8 citations · 16 across the 14 of their papers we have counts for
14 papers
HACMatch Semi-Supervised Rotation Regression with Hardness-Aware Curriculum Pseudo Labeling
Mei Li, Huayi Zhou, Suizhi Huang +3
Regressing 3D rotations of objects from 2D images is a crucial yet challenging task, with broad applications in autonomous driving, virtual reality, and robotic control. Existing r…
TextResNet: Decoupling and Routing Optimization Signals in Compound AI Systems via Deep Residual Tuning
Suizhi Huang, Mei Li, Han Yu +1
Textual Gradient-style optimizers (TextGrad) enable gradient-like feedback propagation through compound AI systems. However, they do not work well for deep chains. The root cause o…
MORE: Multi-Organ Medical Image REconstruction Dataset
Shaokai Wu, Yapan Guo, Yanbiao Ji +6
CT reconstruction provides radiologists with images for diagnosis and treatment, yet current deep learning methods are typically limited to specific anatomies and datasets, hinderi…
A Comprehensive Survey on Benchmarks and Solutions in Software Engineering of LLM-Empowered Agentic System
Jiale Guo, Suizhi Huang, Mei Li +8
The integration of Large Language Models (LLMs) into software engineering has driven a transition from traditional rule-based systems to autonomous agentic systems capable of solvi…
BECAME: BayEsian Continual Learning with Adaptive Model MErging
Mei Li, Yuxiang Lu, Qinyan Dai +3
Continual Learning (CL) strives to learn incrementally across tasks while mitigating catastrophic forgetting. A key challenge in CL is balancing stability (retaining prior knowledg…
Few-shot Implicit Function Generation via Equivariance
Suizhi Huang, Xingyi Yang, Hongtao Lu +1
Implicit Neural Representations (INRs) have emerged as a powerful framework for representing continuous signals. However, generating diverse INR weights remains challenging due to…
Discretized Gaussian Representation for Tomographic Reconstruction
Shaokai Wu, Yuxiang Lu, Yapan Guo +9
Computed Tomography (CT) enables detailed cross-sectional imaging but continues to face challenges in balancing reconstruction quality and computational efficiency. While deep lear…
Towards Personalized Federated Multi-Scenario Multi-Task Recommendation
Yue Ding, Yanbiao Ji, Xun Cai +7
In modern recommender systems, especially in e-commerce, predicting multiple targets such as click-through rate (CTR) and post-view conversion rate (CTCVR) is common. Multi-task re…
Task Indicating Transformer for Task-conditional Dense Predictions
Yuxiang Lu, Shalayiding Sirejiding, Bayram Bayramli +3
The task-conditional model is a distinctive stream for efficient multi-task learning. Existing works encounter a critical limitation in learning task-agnostic and task-specific rep…
YOLO-MED : Multi-Task Interaction Network for Biomedical Images
Suizhi Huang, Shalayiding Sirejiding, Yuxiang Lu +4
Object detection and semantic segmentation are pivotal components in biomedical image analysis. Current single-task networks exhibit promising outcomes in both detection and segmen…
Federated Multi-Task Learning on Non-IID Data Silos: An Experimental Study
Yuwen Yang, Yuxiang Lu, Suizhi Huang +3
The innovative Federated Multi-Task Learning (FMTL) approach consolidates the benefits of Federated Learning (FL) and Multi-Task Learning (MTL), enabling collaborative model traini…
FedHCA: Towards Hetero-Client Federated Multi-Task Learning
Yuxiang Lu, Suizhi Huang, Yuwen Yang +3
Federated Learning (FL) enables joint training across distributed clients using their local data privately. Federated Multi-Task Learning (FMTL) builds on FL to handle multiple tas…
UNIDEAL: Curriculum Knowledge Distillation Federated Learning
Yuwen Yang, Chang Liu, Xun Cai +3
Federated Learning (FL) has emerged as a promising approach to enable collaborative learning among multiple clients while preserving data privacy. However, cross-domain FL tasks, w…
Optimization of convolutional neural networks for background suppression in the PandaX-III experiment
Shangning Xia, Suizhi Huang, Kexin Xu +4
The tracks recorded by a gaseous detector provide a possibility for charged particle identification. For searching the neutrinoless double beta decay events of 136Xe in the PandaX-…