Ziyang Song

Deep Ganguli

Liane Lovitt

Jackson Kernion

Amanda Askell

Yuntao Bai

Saurav Kadavath

Benjamin Mann

Ethan Perez

Nicholas Schiefer

Kamal Ndousse

Andy Jones

Sam Bowman

Anna Chen

Tom Con-erly

Nova Dassarma

Dawn Drain

Nelson Elhage Sheer

Stanislav Fort

Zac Hatfield-Dodds

T. Henighan

Danny Hernandez

Tristan Hume

Josh Jacobson

Scott Johnston

Shauna Kravec

Catherine Olsson

Sam Ringer

Eli Tran-Johnson

Dario Amodei

Tom Brown

Nicholas Joseph

Sam McCandlish

Chris Olah

Jared Kaplan

Jack Clark. 2022. Red

Aaron Grattafiori

Abhimanyu Dubey

Abhinav Jauhri

Abhinav Pandey

Abhishek Kadian

Ahmad Al-Dahle

Aiesha Letman

Akhil Mathur

Alan Schel-ten

Alex Vaughan

Amy Yang

Angela Fan

Anirudh Goyal

A. Hartshorn

Aobo Yang

Archi Mitra

Archie Sravankumar

Artem Korenev

Arthur Hinsvark

Arun Rao

Aston Zhang

Aurelien Ro-driguez

Austen Gregerson

Ava Spataru

Baptiste Rozière

Bethany Biron

Binh Tang

Bobbie Chern

Charlotte Caucheteux

Chaya Nayak

Chloe Bi

Chris Marra

Chris McConnell

Christian Keller

Christophe Touret

Chunyang Wu

Corinne Wong

Cris-tian Cantón Ferrer

Cyrus Nikolaidis

Damien Al-lonsius

Daniel Song

Danielle Pintz

Danny Livshits

Danny Wyatt

David Esiobu

Dhruv Choudhary

Dhruv Mahajan 0001

Diego Garcia-Olano

Diego Perino

Dieuwke Hupkes

Egor Lakomkin

Ehab A. AlBadawy

Elina Lobanova

Emily Dinan

Eric Michael Smith

Filip Radenovic

Francisco Guzmán

Frank Zhang

Gabriele Synnaeve

Gabrielle Lee

Georgia Lewis

G. Thattai

Graeme Nail

Gregoire Mi-alon

Guan Pang

Guillem Cucurell

Hailey Nguyen

Han-nah Korevaar

Hu Xu

Hugo Touvron

Imanol Iliyan Zarov

Arrieta Ibarra

Is-abel Kloumann

Ishan Misra

Ivan Evtimov

Jack Zhang

Jade Copet

Jaewon Lee

Jan Geffert

Jana Vranes

Jason Park

Jay Mahadeokar

Jeet Shah

Jelmer van der Linde

Jennifer Billock

Jenny Hong

Jenya Lee

Jeremy Fu

J. Fu

Jianfeng Chi

Jianyu Huang

Jiawen Liu

Jie Wang

Jiecao Yu

Joanna Bitton

Joe Spisak

Jongsoo Park

Joseph Rocca

J. Johnstun

Joshua Saxe

Junteng Jia

Kalyan Vasuden Alwala

Karthik Prasad

Kartikeya Upasani

Kate Plawiak

Keqian Li

K. Heafield

Kevin R. Stone

Khalid El-Arini

Krithika Iyer

Kshitiz Malik

Kuen-ley Chiu

Kunal Bhalla

Kushal Lakhotia

Lauren Rantala-Yeary

Laurens van der Maaten

Lawrence Chen

Liang Tan

Liz Jenkins

Louis Martin

Lovish Madaan

Lubo Malo

Lukas Blecher

Lukas Landzaat

Luke de Oliveira

Madeline Muzzi

Mahesh Pasupuleti

Mannat Singh

Manohar Paluri

Marcin Kardas

Maria Tsimpoukelli

Mathew Oldham

Mathieu Rita

Maya Pavlova

Melanie Kam-badur

Mike Lewis

Mitesh Min Si

Kumar Singh

Mona Hassan

Naman Goyal

Narjes Torabi

Niko-lay Bashlykov

Nikolay Bogoychev

Niladri S. Chatterji

Ning Zhang

Olivier Duchenne

Onur Çelebi

Patrick Alrassy

Petar Pengwei Li

Peter Weng

Prajjwal Bhargava

Pratik Dubal

Punit Praveen Krishnan

Singh Koura

Puxin Xu

Qing He

Qingxiao Dong

Ragavan Srinivasan

Raj Ganapathy

Ramon Calderer

Ricardo Silveira Cabral

Robert Stojnic

Roberta Raileanu

Rohan Maheswari

Rohit Girdhar

Rohit Patel

Ro-main Sauvestre

Ron-nie Polidoro

Roshan Sumbaly

Ross Taylor

Ruan Silva

Rui Hou

Rui Wang

S. Hosseini

Sa-hana Chennabasappa

Sanjay Singh

Sean Bell

Seo-hyun Sonia Kim

Sergey Edunov

Shaoliang Nie

Sharan Narang

Sharath Chandra Raparthy

Sheng Shen

Shengye Wan

Shruti Bhosale

Shun Zhang

Simon Van-denhende

Soumya Batra

Spencer Whitman

Sten Sootla

Stephane Collot

Suchin Gururangan

S. Borodinsky

Tamar Herman

Tara Fowler

Tarek Sheasha

Thomas Georgiou

Thomas Scialom

Tobias Speckbacher

Todor Mihaylov

Tong Xiao

Ujjwal Karn

Vedanuj Goswami

Vibhor Gupta

Vignesh Ramanathan

Viktor Kerkez

Vincent Gonguet

Vir-ginie Do

Vish Vogeti

Vitor Albiero

Vladan Petro-vic

Weiwei Chu

Wenhan Xiong

Wenyin Fu

2025-05-28

ArXiv (preprint)

Extrapolatable Transformer Pre-training for Ultra Long Time-Series Forecasting

Qincheng Lu

Hao Xu

Mike He Zhu

2024-12-16

Proceedings of the 15th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics (published)

MixEHR-Nest: Identifying Subphenotypes within Electronic Health Records through Hierarchical Guided-Topic Modeling

Ruohan Wang

Zilong Wang

