Publications

Towards Continual Reinforcement Learning: A Review and Perspectives
Khimya Khetarpal
Matthew D Riemer
Irina Rish
Doina Precup
2022-12-21
Journal of Artificial Intelligence Research (publié)
doi.org
arxiv.org
Meta-topologies define distinct anatomical classes of brain tumours linked to histology and survival
Julius M. Kernbach
Daniel Delev
Georg Neuloh
Hans Clusmann
Danilo Bzdok
Simon B. Eickhoff
Victor E. Staartjes
Flavio Vasella
Michael Weller
Luca Regli
Carlo Serra
Niklaus Krayenbühl
Kevin Akeret
The current World Health Organization classification integrates histological and molecular features of brain tumours. The aim of this study … (voir plus)was to identify generalizable topological patterns with the potential to add an anatomical dimension to the classification of brain tumours. We applied non-negative matrix factorization as an unsupervised pattern discovery strategy to the fine-grained topographic tumour profiles of 936 patients with neuroepithelial tumours and brain metastases. From the anatomical features alone, this machine learning algorithm enabled the extraction of latent topological tumour patterns, termed meta-topologies. The optimal part-based representation was automatically determined in 10 000 split-half iterations. We further characterized each meta-topology’s unique histopathologic profile and survival probability, thus linking important biological and clinical information to the underlying anatomical patterns. In neuroepithelial tumours, six meta-topologies were extracted, each detailing a transpallial pattern with distinct parenchymal and ventricular compositions. We identified one infratentorial, one allopallial, three neopallial (parieto-occipital, frontal, temporal) and one unisegmental meta-topology. Each meta-topology mapped to distinct histopathologic and molecular profiles. The unisegmental meta-topology showed the strongest anatomical–clinical link demonstrating a survival advantage in histologically identical tumours. Brain metastases separated to an infra- and supratentorial meta-topology with anatomical patterns highlighting their affinity to the cortico-subcortical boundary of arterial watershed areas.Using a novel data-driven approach, we identified generalizable topological patterns in both neuroepithelial tumours and brain metastases. Differences in the histopathologic profiles and prognosis of these anatomical tumour classes provide insights into the heterogeneity of tumour biology and might add to personalized clinical decision-making.
2022-12-19
Brain Communications (publié)
doi.org
BLOOM+1: Adding Language Support to BLOOM for Zero-Shot Prompting
Zheng Xin Yong
Hailey Schoelkopf
Niklas Muennighoff
Alham Fikri Aji
David Ifeoluwa Adelani
Khalid Almubarak
M. Saiful Bari
Lintang A. Sutawika
Jungo Kasai
Ahmed Baruwa
Genta Indra Winata
Stella Biderman
Dragomir R. Radev
Vassilina Nikoulina
The BLOOM model is a large publicly available multilingual language model, but its pretraining was limited to 46 languages. To extend the be… (voir plus)nefits of BLOOM to other languages without incurring prohibitively large costs, it is desirable to adapt BLOOM to new languages not seen during pretraining. In this work, we apply existing language adaptation strategies to BLOOM and benchmark its zero-shot prompting performance on eight new languages in a resource-constrained setting. We find language adaptation to be effective at improving zero-shot performance in new languages. Surprisingly, we find that adapter-based finetuning is more effective than continued pretraining for large models. In addition, we discover that prompting performance is not significantly affected by language specifics, such as the writing system. It is primarily determined by the size of the language adaptation data. We also add new languages to BLOOMZ, which is a multitask finetuned version of BLOOM capable of following task instructions zero-shot. We find including a new language in the multitask fine-tuning mixture to be the most effective method to teach BLOOMZ a new language. We conclude that with sufficient training data language adaptation can generalize well to diverse languages. Our code is available at https://github.com/bigscience-workshop/multilingual-modeling.
