Search Results for author:
Found 8 papers, 4 papers with code
A Meta-Learning Based Gradient Descent Algorithm for MU-MIMO Beamforming
Multi-user multiple-input multiple-output (MU-MIMO) beamforming design is typically formulated as a non-convex weighted sum rate (WSR) maximization problem that is known to be NP-hard.
ProCo: Prototype-aware Contrastive Learning for Long-tailed Medical Image Classification
The overall framework, namely as Prototype-aware Contrastive learning (ProCo), is unified as a single-stage pipeline in an end-to-end manner to alleviate the imbalanced problem in medical image classification, which is also a distinct progress than existing works as they follow the traditional two-stage pipeline.
A Learning Aided Flexible Gradient Descent Approach to MISO Beamforming
This paper proposes a learning aided gradient descent (LAGD) algorithm to solve the weighted sum rate (WSR) maximization problem for multiple-input single-output (MISO) beamforming.
DURRNet: Deep Unfolded Single Image Reflection Removal Network
With the deep unrolling technique, we build the DURRNet with ProxNets to model natural image priors and ProxInvNets which are constructed with invertible networks to impose the exclusion prior.
GIPA: General Information Propagation Algorithm for Graph Learning
Graph neural networks (GNNs) have been popularly used in analyzing graph-structured data, showing promising results in various applications such as node classification, link prediction and network recommendation.
Ranked #1 on
Node Property Prediction
on ogbn-proteins
Adversary-resilient Distributed and Decentralized Statistical Inference and Machine Learning: An Overview of Recent Advances Under the Byzantine Threat Model
While the last few decades have witnessed a huge body of work devoted to inference and learning in distributed and decentralized setups, much of this work assumes a non-adversarial setting in which individual nodes---apart from occasional statistical failures---operate as intended within the algorithmic framework.
BRIDGE: Byzantine-resilient Decentralized Gradient Descent
The focus of this paper is on robustification of decentralized learning in the presence of nodes that have undergone Byzantine failures.
ByRDiE: Byzantine-resilient distributed coordinate descent for decentralized learning
Distributed machine learning algorithms enable learning of models from datasets that are distributed over a network without gathering the data at a centralized location.
