Search Results for author:
Found 10 papers, 3 papers with code
Unimodular Waveform Design for Integrated Sensing and Communication MIMO System via Manifold Optimization
In this paper, we focus on the waveform design of ISAC system, which can realize radar sensing while also facilitate information transmission.
Scaling Capability in Token Space: An Analysis of Large Vision Language Model
One important question is that does the scaling capability also exists similarly with respect to the number of vision tokens in large vision language Model?
Designing Unimodular Waveforms for MIMO Radar Based on Manifold Optimization Method
By embedding it into the search space, we transform the original non-convex optimization problem into an unconstrained problem on a Riemannian manifold.
A Statistical Theory of Regularization-Based Continual Learning
Moreover, the estimation error of the optimal algorithm is derived explicitly, which is of the same order as that of the oracle estimator.
EpilepsyLLM: Domain-Specific Large Language Model Fine-tuned with Epilepsy Medical Knowledge
Based on those powerful LLMs, the model fine-tuned with domain-specific datasets posseses more specialized knowledge and thus is more practical like medical LLMs.
ArCL: Enhancing Contrastive Learning with Augmentation-Robust Representations
Moreover, we show that contrastive learning fails to learn domain-invariant features, which limits its transferability.
When Noisy Labels Meet Long Tail Dilemmas: A Representation Calibration Method
In this paper, to handle the problem and address the limitations of prior works, we propose a representation calibration method RCAL.
Heterogeneous Federated Learning on a Graph
In this work, we consider parameter estimation in federated learning with data distribution and communication heterogeneity, as well as limited computational capacity of local devices.
Towards the Generalization of Contrastive Self-Supervised Learning
It reveals that the generalization ability of contrastive self-supervised learning is related to three key factors: alignment of positive samples, divergence of class centers, and concentration of augmented data.
Tensorizing Generative Adversarial Nets
Generative Adversarial Network (GAN) and its variants exhibit state-of-the-art performance in the class of generative models.
