Search Results for author: Dianbo Liu

Found 36 papers, 5 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.

Unsupervised Concept Discovery Mitigates Spurious Correlations

no code implementations20 Feb 2024 Md Rifat Arefin, Yan Zhang, Aristide Baratin, Francesco Locatello, Irina Rish, Dianbo Liu, Kenji Kawaguchi

Models prone to spurious correlations in training data often produce brittle predictions and introduce unintended biases.

BarlowTwins-CXR : Enhancing Chest X-Ray abnormality localization in heterogeneous data with cross-domain self-supervised learning

no code implementations9 Feb 2024 Haoyue Sheng, Linrui Ma, Jean-Francois Samson, Dianbo Liu

Conclusion: BarlowTwins-CXR significantly enhances the efficiency and accuracy of chest X-ray image-based abnormality localization, outperforming traditional transfer learning methods and effectively overcoming domain inconsistency in cross-domain scenarios.

Self-Supervised Learning Transfer Learning

Evolution Guided Generative Flow Networks

no code implementations3 Feb 2024 Zarif Ikram, Ling Pan, Dianbo Liu

Generative Flow Networks (GFlowNets) are a family of probabilistic generative models that learn to sample compositional objects proportional to their rewards.

Evolutionary Algorithms

Discrete Messages Improve Communication Efficiency among Isolated Intelligent Agents

no code implementations26 Dec 2023 Hang Chen, Yuchuan Jang, Weijie Zhou, Cristian Meo, Ziwei Chen, Dianbo Liu

Individuals, despite having varied life experiences and learning processes, can communicate effectively through languages.

Physical Reasoning and Object Planning for Household Embodied Agents

1 code implementation22 Nov 2023 Ayush Agrawal, Raghav Prabhakar, Anirudh Goyal, Dianbo Liu

In this study, we explore the sophisticated domain of task planning for robust household embodied agents, with a particular emphasis on the intricate task of selecting substitute objects.

Decision Making Object +1

Probabilistic Generative Modeling for Procedural Roundabout Generation for Developing Countries

no code implementations5 Oct 2023 Zarif Ikram, Ling Pan, Dianbo Liu

Due to limited resources and fast economic growth, designing optimal transportation road networks with traffic simulation and validation in a cost-effective manner is vital for developing countries, where extensive manual testing is expensive and often infeasible.

Causal Inference in Gene Regulatory Networks with GFlowNet: Towards Scalability in Large Systems

no code implementations5 Oct 2023 Trang Nguyen, Alexander Tong, Kanika Madan, Yoshua Bengio, Dianbo Liu

Understanding causal relationships within Gene Regulatory Networks (GRNs) is essential for unraveling the gene interactions in cellular processes.

Causal Discovery Causal Inference

Attention Schema in Neural Agents

no code implementations27 May 2023 Dianbo Liu, Samuele Bolotta, He Zhu, Yoshua Bengio, Guillaume Dumas

A strong prediction of this theory is that an agent can use its own AS to also infer the states of other agents' attention and consequently enhance coordination with other agents.

Descriptive Multi-agent Reinforcement Learning

Enhancing Human Capabilities through Symbiotic Artificial Intelligence with Shared Sensory Experiences

no code implementations26 May 2023 Rui Hao, Dianbo Liu, Linmei Hu

In this paper, we introduce a novel concept in Human-AI interaction called Symbiotic Artificial Intelligence with Shared Sensory Experiences (SAISSE), which aims to establish a mutually beneficial relationship between AI systems and human users through shared sensory experiences.

A Transformer-based Deep Learning Algorithm to Auto-record Undocumented Clinical One-Lung Ventilation Events

no code implementations16 Feb 2023 Zhihua Li, Alexander Nagrebetsky, Sylvia Ranjeva, Nan Bi, Dianbo Liu, Marcos F. Vidal Melo, Timothy Houle, Lijun Yin, Hao Deng

We hypothesized that available intraoperative mechanical ventilation and physiological time-series data combined with other clinical events could be used to accurately predict missing start and end times of OLV.

Time Series Time Series Analysis

GFlowOut: Dropout with Generative Flow Networks

no code implementations24 Oct 2022 Dianbo Liu, Moksh Jain, Bonaventure Dossou, Qianli Shen, Salem Lahlou, Anirudh Goyal, Nikolay Malkin, Chris Emezue, Dinghuai Zhang, Nadhir Hassen, Xu Ji, Kenji Kawaguchi, Yoshua Bengio

These methods face two important challenges: (a) the posterior distribution over masks can be highly multi-modal which can be difficult to approximate with standard variational inference and (b) it is not trivial to fully utilize sample-dependent information and correlation among dropout masks to improve posterior estimation.

Bayesian Inference Variational Inference

Stateful active facilitator: Coordination and Environmental Heterogeneity in Cooperative Multi-Agent Reinforcement Learning

2 code implementations4 Oct 2022 Dianbo Liu, Vedant Shah, Oussama Boussif, Cristian Meo, Anirudh Goyal, Tianmin Shu, Michael Mozer, Nicolas Heess, Yoshua Bengio

We formalize the notions of coordination level and heterogeneity level of an environment and present HECOGrid, a suite of multi-agent RL environments that facilitates empirical evaluation of different MARL approaches across different levels of coordination and environmental heterogeneity by providing a quantitative control over coordination and heterogeneity levels of the environment.

Multi-agent Reinforcement Learning reinforcement-learning +1

Graph-Based Active Machine Learning Method for Diverse and Novel Antimicrobial Peptides Generation and Selection

no code implementations18 Sep 2022 Bonaventure F. P. Dossou, Dianbo Liu, Xu Ji, Moksh Jain, Almer M. van der Sloot, Roger Palou, Michael Tyers, Yoshua Bengio

As antibiotic-resistant bacterial strains are rapidly spreading worldwide, infections caused by these strains are emerging as a global crisis causing the death of millions of people every year.

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 +2

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.

object-detection 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.

Data Integration

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.

Generative Adversarial Network

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.

BIG-bench Machine Learning Clustering +2

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.

BIG-bench Machine Learning

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 Privacy Preserving

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.

BIG-bench Machine Learning 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 +1

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.

BIG-bench Machine Learning Clustering

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.

Computational Efficiency Mortality Prediction +1

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.

BIG-bench Machine Learning 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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