2022-12-18
ArXiv (prépublication)
doi.org
arxiv.org
Biomedical image analysis competitions: The state of current participation practice
Matthias Eisenmann
Annika Reinke
Vivienn Weru
Minu Dietlinde Tizabi
Fabian Isensee
T. Adler
PATRICK GODAU
Veronika Cheplygina
Michal Kozubek
Sharib Ali
Anubha Gupta
Jan. Kybic
Alison Professor Noble
Carlos Ortiz de Sol'orzano
Samiksha Pachade
Caroline Petitjean
Daniel Sage
Donglai Wei
Elizabeth Wilden
Deepak Alapatt … (voir 334 de plus)
Vincent Andrearczyk
Ujjwal Baid
Spyridon Bakas
Niranjan Balu
Sophia Bano
Vivek Singh Bawa
Jorge Bernal
Sebastian Bodenstedt
Alessandro Casella
Jinwook Choi
Olivier Commowick
M. Daum
Adrien Depeursinge
Reuben Dorent
J. Egger
H. Eichhorn
Sandy Engelhardt
Melanie Ganz
Gabriel Girard
Lasse Donovan Hansen
Mattias Paul Heinrich
Nicholas Heller
Alessa Hering
Arnaud Huaulm'e
Hyunjeong Kim
Bennett Landman
Hongwei Bran Li
Jianning Li
Junfang Ma
Anne L. Martel
Carlos Mart'in-Isla
Bjoern Menze
Chinedu Innocent Nwoye
Valentin Oreiller
Nicolas Padoy
Sarthak Pati
Kelly Payette
Carole H. Sudre
K. V. Wijnen
Armine Vardazaryan
Tom Kamiel Magda Vercauteren
Martin Wagner
Chuanbo Wang
Moi Hoon Yap
Zeyun Yu
Chuner Yuan
Maximilian Zenk
Aneeq Zia
David Zimmerer
Rina Bao
Chanyeol Choi
Andrew Cohen
Oleh Dzyubachyk
Adrian Galdran
Tianyuan Gan
Tianqi Guo
Pradyumna Gupta
M. Haithami
Edward Ho
Ikbeom Jang
Zhili Li
Zheng Luo
Filip Lux
Sokratis Makrogiannis
Dominikus Muller
Young-Tack Oh
Subeen Pang
Constantin Pape
Gorkem Polat
Charlotte Rosalie Reed
Kanghyun Ryu
Tim Scherr
Vajira L. Thambawita
Haoyu Wang
Xinliang Wang
Kele Xu
H.-I. Yeh
Doyeob Yeo
Yi Yuan
Yan Zeng
Xingwen Zhao
Julian Ronald Abbing
Jannes Adam
Nagesh Adluru
Niklas Agethen
S. Ahmed
Yasmina Al Khalil
Mireia Alenya
Esa J. Alhoniemi
C. An
Talha E Anwar
Tewodros Arega
Netanell Avisdris
D. Aydogan
Yi-Shi Bai
Maria Baldeon Calisto
Berke Doga Basaran
Marcel Beetz
Cheng Bian
Hao-xuan Bian
Kevin Blansit
Louise Bloch
Robert Bohnsack
Sara Bosticardo
J. Breen
Mikael Brudfors
Raphael Brungel
Mariano Cabezas
Alberto Cacciola
Zhiwei Chen
Yucong Chen
Dan Chen
Minjeong Cho
Min-Kook Choi
Chuantao Xie Chuantao Xie
Dana Cobzas
Julien Cohen-Adad
Jorge Corral Acero
Sujit Kumar Das
Marcela de Oliveira
Hanqiu Deng
Guiming Dong
Lars Doorenbos
Cory Efird
Di Fan
Mehdi Fatan Serj
Alexandre Fenneteau
Lucas Fidon
Patryk Filipiak
Ren'e Finzel
Nuno Renato Freitas
C. Friedrich
Mitchell J. Fulton
Finn Gaida
Francesco Galati
Christoforos Galazis
Changna Gan
Zheyao Gao
Sheng Gao
Matej Gazda
Beerend G. A. Gerats
Neil Getty
Adam Gibicar
Ryan J. Gifford
Sajan Gohil
Maria Grammatikopoulou
Daniel Grzech
Orhun Guley
Timo Gunnemann
Chun-Hai Guo
Sylvain Guy
Heonjin Ha
Luyi Han
Ilseok Han
Ali Hatamizadeh
Tianhai He
Ji-Wu Heo
Sebastian Hitziger
SeulGi Hong
Seungbum Hong
Rian Huang
Zi-You Huang