Automatic subphenotyping from electronic health records (EHRs)provides numerous opportunities to understand diseases with unique subgroups a… (see more)nd enhance personalized medicine for patients. However, existing machine learning algorithms either focus on specific diseases for better interpretability or produce coarse-grained phenotype topics without considering nuanced disease patterns. In this study, we propose a guided topic model, MixEHR-Nest, to infer sub-phenotype topics from thousands of disease using multi-modal EHR data. Specifically, MixEHR-Nest detects multiple subtopics from each phenotype topic, whose prior is guided by the expert-curated phenotype concepts such as Phenotype Codes (PheCodes) or Clinical Classification Software (CCS) codes. We evaluated MixEHR-Nest on two EHR datasets: (1) the MIMIC-III dataset consisting of over 38 thousand patients from intensive care unit (ICU) from Beth Israel Deaconess Medical Center (BIDMC) in Boston, USA; (2) the healthcare administrative database PopHR, comprising 1.3 million patients from Montreal, Canada. Experimental results demonstrate that MixEHR-Nest can identify subphenotypes with distinct patterns within each phenotype, which are predictive for disease progression and severity. Consequently, MixEHR-Nest distinguishes between type 1 and type 2 diabetes by inferring subphenotypes using CCS codes, which do not differentiate these two subtype concepts. Additionally, MixEHR-Nest not only improved the prediction accuracy of short-term mortality of ICU patients and initial insulin treatment in diabetic patients but also revealed the contributions of subphenotypes. For longitudinal analysis, MixEHR-Nest identified subphenotypes of distinct age prevalence under the same phenotypes, such as asthma, leukemia, epilepsy, and depression. The MixEHR-Nest software is available at GitHub: https://github.com/li-lab-mcgill/MixEHR-Nest.

2024-12-16

Proceedings of the 15th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics (published)

Bidirectional Generative Pre-training for Improving Healthcare Time-series Representation Learning

Qincheng Lu

Learning time-series representations for discriminative tasks, such as classification and regression, has been a long-standing challenge in … (see more)the healthcare domain. Current pre-training methods are limited in either unidirectional next-token prediction or randomly masked token prediction. We propose a novel architecture called Bidirectional Timely Generative Pre-trained Transformer (BiTimelyGPT), which pre-trains on biosignals and longitudinal clinical records by both next-token and previous-token prediction in alternating transformer layers. This pre-training task preserves original distribution and data shapes of the time-series. Additionally, the full-rank forward and backward attention matrices exhibit more expressive representation capabilities. Using biosignals and longitudinal clinical records, BiTimelyGPT demonstrates superior performance in predicting neurological functionality, disease diagnosis, and physiological signs. By visualizing the attention heatmap, we observe that the pre-trained BiTimelyGPT can identify discriminative segments from biosignal time-series sequences, even more so after fine-tuning on the task.

2024-11-25

Proceedings of the 9th Machine Learning for Healthcare Conference (published)

proceedings.mlr.press

TrajGPT: Healthcare Time-Series Representation Learning for Trajectory Prediction

Qincheng Lu

Mike He Zhu

In many domains, such as healthcare, time-series data is irregularly sampled with varying intervals between observations. This creates chall… (see more)enges for classical time-series models that require equally spaced data. To address this, we propose a novel time-series Transformer called **Trajectory Generative Pre-trained Transformer (TrajGPT)**. It introduces a data-dependent decay mechanism that adaptively forgets irrelevant information based on clinical context. By interpreting TrajGPT as ordinary differential equations (ODEs), our approach captures continuous dynamics from sparse and irregular time-series data. Experimental results show that TrajGPT, with its time-specific inference approach, accurately predicts trajectories without requiring task-specific fine-tuning.

