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Found 38 papers, 17 papers with code
Exploring Word Segmentation and Medical Concept Recognition for Chinese Medical Texts
Chinese word segmentation (CWS) and medical concept recognition are two fundamental tasks to process Chinese electronic medical records (EMRs) and play important roles in downstream tasks for understanding Chinese EMRs.
A Label-Aware Autoregressive Framework for Cross-Domain NER
In doing so, label information contained in the embedding vectors can be effectively transferred to the target domain, and Bi-LSTM can further model the label relationship among different domains by pre-train and then fine-tune setting.
Cross-Domain Named Entity Recognition
named-entity-recognition
+2
Learn from Relation Information: Towards Prototype Representation Rectification for Few-Shot Relation Extraction
Few-shot Relation Extraction refers to fast adaptation to novel relation classes with few samples through training on the known relation classes.
Accelerating Parallel Sampling of Diffusion Models
Our experiments demonstrate that ParaTAA can decrease the inference steps required by common sequential sampling algorithms such as DDIM and DDPM by a factor of 4~14 times.
FedLion: Faster Adaptive Federated Optimization with Fewer Communication
In Federated Learning (FL), a framework to train machine learning models across distributed data, well-known algorithms like FedAvg tend to have slow convergence rates, resulting in high communication costs during training.
Decentralized Equalization for Massive MIMO Systems With Colored Noise Samples
However, current detection methods tailored to DBP only consider ideal white Gaussian noise scenarios, while in practice, the noise is often colored due to interference from neighboring cells.
Zeroth-Order Optimization Meets Human Feedback: Provable Learning via Ranking Oracles
In this study, we delve into an emerging optimization challenge involving a black-box objective function that can only be gauged via a ranking oracle-a situation frequently encountered in real-world scenarios, especially when the function is evaluated by human judges.
A Physics-based and Data-driven Approach for Localized Statistical Channel Modeling
Localized channel modeling is crucial for offline performance optimization of 5G cellular networks, but the existing channel models are for general scenarios and do not capture local geographical structures.
$z$-SignFedAvg: A Unified Stochastic Sign-based Compression for Federated Learning
In this paper, we propose a novel noisy perturbation scheme with a general symmetric noise distribution for sign-based compression, which not only allows one to flexibly control the tradeoff between gradient bias and convergence performance, but also provides a unified viewpoint to existing stochastic sign-based methods.
Why Batch Normalization Damage Federated Learning on Non-IID Data?
In view of this, we develop a new FL algorithm that is tailored to BN, called FedTAN, which is capable of achieving robust FL performance under a variety of data distributions via iterative layer-wise parameter aggregation.
Beyond ADMM: A Unified Client-variance-reduced Adaptive Federated Learning Framework
In this paper, we firstly reveal the fact that the federated ADMM is essentially a client-variance-reduced algorithm.
Large-Scale Bandwidth and Power Optimization for Multi-Modal Edge Intelligence Autonomous Driving
However, wireless channels between the edge server and the autonomous vehicles are time-varying due to the high-mobility of vehicles.
Improving Radiology Summarization with Radiograph and Anatomy Prompts
The impression is crucial for the referring physicians to grasp key information since it is concluded from the findings and reasoning of radiologists.
Multi-Modal Masked Autoencoders for Medical Vision-and-Language Pre-Training
Besides, we conduct further analysis to better verify the effectiveness of different components of our approach and various settings of pre-training.
Rethinking Bayesian Learning for Data Analysis: The Art of Prior and Inference in Sparsity-Aware Modeling
However, a come back of Bayesian methods is taking place that sheds new light on the design of deep neural networks, which also establish firm links with Bayesian models and inspire new paths for unsupervised learning, such as Bayesian tensor decomposition.
A Simple yet Effective Relation Information Guided Approach for Few-Shot Relation Extraction
We argue that relation information can be introduced more explicitly and effectively into the model.
Hero-Gang Neural Model For Named Entity Recognition
Named entity recognition (NER) is a fundamental and important task in NLP, aiming at identifying named entities (NEs) from free text.
Ranked #1 on
Named Entity Recognition (NER)
on WNUT 2016
Federated Stochastic Primal-dual Learning with Differential Privacy
Specifically, we show that, by guaranteeing $(\epsilon, \delta)$-DP for each client per communication round, the proposed algorithm guarantees $(\mathcal{O}(q\epsilon \sqrt{p T}), \delta)$-DP after $T$ communication rounds while maintaining an $\mathcal{O}(1/\sqrt{pTQ})$ convergence rate for a convex and non-smooth learning problem, where $Q$ is the number of local SGD steps, $p$ is the client sampling probability, $q=\max_{i} q_i/\sqrt{1-q_i}$ and $q_i$ is the data sampling probability of each client under PCP.