Markus Huellebrand
Stephan Huschauer
M. Hussain
Tomoo Inubushi
Ece Isik Polat
Mojtaba Jafaritadi
Seonghun Jeong
Bailiang Jian
Yu Jiang
Zhifan Jiang
Yu Jin
Smriti Joshi
A. Kadkhodamohammadi
R. A. Kamraoui
Inhak Kang
Jun-Su Kang
Davood Karimi
April Ellahe Khademi
Muhammad Irfan Khan
Suleiman A. Khan
Rishab Khantwal
Kwang-Ju Kim
Timothy Lee Kline
Satoshi Kondo
Elina Kontio
Adrian Krenzer
Artem Kroviakov
Hugo J. Kuijf
Satyadwyoom Kumar
Francesco La Rosa
Abhishek Lad
Doohee Lee
Minho Lee
Chiara Lena
Hao Li
Ling Li
Xingyu Li
F. Liao
Kuan-Ya Liao
Arlindo L. Oliveira
Chaonan Lin
Shanhai Lin
Akis Linardos
M. Linguraru
Han Liu
Tao Liu
Dian Liu
Yanling Liu
Joao Lourencco-Silva
Jing Lu
Jia Lu
Imanol Luengo
Christina Bach Lund
Huan Minh Luu
Yingqi Lv
Uzay Macar
Leon Maechler
L. SinaMansour
Kenji Marshall
Moona Mazher
Richard McKinley
Alfonso Medela
Felix Meissen
Mingyuan Meng
Dylan Bradley Miller
S. Mirjahanmardi
Arnab Kumar Mishra
Samir Mitha
Hassan Mohy-ud-Din
Tony C. W. Mok
Gowtham Krishnan Murugesan
Enamundram Naga Karthik
Sahil Nalawade
Jakub Nalepa
M. Naser
Ramin Nateghi
Hammad Naveed
Quang-Minh Nguyen
Cuong Nguyen Quoc
Brennan Nichyporuk
Bruno Oliveira
David Owen
Jimut Bahan Pal
Junwen Pan
Wei-Dong Pan
Winnie Pang
Bogyu Park
Vivek G. Pawar
Kamlesh Pawar
Michael Peven
Lena Philipp
Tomasz Pieciak
Szymon S Płotka
Marcel Plutat
Fattane Pourakpour
Domen Prelovznik
K. Punithakumar
Abdul Qayyum
Sandro Queir'os
Arman Rahmim
Salar Razavi
Jintao Ren
Mina Rezaei
Jonathan Adam Rico
ZunHyan Rieu
Markus Rink
Johannes Roth
Yusely Ruiz-gonzalez
Numan Saeed
Anindo Saha
Mostafa M. Sami Salem
Ricardo Sanchez-matilla
Kurt G Schilling
Weizhen Shao
Zhiqiang Shen
Ruize Shi
Pengcheng Shi
Daniel Sobotka
Th'eodore Soulier
Bella Specktor Fadida
D. Stoyanov
Timothy Sum Hon Mun
Xiao-Fu Sun
Rong Tao
Franz Thaler
Antoine Th'eberge
Felix Thielke
Helena R. Torres
K. Wahid
Jiacheng Wang
Yifei Wang
Wei David Wang
Xiong Jun Wang
Jianhui Wen
Ning Wen
Marek Wodziński
Yehong Wu
Fangfang Xia
Tianqi Xiang
Cheng Xiaofei
Lizhang Xu
Tingting Xue
Yu‐Xia Yang
Lingxian Yang
Kai Yao
Huifeng Yao
Amirsaeed Yazdani
Michael Yip
Hwa-Seong Yoo
Fereshteh Yousefirizi
Shu-Fen Yu
Lei Yu
Jonathan Zamora
Ramy A. Zeineldin
Dewen Zeng
Jianpeng Zhang
Bokai Zhang
Jiapeng Zhang
Fangxi Zhang
Huahong Zhang
Zhongchen Zhao
Zixuan Zhao
Jia Zhao
Can Zhao
Qiuyue Zheng
Yuheng Zhi
Ziqi Zhou
Baosheng Zou
Klaus Maier-Hein
PAUL F. JÄGER
Annette Kopp-Schneider
Lena Maier-Hein
2022-12-15
ArXiv (prépublication)
doi.org
arxiv.org
Dynamic Consolidation for Continual Learning
Hang Li
Chen Ma
X. T. Chen
Xue Liu
