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Found 17 papers, 3 papers with code
Bayesian Beamforming for Integrated Sensing and Communication Systems
The uncertainty of the sensing target brings great challenge to the beamforming design of the integrated sensing and communication (ISAC) system.
Joint Beamforming for Multi-target Detection and Multi-user Communication in ISAC Systems
Sensing and communication suffer from a performance trade-off in ISAC systems.
Joint Beamforming for Backscatter Integrated Sensing and Communication
Integrated sensing and communication (ISAC) is a key technology of next generation wireless communication.
Misaligned Over-The-Air Computation of Multi-Sensor Data with Wiener-Denoiser Network
In data driven deep learning, distributed sensing and joint computing bring heavy load for computing and communication.
B-ISAC: Backscatter Integrated Sensing and Communication for IoE Applications
In this paper, we propose a novel cognitive wireless system called backscatter-ISAC (B-ISAC) and develop a joint beamforming framework for different stages (task modes).
A Survey on Efficient Inference for Large Language Models
This paper presents a comprehensive survey of the existing literature on efficient LLM inference.
FlashDecoding++: Faster Large Language Model Inference on GPUs
A single and static dataflow may lead to a 50. 25% performance loss for GEMMs of different shapes in LLM inference.
Contrastive Learning for Low-light Raw Denoising
Inspired by the success of contrastive learning used in some high-level computer vision tasks, we bring in this idea to the low-level denoising task.
Bokeh Rendering Based on Adaptive Depth Calibration Network
This allows the camera to capture images with shallow depth-of-field, in which only a small area of the image is in sharp focus, while the rest of the image is blurred.
Federated attention consistent learning models for prostate cancer diagnosis and Gleason grading
We assessed the effectiveness of FACL in cancer diagnosis and Gleason grading tasks using 19, 461 whole-slide images of prostate cancer from multiple centers.
TAOTF: A Two-stage Approximately Orthogonal Training Framework in Deep Neural Networks
In the first stage, we propose a novel algorithm called polar decomposition-based orthogonal initialization (PDOI) to find a good initialization for the orthogonal optimization.
CLOSE: Curriculum Learning On the Sharing Extent Towards Better One-shot NAS
Specifically, we train the supernet with a large sharing extent (an easier curriculum) at the beginning and gradually decrease the sharing extent of the supernet (a harder curriculum).
A Fair Federated Learning Framework With Reinforcement Learning
More importantly, we apply the Gini coefficient and validation accuracy of clients in each communication round to construct a reward function for the reinforcement learning.
Mixed-UNet: Refined Class Activation Mapping for Weakly-Supervised Semantic Segmentation with Multi-scale Inference
Deep learning techniques have shown great potential in medical image processing, particularly through accurate and reliable image segmentation on magnetic resonance imaging (MRI) scans or computed tomography (CT) scans, which allow the localization and diagnosis of lesions.
RCMNet: A deep learning model assists CAR-T therapy for leukemia
Although testing on the CAR-T cells dataset, a decent performance is observed, which is attributed to the limited size of the dataset.
Point Cloud Color Constancy
In this paper, we present Point Cloud Color Constancy, in short PCCC, an illumination chromaticity estimation algorithm exploiting a point cloud.
Driving maneuvers prediction based on cognition-driven and data-driven method
To overcome this challenge, we propose a novel method that combines both the cognition-driven model and the data-driven model.
