Publications

« Que notre cerveau soit constitué de neurones n’est pas un accident »
Guillaume Lajoie
Roman Ikonicoff
2023-01-01
Pour la Science (inconnu)
doi.org
On the Challenges of using Reinforcement Learning in Precision Drug Dosing: Delay and Prolongedness of Action Effects
Sumana Basu
M. Legault
Adriana Romero
Doina Precup
Drug dosing is an important application of AI, which can be formulated as a Reinforcement Learning (RL) problem. In this paper, we identify … (voir plus)two major challenges of using RL for drug dosing: delayed and prolonged effects of administering medications, which break the Markov assumption of the RL framework. We focus on prolongedness and define PAE-POMDP (Prolonged Action Effect-Partially Observable Markov Decision Process), a subclass of POMDPs in which the Markov assumption does not hold specifically due to prolonged effects of actions. Motivated by the pharmacology literature, we propose a simple and effective approach to converting drug dosing PAE-POMDPs into MDPs, enabling the use of the existing RL algorithms to solve such problems. We validate the proposed approach on a toy task, and a challenging glucose control task, for which we devise a clinically-inspired reward function. Our results demonstrate that: (1) the proposed method to restore the Markov assumption leads to significant improvements over a vanilla baseline; (2) the approach is competitive with recurrent policies which may inherently capture the prolonged affect of actions; (3) it is remarkably more time and memory efficient than the recurrent baseline and hence more suitable for real-time dosing control systems; and (4) it exhibits favourable qualitative behavior in our policy analysis.
2023-01-01
ArXiv (prépublication)
doi.org
arxiv.org
2023 S TOCHASTIC S IMULATED Q UANTUM A NNEALING FOR F AST S OLVING C OMBINATORIAL O PTIMIZATION P ROBLEMS
Naoya Onizawa
Ryoma Sasaki
Duckgyu Shin
Warren J. Gross
Takahiro Hanyu
method. Additionally, it can handle a 100-times larger problem size compared to QA and a 25-times larger problem size compared to a traditio… (voir plus)nal SA method, respectively, for similar convergence probabilities.
2022-12-31
(publié)
www.semanticscholar.org
2023 S TOCHASTIC Q UANTUM M ONTE C ARLO A LGORITHM FOR L ARGE -S CALE C OMBINATORIAL O PTIMIZATION P ROBLEMS
Naoya Onizawa
Ryoma Sasaki
Duckgyu Shin
Warren J. Gross
Takahiro Hanyu
computing. In addition, it solves problems using two orders-of-magnitude larger number of spins than the D-Wave Two QA machine.
2022-12-31
(publié)
www.semanticscholar.org
Adaptive Network Interactions in Cognition After Stimulation-Induced Inferior Parietal Lobe Inhibition
Kathleen Williams
Ole Numssen
Danilo Bzdok
Gesa Hartwigsen
2022-12-31
Brain Stimulation (inconnu)
doi.org
AfriMTE and AfriCOMET: Empowering COMET to Embrace Under-resourced African Languages
Jiayi Wang
David Ifeoluwa Adelani
Sweta Agrawal
Ricardo Rei
Eleftheria Briakou
Marine Carpuat
Marek Masiak
Xuanli He
Sofia Bourhim
Andiswa Bukula
Muhidin A. Mohamed
Temitayo Olatoye
Hamam Mokayed
Christine Mwase
Wangui Kimotho
Foutse Yuehgoh
Aremu Anuoluwapo
Jessica Ojo
Shamsuddeen Hassan Muhammad
Salomey Osei … (voir 37 de plus)
Abdul-Hakeem Omotayo
Chiamaka Ijeoma Chukwuneke
Perez Ogayo
Oumaima Hourrane
Salma El Anigri
Lolwethu Ndolela
Thabiso Mangwana
Shafie Abdi Mohamed
Ayinde Hassan
Oluwabusayo Olufunke Awoyomi
Lama Alkhaled
sana Sabah al-azzawi
Naome Etori
Millicent Ochieng
Clemencia Siro
Samuel Njoroge
Eric Muchiri
Wangari Kimotho
Lyse Naomi Wamba
Daud Abolade
Simbiat Ajao
Tosin Adewumi
Iyanuoluwa Shode
Ricky Macharm
Ruqayya Nasir Iro
Saheed Salahudeen Abdullahi
Stephen Moore
Bernard Opoku
Zainab Akinjobi
Abeeb Afolabi
Nnaemeka Casmir Obiefuna
Onyekachi Ogbu
Sam Brian
Verrah Akinyi Otiende
CHINEDU EMMANUEL MBONU
Toadoum Sari Sakayo
Pontus Stenetorp
Despite the progress we have recorded in scaling multilingual machine translation (MT) models and evaluation data to several under-resourced… (voir plus) African languages, it is difficult to measure accurately the progress we have made on these languages because evaluation is often performed on n -gram matching metrics like BLEU that often have worse correlation with human judgments. Embedding-based metrics such as COMET correlate better; however, lack of evaluation data with human ratings for under-resourced languages, complexity of annotation guidelines like Multidimensional Quality Metrics (MQM), and limited language coverage of multilingual encoders have hampered their applicability to African languages. In this paper, we address these challenges by creating high-quality human evaluation data with a simplified MQM guideline for error-span annotation and direct assessment (DA) scoring for 13 typologi-cally diverse African languages. Furthermore, we develop A FRI COMET—a COMET evaluation metric for African languages by leveraging DA training data from high-resource languages and African-centric multilingual encoder (AfroXLM-Roberta) to create the state-of-the-art evaluation metric for African languages MT with respect to Spearman-rank correlation with human judgments ( +0 . 406 ).
