Search Results for author:
Found 7 papers, 2 papers with code
MetaWeighting: Learning to Weight Tasks in Multi-Task Learning
Task weighting, which assigns weights on the including tasks during training, significantly matters the performance of Multi-task Learning (MTL); thus, recently, there has been an explosive interest in it.
Exploring Large Language Model based Intelligent Agents: Definitions, Methods, and Prospects
Intelligent agents stand out as a potential path toward artificial general intelligence (AGI).
Better Diffusion Models Further Improve Adversarial Training
Under the $\ell_\infty$-norm threat model with $\epsilon=8/255$, our models achieve $70. 69\%$ and $42. 67\%$ robust accuracy on CIFAR-10 and CIFAR-100, respectively, i. e. improving upon previous state-of-the-art models by $+4. 58\%$ and $+8. 03\%$.
Hierarchical Deep Learning with Generative Adversarial Network for Automatic Cardiac Diagnosis from ECG Signals
In this paper, we propose a two-level hierarchical deep learning framework with Generative Adversarial Network (GAN) for automatic diagnosis of ECG signals.
Deep learning for non-parameterized MEMS structural design
With reasonable training, our deep learning neural network becomes a high-speed, high-accuracy calculator: it can identify the flexural mode frequency and the quality factor 4. 6 × 10 times and 2. 6 × 10 times faster, respectively, than conventional numerical simulation packages, with good accuracies of 98. 8 ± 1. 6% and 96. 8 ± 3. 1%, respectively.
FakeTransformer: Exposing Face Forgery From Spatial-Temporal Representation Modeled By Facial Pixel Variations
With the rapid development of generation model, AI-based face manipulation technology, which called DeepFakes, has become more and more realistic.
BanditMTL: Bandit-based Multi-task Learning for Text Classification
Task variance regularization, which can be used to improve the generalization of Multi-task Learning (MTL) models, remains unexplored in multi-task text classification.
