Popular deep learning algorithms for disease prediction: a review
Z Yu, K Wang, Z Wan, S Xie, Z Lv - Cluster Computing, 2023 - Springer
Due to its automatic feature learning ability and high performance, deep learning has
gradually become the mainstream of artificial intelligence in recent years, playing a role in …
gradually become the mainstream of artificial intelligence in recent years, playing a role in …
BaGFN: broad attentive graph fusion network for high-order feature interactions
Modeling feature interactions is of crucial significance to high-quality feature engineering on
multifiled sparse data. At present, a series of state-of-the-art methods extract cross features …
multifiled sparse data. At present, a series of state-of-the-art methods extract cross features …
Bridging the theoretical bound and deep algorithms for open set domain adaptation
In the unsupervised open set domain adaptation (UOSDA), the target domain contains
unknown classes that are not observed in the source domain. Researchers in this area aim …
unknown classes that are not observed in the source domain. Researchers in this area aim …
Cl4ctr: A contrastive learning framework for ctr prediction
Many Click-Through Rate (CTR) prediction works focused on designing advanced
architectures to model complex feature interactions but neglected the importance of feature …
architectures to model complex feature interactions but neglected the importance of feature …
Enhancing CTR prediction with context-aware feature representation learning
CTR prediction has been widely used in the real world. Many methods model feature
interaction to improve their performance. However, most methods only learn a fixed …
interaction to improve their performance. However, most methods only learn a fixed …
FedCTR: Federated native ad CTR prediction with cross-platform user behavior data
Native ad is a popular type of online advertisement that has similar forms with the native
content displayed on websites. Native ad click-through rate (CTR) prediction is useful for …
content displayed on websites. Native ad click-through rate (CTR) prediction is useful for …
Learning fine-grained user interests for micro-video recommendation
Recent years have witnessed the rapid development of online micro-video platforms, in
which the recommender system plays an essential role in overcoming the information …
which the recommender system plays an essential role in overcoming the information …
Apg: Adaptive parameter generation network for click-through rate prediction
B Yan, P Wang, K Zhang, F Li… - Advances in Neural …, 2022 - proceedings.neurips.cc
In many web applications, deep learning-based CTR prediction models (deep CTR models
for short) are widely adopted. Traditional deep CTR models learn patterns in a static …
for short) are widely adopted. Traditional deep CTR models learn patterns in a static …
How does the combined risk affect the performance of unsupervised domain adaptation approaches?
Unsupervised domain adaptation (UDA) aims to train a target classifier with labeled samples
from the source domain and unlabeled samples from the target domain. Classical UDA …
from the source domain and unlabeled samples from the target domain. Classical UDA …
Towards deeper, lighter and interpretable cross network for ctr prediction
Click Through Rate (CTR) prediction plays an essential role in recommender systems and
online advertising. It is crucial to effectively model feature interactions to improve the …
online advertising. It is crucial to effectively model feature interactions to improve the …