Search Results for author: Dianbo Liu

Found 22 papers, 3 papers with code

hBERT + BiasCorp - Fighting Racism on the Web

no code implementations EACL (LTEDI) 2021 Olawale Onabola, Zhuang Ma, Xie Yang, Benjamin Akera, Ibraheem Abdulrahman, Jia Xue, Dianbo Liu, Yoshua Bengio

In this work, we present hBERT, where we modify certain layers of the pretrained BERT model with the new Hopfield Layer.

FedILC: Weighted Geometric Mean and Invariant Gradient Covariance for Federated Learning on Non-IID Data

1 code implementation19 May 2022 Mike He Zhu, Léna Néhale Ezzine, Dianbo Liu, Yoshua Bengio

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.

Federated Learning

Adaptive Discrete Communication Bottlenecks with Dynamic Vector Quantization

no code implementations2 Feb 2022 Dianbo Liu, Alex Lamb, Xu Ji, Pascal Notsawo, Mike Mozer, Yoshua Bengio, Kenji Kawaguchi

Vector Quantization (VQ) is a method for discretizing latent representations and has become a major part of the deep learning toolkit.

Quantization reinforcement-learning +1

PMFL: Partial Meta-Federated Learning for heterogeneous tasks and its applications on real-world medical records

no code implementations10 Dec 2021 Tianyi Zhang, Shirui Zhang, Ziwei Chen, Dianbo Liu

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.

Federated Learning Meta-Learning +1

Discrete-Valued Neural Communication

no code implementations NeurIPS 2021 Dianbo Liu, Alex Lamb, Kenji Kawaguchi, Anirudh Goyal, Chen Sun, Michael Curtis Mozer, Yoshua Bengio

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.

Quantization Systematic Generalization

HBert + BiasCorp -- Fighting Racism on the Web

no code implementations6 Apr 2021 Olawale Onabola, Zhuang Ma, Yang Xie, Benjamin Akera, Abdulrahman Ibraheem, Jia Xue, Dianbo Liu, Yoshua Bengio

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

1 code implementation ICCV 2021 Yuwei Cheng, Jiannan Zhu, Mengxin Jiang, Jie Fu, Changsong Pang, Peidong Wang, Kris Sankaran, Olawale Onabola, Yimin Liu, Dianbo Liu, Yoshua Bengio

To promote the practical application for autonomous floating wastes cleaning, we present FloW, the first dataset for floating waste detection in inland water areas.

Robust Object Detection

Patient similarity: methods and applications

no code implementations1 Dec 2020 Leyu Dai, He Zhu, Dianbo Liu

Patient similarity analysis is important in health care applications.

FakeSafe: Human Level Data Protection by Disinformation Mapping using Cycle-consistent Adversarial Network

no code implementations23 Nov 2020 He Zhu, Dianbo Liu

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

1 code implementation8 Apr 2020 Dianbo Liu, Leonardo Clemente, Canelle Poirier, Xiyu Ding, Matteo Chinazzi, Jessica T Davis, Alessandro Vespignani, Mauricio Santillana

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.

Data Augmentation Interpretable Machine Learning

Federated pretraining and fine tuning of BERT using clinical notes from multiple silos

no code implementations20 Feb 2020 Dianbo Liu, Tim Miller

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

no code implementations25 Dec 2019 Jianfei Cui, He Zhu, Hao Deng, Ziwei Chen, Dianbo Liu

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

no code implementations23 Oct 2019 Rulin Shao, Hongyu He, Hui Liu, Dianbo Liu

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.

Federated Learning

Confederated Machine Learning on Horizontally and Vertically Separated Medical Data for Large-Scale Health System Intelligence

no code implementations ICLR 2020 Dianbo Liu, Kathe Fox, Griffin Weber, Tim Miller

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.

Federated Learning

Privacy Preserving Stochastic Channel-Based Federated Learning with Neural Network Pruning

no code implementations4 Oct 2019 Rulin Shao, Hui Liu, Dianbo Liu

Artificial neural network has achieved unprecedented success in a wide variety of domains such as classifying, predicting and recognizing objects.

Federated Learning Network Pruning

Two-stage Federated Phenotyping and Patient Representation Learning

no code implementations WS 2019 Dianbo Liu, Dmitriy Dligach, Timothy Miller

A large percentage of medical information is in unstructured text format in electronic medical record systems.

Representation Learning

Patient Clustering Improves Efficiency of Federated Machine Learning to predict mortality and hospital stay time using distributed Electronic Medical Records

no code implementations22 Mar 2019 Li Huang, Dianbo Liu

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

no code implementations23 Dec 2018 Dianbo Liu, Nestor Sepulveda, Ming Zheng

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.

Mortality Prediction Quantization

LoAdaBoost: loss-based AdaBoost federated machine learning with reduced computational complexity on IID and non-IID intensive care data

no code implementations30 Nov 2018 Li Huang, Yifeng Yin, Zeng Fu, Shifa Zhang, Hao Deng, Dianbo Liu

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

no code implementations28 Nov 2018 Dianbo Liu, Timothy Miller, Raheel Sayeed, Kenneth D. Mandl

Electronic health record (EHR) data is collected by individual institutions and often stored across locations in silos.

Federated Learning

DeepFaceLIFT: Interpretable Personalized Models for Automatic Estimation of Self-Reported Pain

no code implementations9 Aug 2017 Dianbo Liu, Fengjiao Peng, Andrew Shea, Ognjen, Rudovic, Rosalind Picard

Previous research on automatic pain estimation from facial expressions has focused primarily on "one-size-fits-all" metrics (such as PSPI).

Multi-Task Learning

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