Search Results for author:
Found 9 papers, 1 papers with code
Superpixel Boundary Correction for Weakly-Supervised Semantic Segmentation on Histopathology Images
With the rapid advancement of deep learning, computational pathology has made significant progress in cancer diagnosis and subtyping.
CERD: A Comprehensive Chinese Rhetoric Dataset for Rhetorical Understanding and Generation in Essays
Existing rhetorical understanding and generation datasets or corpora primarily focus on single coarse-grained categories or fine-grained categories, neglecting the common interrelations between different rhetorical devices by treating them as independent sub-tasks.
Beam Profiling and Beamforming Modeling for mmWave NextG Networks
The datasets we have obtained from the beam profiling and the machine learning model for beamforming are valuable for a broad set of network design problems, such as network topology optimization, user equipment association, power allocation, and beam scheduling, in complex and dynamic mmWave networks.
Comet: A Communication-efficient and Performant Approximation for Private Transformer Inference
The prevalent use of Transformer-like models, exemplified by ChatGPT in modern language processing applications, underscores the critical need for enabling private inference essential for many cloud-based services reliant on such models.
Prognostic Significance of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images in Colorectal Cancers
Methods We developed an automated, multiscale LinkNet workflow for quantifying cellular-level TILs for CRC tumors using H&E-stained images.
Energy Minimization for Federated Asynchronous Learning on Battery-Powered Mobile Devices via Application Co-running
Energy is an essential, but often forgotten aspect in large-scale federated systems.
CLEAR: Clean-Up Sample-Targeted Backdoor in Neural Networks
Based on this observation, we propose a novel defense mechanism to pinpoint a malicious pocket by "wrapping" them into a tight convex hull in the feature space.
CHEETAH: An Ultra-Fast, Approximation-Free, and Privacy-Preserved Neural Network Framework based on Joint Obscure Linear and Nonlinear Computations
This significant speedup enables a wide range of practical applications based on privacy-preserved deep neural networks.