Abstract Training deep learning models from a stream of nonstationary data is a critical problem to be solved to achieve general artificial … (voir plus)intelligence. As a promising solution, the continual learning (CL) technique aims to build intelligent systems that have the plasticity to learn from new information without forgetting the previously obtained knowledge. Unfortunately, existing CL methods face two nontrivial limitations. First, when updating a model with new data, existing CL methods usually constrain the model parameters within the vicinity of the parameters optimized for old data, limiting the exploration ability of the model; second, the important strength of each parameter (used to consolidate the previously learned knowledge) is fixed and thus is suboptimal for the dynamic parameter updates. To address these limitations, we first relax the vicinity constraints with a global definition of the important strength, which allows us to explore the full parameter space. Specifically, we define the important strength as the sensitivity of the global loss function to the model parameters. Moreover, we propose adjusting the important strength adaptively to align it with the dynamic parameter updates. Through extensive experiments on popular data sets, we demonstrate that our proposed method outperforms the strong baselines by up to 24% in terms of average accuracy.
2022-12-13
Neural Computation (publié)
doi.org
A Tweedie Compound Poisson Model in Reproducing Kernel Hilbert Space
Yi Lian
Archer Yang
Boxiang Wang
Peng Shi
Robert William Platt
Abstract Tweedie models can be used to analyze nonnegative continuous data with a probability mass at zero. There have been wide application… (voir plus)s in natural science, healthcare research, actuarial science, and other fields. The performance of existing Tweedie models can be limited on today’s complex data problems with challenging characteristics such as nonlinear effects, high-order interactions, high-dimensionality and sparsity. In this article, we propose a kernel Tweedie model, Ktweedie, and its sparse variant, SKtweedie, that can simultaneously address the above challenges. Specifically, nonlinear effects and high-order interactions can be flexibly represented through a wide range of kernel functions, which is fully learned from the data; In addition, while the Ktweedie can handle high-dimensional data, the SKtweedie with integrated variable selection can further improve the interpretability. We perform extensive simulation studies to justify the prediction and variable selection accuracy of our method, and demonstrate the applications in ratemaking and loss-reserving in general insurance. Overall, the Ktweedie and SKtweedie outperform existing Tweedie models when there exist nonlinear effects and high-order interactions, particularly when the dimensionality is high relative to the sample size. The model is implemented in an efficient and user-friendly R package ktweedie (https://cran.r-project.org/package=ktweedie).
