Search Results for author:
Found 10 papers, 2 papers with code
Physical formula enhanced multi-task learning for pharmacokinetics prediction
Overall, our work illustrates the benefits and potential of using PEMAL in AIDD and other scenarios with data scarcity and noise.
ChemLLM: A Chemical Large Language Model
ChemLLM beats GPT-3. 5 on all three principal tasks in chemistry, i. e., name conversion, molecular caption, and reaction prediction, and surpasses GPT-4 on two of them.
Towards Simple and Accurate Human Pose Estimation with Stair Network
In this paper, we focus on tackling the precise keypoint coordinates regression task.
Perturbation Diversity Certificates Robust Generalisation
It is possibly due to the fact that the conventional adversarial training methods generate adversarial perturbations usually in a supervised way, so that the adversarial samples are highly biased towards the decision boundary, resulting in an inhomogeneous data distribution.
Improving Model Robustness with Latent Distribution Locally and Globally
The proposed adversarial training with latent distribution (ATLD) method defends against adversarial attacks by crafting LMAEs with the latent manifold in an unsupervised manner.
Gradient Distribution Alignment Certificates Better Adversarial Domain Adaptation
In particular, we show that the distribution discrepancy can be reduced by constraining feature gradients of two domains to have similar distributions.
Robust Generative Adversarial Network
Generative adversarial networks (GANs) are powerful generative models, but usually suffer from instability and generalization problem which may lead to poor generations.
On Model Robustness Against Adversarial Examples
We propose to exploit an energy function to describe the stability and prove that reducing such energy guarantees the robustness against adversarial examples.
LEARNING ADVERSARIAL EXAMPLES WITH RIEMANNIAN GEOMETRY
In this paper, we propose a generalized framework that addresses the learning problem of adversarial examples with Riemannian geometry.
Manifold Adversarial Learning
All the existing adversarial training methods consider only how the worst perturbed examples (i. e., adversarial examples) could affect the model output.
