...
Model-agnostic meta-learning (MAML) is one of the most successful
meta-l...
Since the introduction of deep learning, a wide scope of representation
...
In autonomous driving, data augmentation is commonly used for improving ...
The performance of cardiac arrhythmia detection with electrocardiograms(...
The recent advancement in language representation modeling has broadly
a...
For many decades, BM25 and its variants have been the dominant document
...
The early pioneering Neural Architecture Search (NAS) works were multi-t...
Compression has emerged as one of the essential deep learning research
t...
A BERT-based Neural Ranking Model (NRM) can be either a cross-encoder or...
We propose an AID-purifier that can boost the robustness of
adversariall...
BERT-based Neural Ranking Models (NRMs) can be classified according to h...
In modern transportation systems, an enormous amount of traffic data is
...
Recently, applying deep neural networks in IR has become an important an...
Graph convolutional network is a generalization of convolutional network...
The performance of deep neural networks (DNN) is very sensitive to the
p...
Non-intrusive load monitoring (NILM), also known as energy disaggregatio...
It has been common to argue or imply that a regularizer can be used to a...
Statistical characteristics of DNN (Deep Neural Network) representations...
Because CNN models are compute-intensive, where billions of operations c...
The design complexity of CNNs has been steadily increasing to improve
ac...