2022-12-12
Technometrics (publié)
doi.org
Detection and genomic analysis of BRAF fusions in Juvenile Pilocytic Astrocytoma through the combination and integration of multi-omic data
Melissa Zwaig
Audrey Baguette
Bo Hu
Michael Johnston
Hussein Lakkis
Emily M. Nakada
Damien Faury
Nikoleta Juretic
Benjamin Ellezam
Alexandre G. Weil
Jason Karamchandani
Jacek Majewski
Mathieu Blanchette
Michael D. Taylor
Marco Gallo
Claudia L. Kleinman
Nada Jabado
Jiannis Ragoussis
Juvenile Pilocytic Astrocytomas (JPAs) are one of the most common pediatric brain tumors, and they are driven by aberrant activation of the … (voir plus)mitogen-activated protein kinase (MAPK) signaling pathway. RAF-fusions are the most common genetic alterations identified in JPAs, with the prototypical KIAA1549-BRAF fusion leading to loss of BRAF’s auto-inhibitory domain and subsequent constitutive kinase activation. JPAs are highly vascular and show pervasive immune infiltration, which can lead to low tumor cell purity in clinical samples. This can result in gene fusions that are difficult to detect with conventional omics approaches including RNA-Seq. To this effect, we applied RNA-Seq as well as linked-read whole-genome sequencing and in situ Hi-C as new approaches to detect and characterize low-frequency gene fusions at the genomic, transcriptomic and spatial level. Integration of these datasets allowed the identification and detailed characterization of two novel BRAF fusion partners, PTPRZ1 and TOP2B, in addition to the canonical fusion with partner KIAA1549. Additionally, our Hi-C datasets enabled investigations of 3D genome architecture in JPAs which showed a high level of correlation in 3D compartment annotations between JPAs compared to other pediatric tumors, and high similarity to normal adult astrocytes. We detected interactions between BRAF and its fusion partners exclusively in tumor samples containing BRAF fusions. We demonstrate the power of integrating multi-omic datasets to identify low frequency fusions and characterize the JPA genome at high resolution. We suggest that linked-reads and Hi-C could be used in clinic for the detection and characterization of JPAs.
2022-12-11
BMC Cancer (publié)
doi.org
Galaxies on graph neural networks: towards robust synthetic galaxy catalogs with deep generative models
Yesukhei Jagvaral
François Lanusse
Sukhdeep Singh
Rachel Mandelbaum
Siamak Ravanbakhsh
Duncan Campbell
The future astronomical imaging surveys are set to provide precise constraints on cosmological parameters, such as dark energy. However, pro… (voir plus)-duction of synthetic data for these surveys, to test and validate analysis methods, suffers from a very high computational cost. In particular, generating mock galaxy catalogs at sufficiently large volume and high resolution will soon become computa-tionally unreachable. In this paper, we address this problem with a Deep Generative Model to create robust mock galaxy catalogs that may be used to test and develop the analysis pipelines of future weak lensing surveys. We build our model on a custom built Graph Convolutional Networks, by placing each galaxy on a graph node and then connecting the graphs within each gravitationally bound system. We train our model on a cosmological simulation with realistic galaxy populations to capture the 2D and 3D orientations of galaxies. The samples from the model exhibit comparable statistical properties to those in the simulations. To the best of our knowledge, this is the first instance of a generative model on graphs in an astrophysical/cosmological context.
2022-12-10
ArXiv (prépublication)
arxiv.org
arxiv.org
Neural Bandits for Data Mining: Searching for Dangerous Polypharmacy
Alexandre Larouche
Audrey Durand
Richard Khoury
Caroline Sirois
2022-12-09
ArXiv (prépublication)
doi.org
arxiv.org
Bayesian Q-learning With Imperfect Expert Demonstrations
Fengdi Che
Xiru Zhu
Doina Precup
David Meger
Gregory Dudek
Guided exploration with expert demonstrations improves data efficiency for reinforcement learning, but current algorithms often overuse expe… (voir plus)rt information. We propose a novel algorithm to speed up Q-learning with the help of a limited amount of imperfect expert demonstrations. The algorithm avoids excessive reliance on expert data by relaxing the optimal expert assumption and gradually reducing the usage of uninformative expert data. Experimentally, we evaluate our approach on a sparse-reward chain environment and six more complicated Atari games with delayed rewards. With the proposed methods, we can achieve better results than Deep Q-learning from Demonstrations (Hester et al., 2017) in most environments.
2022-12-08
NeurIPS.cc/2022/Workshop/DeepRL (accepté)
doi.org
openreview.net
Energy efficiency as a normative account for predictive coding
Shahab Bakhtiari
2022-12-08
Patterns (publié)
doi.org
Imitation from Observation With Bootstrapped Contrastive Learning
Medric Sonwa
Johanna Hansen
Eugene Belilovsky
2022-12-08
NeurIPS.cc/2022/Workshop/DeepRL (inconnu)
doi.org
openreview.net