Search Results for author:
Found 22 papers, 3 papers with code
hBERT + BiasCorp - Fighting Racism on the Web
In this work, we present hBERT, where we modify certain layers of the pretrained BERT model with the new Hopfield Layer.
Coordinating Policies Among Multiple Agents via an Intelligent Communication Channel
In Multi-Agent Reinforcement Learning (MARL), specialized channels are often introduced that allow agents to communicate directly with one another.
FedILC: Weighted Geometric Mean and Invariant Gradient Covariance for Federated Learning on Non-IID Data
Federated learning is a distributed machine learning approach which enables a shared server model to learn by aggregating the locally-computed parameter updates with the training data from spatially-distributed client silos.
Adaptive Discrete Communication Bottlenecks with Dynamic Vector Quantization
Vector Quantization (VQ) is a method for discretizing latent representations and has become a major part of the deep learning toolkit.
PMFL: Partial Meta-Federated Learning for heterogeneous tasks and its applications on real-world medical records
Federated machine learning is a versatile and flexible tool to utilize distributed data from different sources, especially when communication technology develops rapidly and an unprecedented amount of data could be collected on mobile devices nowadays.
Discrete-Valued Neural Communication
Deep learning has advanced from fully connected architectures to structured models organized into components, e. g., the transformer composed of positional elements, modular architectures divided into slots, and graph neural nets made up of nodes.
HBert + BiasCorp -- Fighting Racism on the Web
In this work, we present hBERT, where we modify certain layers of the pretrained BERT model with the new Hopfield Layer.
FloW: A Dataset and Benchmark for Floating Waste Detection in Inland Waters
To promote the practical application for autonomous floating wastes cleaning, we present FloW, the first dataset for floating waste detection in inland water areas.
Patient similarity: methods and applications
Patient similarity analysis is important in health care applications.
FakeSafe: Human Level Data Protection by Disinformation Mapping using Cycle-consistent Adversarial Network
The concept of disinformation is to use fake messages to confuse people in order to protect the real information.
A machine learning methodology for real-time forecasting of the 2019-2020 COVID-19 outbreak using Internet searches, news alerts, and estimates from mechanistic models
We present a timely and novel methodology that combines disease estimates from mechanistic models with digital traces, via interpretable machine-learning methodologies, to reliably forecast COVID-19 activity in Chinese provinces in real-time.
Federated pretraining and fine tuning of BERT using clinical notes from multiple silos
Large scale contextual representation models, such as BERT, have significantly advanced natural language processing (NLP) in recently years.
Federated machine learning with Anonymous Random Hybridization (FeARH) on medical records
Sometimes electrical medical records are restricted and difficult to centralize for machine learning, which could only be trained in distributed manner that involved many institutions in the process.
Stochastic Channel-Based Federated Learning for Medical Data Privacy Preserving
Specifically, we design, implement and evaluate a channel-based update algorithm for the central server in a distributed system, which selects the channels with regard to the most active features in a training loop and uploads them as learned information from local datasets.
Confederated Machine Learning on Horizontally and Vertically Separated Medical Data for Large-Scale Health System Intelligence
We proposed and evaluated a confederated learning to training machine learning model to stratify the risk of several diseases among when data are horizontally separated by individual, vertically separated by data type, and separated by identity without patient ID matching.
Privacy Preserving Stochastic Channel-Based Federated Learning with Neural Network Pruning
Artificial neural network has achieved unprecedented success in a wide variety of domains such as classifying, predicting and recognizing objects.
Two-stage Federated Phenotyping and Patient Representation Learning
A large percentage of medical information is in unstructured text format in electronic medical record systems.
Patient Clustering Improves Efficiency of Federated Machine Learning to predict mortality and hospital stay time using distributed Electronic Medical Records
Electronic medical records (EMRs) supports the development of machine learning algorithms for predicting disease incidence, patient response to treatment, and other healthcare events.
Artificial neural networks condensation: A strategy to facilitate adaption of machine learning in medical settings by reducing computational burden
In this project we explored methods to increase computational efficiency of ML algorithms, in particular Artificial Neural Nets (NN), while not compromising the accuracy of the predicted results.
LoAdaBoost: loss-based AdaBoost federated machine learning with reduced computational complexity on IID and non-IID intensive care data
One challenge in applying federated machine learning is the possibly different distributions of data from diverse sources.
FADL:Federated-Autonomous Deep Learning for Distributed Electronic Health Record
Electronic health record (EHR) data is collected by individual institutions and often stored across locations in silos.
DeepFaceLIFT: Interpretable Personalized Models for Automatic Estimation of Self-Reported Pain
Previous research on automatic pain estimation from facial expressions has focused primarily on "one-size-fits-all" metrics (such as PSPI).