Graph Enhanced Contrastive Learning for Radiology Findings Summarization
To address the limitation, we propose a unified framework for exploiting both extra knowledge and the original findings in an integrated way so that the critical information (i. e., key words and their relations) can be extracted in an appropriate way to facilitate impression generation.
Word Graph Guided Summarization for Radiology Findings
In this paper, we propose a novel method for automatic impression generation, where a word graph is constructed from the findings to record the critical words and their relations, then a Word Graph guided Summarization model (WGSum) is designed to generate impressions with the help of the word graph.
Federated Semi-Supervised Learning with Class Distribution Mismatch
Similar to that encountered in federated supervised learning, class distribution of labeled/unlabeled data could be non-i. i. d.
Low-rank Matrix Recovery With Unknown Correspondence
We study a matrix recovery problem with unknown correspondence: given the observation matrix $M_o=[A,\tilde P B]$, where $\tilde P$ is an unknown permutation matrix, we aim to recover the underlying matrix $M=[A, B]$.
Quantized Federated Learning under Transmission Delay and Outage Constraints
Federated learning (FL) has been recognized as a viable distributed learning paradigm which trains a machine learning model collaboratively with massive mobile devices in the wireless edge while protecting user privacy.
An Efficient Learning Framework For Federated XGBoost Using Secret Sharing And Distributed Optimization
XGBoost is one of the most widely used machine learning models in the industry due to its superior learning accuracy and efficiency.
Learning to Continuously Optimize Wireless Resource in a Dynamic Environment: A Bilevel Optimization Perspective
However, it is often challenging for these approaches to learn in a dynamic environment.
Structured Sparse Non-negative Matrix Factorization with L20-Norm for scRNA-seq Data Analysis
Non-negative matrix factorization (NMF) is a powerful tool for dimensionality reduction and clustering.
Decentralized Non-Convex Learning with Linearly Coupled Constraints
Motivated by the need for decentralized learning, this paper aims at designing a distributed algorithm for solving nonconvex problems with general linear constraints over a multi-agent network.
Optimization and Control Systems and Control Systems and Control
Learning to Continuously Optimize Wireless Resource In Episodically Dynamic Environment
We propose to build the notion of continual learning (CL) into the modeling process of learning wireless systems, so that the learning model can incrementally adapt to the new episodes, {\it without forgetting} knowledge learned from the previous episodes.
Distributed Stochastic Consensus Optimization with Momentum for Nonconvex Nonsmooth Problems
While many distributed optimization algorithms have been proposed for solving smooth or convex problems over the networks, few of them can handle non-convex and non-smooth problems.
Learning to Beamform in Heterogeneous Massive MIMO Networks
It is well-known that the problem of finding the optimal beamformers in massive multiple-input multiple-output (MIMO) networks is challenging because of its non-convexity, and conventional optimization based algorithms suffer from high computational costs.
Generating Radiology Reports via Memory-driven Transformer
Particularly, this is the first work reporting the generation results on MIMIC-CXR to the best of our knowledge.
Federated Matrix Factorization: Algorithm Design and Application to Data Clustering
Recent demands on data privacy have called for federated learning (FL) as a new distributed learning paradigm in massive and heterogeneous networks.
Learning Structured Communication for Multi-agent Reinforcement Learning
This work explores the large-scale multi-agent communication mechanism under a multi-agent reinforcement learning (MARL) setting.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Distributed Learning in the Non-Convex World: From Batch to Streaming Data, and Beyond
In particular, we {provide a selective review} about the recent techniques developed for optimizing non-convex models (i. e., problem classes), processing batch and streaming data (i. e., data types), over the networks in a distributed manner (i. e., communication and computation paradigm).
Clustering by Orthogonal NMF Model and Non-Convex Penalty Optimization
We then apply a non-convex penalty (NCP) approach to add them to the objective as penalty terms, leading to a problem that is efficiently solvable.
Stochastic Proximal Gradient Consensus Over Random Networks
We consider solving a convex, possibly stochastic optimization problem over a randomly time-varying multi-agent network.
Optimization and Control Information Theory Information Theory
Asynchronous Distributed ADMM for Large-Scale Optimization- Part I: Algorithm and Convergence Analysis
By formulating the learning problem as a consensus problem, the ADMM can be used to solve the consensus problem in a fully parallel fashion over a computer network with a star topology.
Asynchronous Distributed ADMM for Large-Scale Optimization- Part II: Linear Convergence Analysis and Numerical Performance
Unfortunately, a direct synchronous implementation of such algorithm does not scale well with the problem size, as the algorithm speed is limited by the slowest computing nodes.