2024-10-10

NeurIPS.cc/2024/Workshop/TSALM (published)

openreview.net

TrajGPT: Healthcare Time-Series Representation Learning for Trajectory Prediction

Qincheng Lu

Mike He Zhu

In many domains, such as healthcare, time-series data is irregularly sampled with varying intervals between observations. This creates chall… (see more)enges for classical time-series models that require equally spaced data. To address this, we propose a novel time-series Transformer called **Trajectory Generative Pre-trained Transformer (TrajGPT)**. It introduces a data-dependent decay mechanism that adaptively forgets irrelevant information based on clinical context. By interpreting TrajGPT as ordinary differential equations (ODEs), our approach captures continuous dynamics from sparse and irregular time-series data. Experimental results show that TrajGPT, with its time-specific inference approach, accurately predicts trajectories without requiring task-specific fine-tuning.

2024-10-10

NeurIPS.cc/2024/Workshop/TSALM (published)

openreview.net

TrajGPT: Irregular Time-Series Representation Learning for Health Trajectory Analysis

Qincheng Lu

Mike He Zhu

2024-10-03

ArXiv (preprint)

openreview.net

Bidirectional Generative Pre-training for Improving Healthcare Time-series Representation Learning

Qincheng Lu

Learning time-series representations for discriminative tasks, such as classification and regression, has been a long-standing challenge in … (see more)the healthcare domain. Current pre-training methods are limited in either unidirectional next-token prediction or randomly masked token prediction. We propose a novel architecture called Bidirectional Timely Generative Pre-trained Transformer (BiTimelyGPT), which pre-trains on biosignals and longitudinal clinical records by both next-token and previous-token prediction in alternating transformer layers. This pre-training task preserves original distribution and data shapes of the time-series. Additionally, the full-rank forward and backward attention matrices exhibit more expressive representation capabilities. Using biosignals and longitudinal clinical records, BiTimelyGPT demonstrates superior performance in predicting neurological functionality, disease diagnosis, and physiological signs. By visualizing the attention heatmap, we observe that the pre-trained BiTimelyGPT can identify discriminative segments from biosignal time-series sequences, even more so after fine-tuning on the task.

2024-02-14

ArXiv (preprint)

Bidirectional Generative Pre-training for Improving Time Series Representation Learning

Qincheng Lu

Mike He Zhu

2024-01-01

arXiv.org (preprint)

Bidirectional Generative Pre-training for Improving Healthcare Time-series Representation Learning

Qincheng Lu

Mike He Zhu

2024-01-01

MLHC (published)

proceedings.mlr.press

TimelyGPT: Extrapolatable Transformer Pre-training for Long-term Time-Series Forecasting in Healthcare

Qincheng Lu

Hao Xu

Mike He Zhu

2023-11-29

ArXiv (preprint)

TimelyGPT: Extrapolatable Transformer Pre-training for Long-term Time-Series Forecasting in Healthcare

Qincheng Lu

Hao Xu

Mike He Zhu

Motivation: Large-scale pre-trained models (PTMs) such as BERT and GPT have recently achieved great success in Natural Language Processing a… (see more)nd Computer Vision domains. However, the development of PTMs on healthcare time-series data is lagging behind. This underscores the limitations of the existing transformer-based architectures, particularly their scalability to handle large-scale time series and ability to capture long-term temporal dependencies. Methods: In this study, we present Timely Generative Pre-trained Transformer (TimelyGPT). TimelyGPT employs an extrapolatable position (xPos) embedding to encode trend and periodic patterns into time-series representations. It also integrates recurrent attention and temporal convolution modules to effectively capture global-local temporal dependencies. Materials: We evaluated TimelyGPT on two large-scale healthcare time series datasets corresponding to continuous biosignals and irregularly-sampled time series, respectively: (1) the Sleep EDF dataset consisting of over 1.2 billion timesteps; (2) the longitudinal healthcare administrative database PopHR, comprising 489,000 patients randomly sampled from the Montreal population. Results: In forecasting continuous biosignals, TimelyGPT achieves accurate extrapolation up to 6,000 timesteps of body temperature during the sleep stage transition, given a short look-up window (i.e., prompt) containing only 2,000 timesteps. For irregularly-sampled time series, TimelyGPT with a proposed time-specific inference demonstrates high top recall scores in predicting future diagnoses using early diagnostic records, effectively handling irregular intervals between clinical records. Together, we envision TimelyGPT to be useful in various health domains, including long-term patient health state forecasting and patient risk trajectory prediction. Availability: The open-sourced code is available at Github.

2023-11-29

ArXiv (preprint)