Search Results for author:
Found 19 papers, 7 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.
Memory and Bandwidth are All You Need for Fully Sharded Data Parallel
Transformer models have revolutionized a wide spectrum of disciplines, especially in language processing.
Scaling Image Tokenizers with Grouped Spherical Quantization
Vision tokenizers have gained a lot of attraction due to their scalability and compactness; previous works depend on old-school GAN-based hyperparameters, biased comparisons, and a lack of comprehensive analysis of the scaling behaviours.
Data Pruning in Generative Diffusion Models
Data pruning is the problem of identifying a core subset that is most beneficial to training and discarding the remainder.
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.
Time Transfer: On Optimal Learning Rate and Batch Size In The Infinite Data Limit
One of the main challenges in optimal scaling of large language models (LLMs) is the prohibitive cost of hyperparameter tuning, particularly learning rate $\eta$ and batch size $B$.
Predict and Interpret Health Risk using EHR through Typical Patients
Predicting health risks from electronic health records (EHR) is a topic of recent interest.
OpenNet: Incremental Learning for Autonomous Driving Object Detection with Balanced Loss
Automated driving object detection has always been a challenging task in computer vision due to environmental uncertainties.
Self-Supervised Learning for WiFi CSI-Based Human Activity Recognition: A Systematic Study
Therefore, considerable efforts have been made to address the challenge of insufficient data in deep learning by leveraging SSL algorithms.
TFDet: Target-Aware Fusion for RGB-T Pedestrian Detection
Different from them, we comprehensively analyze the impacts of false positives on the detection performance and find that enhancing feature contrast can significantly reduce these false positives.
M$^3$Care: Learning with Missing Modalities in Multimodal Healthcare Data
M3Care is an end-to-end model compensating the missing information of the patients with missing modalities to perform clinical analysis.
Secure Transmission for IRS-Assisted MIMO MmWave Systems
In this paper, we investigate the secure beamforming design in an intelligent reflection surface (IRS) assisted millimeter wave (mmWave) system, where the hybrid beamforming (HB) and the passive beamforming (PB) are employed by the transmitter and the IRS, respectively.
Designing Binary Sequence Set with Optimized Correlation Properties via ADMM Approach
In this paper, we design low correlation binary sequences favorable in wireless communication and radar applications.
Efficient QAM Signal Detector for Massive MIMO Systems via PS-ADMM Approach
In this paper, we design an efficient quadrature amplitude modulation (QAM) signal detector for massive multiple-input multiple-output (MIMO) communication systems via the penalty-sharing alternating direction method of multipliers (PS-ADMM).
Interpretable Machine Learning for COVID-19: An Empirical Study on Severity Prediction Task
The black-box nature of machine learning models hinders the deployment of some high-accuracy models in medical diagnosis.
CovidCare: Transferring Knowledge from Existing EMR to Emerging Epidemic for Interpretable Prognosis
Due to the characteristics of COVID-19, the epidemic develops rapidly and overwhelms health service systems worldwide.
AdaCare: Explainable Clinical Health Status Representation Learning via Scale-Adaptive Feature Extraction and Recalibration
It also models the correlation between clinical features to enhance the ones which strongly indicate the health status and thus can maintain a state-of-the-art performance in terms of prediction accuracy while providing qualitative interpretability.
Multi-Label Robust Factorization Autoencoder and its Application in Predicting Drug-Drug Interactions
However, the shallow models leveraging bilinear forms suffer from limitations on capturing complicated nonlinear interactions between drug pairs.