2022-12-31
arXiv.org (prépublication)
doi.org
AI Agents Learn to Trust
Ardavan S. Nobandegani
Irina Rish
T. Shultz
2022-12-31
Annual Meeting of the Cognitive Science Society (publié)
dblp.uni-trier.de
Aperiodic brain activity and response to anesthesia vary in disorders of consciousness
Charlotte Maschke
Catherine Duclos
Adrian M. Owen
Karim Jerbi
Stefanie Blain‐Moraes
The analysis of human EEG has traditionally focused on oscillatory power, which is characterized by peaks above an aperiodic component in th… (voir plus)e power spectral density. This study investigates the aperiodic EEG component of individuals in a disorder of consciousness (DOC); how it changes in response to exposure to anesthesia; and how it relates to the brain’s information richness and criticality. High-density EEG was recorded from 43 individuals in a DOC, with 16 of these individuals undergoing a protocol of propofol anesthesia. The aperiodic component was defined by the spectral slope of the power spectral density. Our results demonstrate that the EEG aperiodic component is more informative about the participants’ level of consciousness than the oscillatory component. Importantly, the pharmacologically induced change in the spectral slope from 30-45 Hz positively correlated with individual’s pre-anesthetic level of consciousness. The pharmacologically induced loss of information-richness and criticality was associated with individual’s pre-anesthetic aperiodic component. During exposure to anesthesia, the aperiodic component was correlated with 3-month recovery status for individuals with DOC. The aperiodic EEG component has been historically neglected; this research highlights the necessity of considering this measure for the assessment of individuals in DOC and future research that seeks to understand the neurophysiological underpinnings of consciousness.
2022-12-31
NeuroImage (publié)
doi.org
ArK: Augmented Reality with Knowledge Emergent Infrastructure
Qiuyuan Huang
J. Park
Abhinav Gupta
Pan Lu
Paul N. Bennett
Ran Gong
Subhojit Som
Baolin Peng
Owais Khan Mohammed
Christopher Pal
Yejin Choi
Jianfeng Gao
Despite the growing adoption of mixed reality and interactive AI, it remains challenging to generate high-quality 2D/3D scenes in unseen env… (voir plus)ironments. Typically, an AI agent requires collecting extensive training data for every new task, which can be costly or impossible for many domains. In this study, we develop an infinite agent that learns to transfer knowledge memory from general foundation models (e.g., GPT4, DALLE) to novel domains or scenarios for scene understanding and generation in physical or virtual worlds. Central to our approach is the interactive emerging mechanism, dubbed Augmented Reality with Knowledge Emergent Infrastructure (ArK) , which leverages knowledge-memory to generate scenes in unseen physical worlds and virtual reality environments. The knowledge interactive emergent ability (Figure 1) is demonstrated through i) micro-action of cross-modality : in multi-modality models to collect a large amount of relevant knowledge-memory data for each interaction task (e.g., unseen scene understanding) from the physical reality; and ii) macro-behavior of reality-agnostic : in mix-reality environments to improve interactions that tailor to different characterized roles, target variables, collaborative information, and so on. We validate ArK’s effectiveness in scene generation and editing tasks and show that our ArK approach, combined with large foundation models, significantly improves the quality of generated 2D/3D scenes, highlighting its potential in applications such as metaverse and gaming simulation.
2022-12-31
(publié)
www.semanticscholar.org
Bayes-MIL: A New Probabilistic Perspective on Attention-based Multiple Instance Learning for Whole Slide Images
Yufei Cui
Ziquan Liu
Xiangyu Liu
Xue Liu
Cong Wang
Tei-Wei Kuo
Chun Jason Xue
Antoni B. Chan
Multiple instance learning (MIL) is a popular weakly-supervised learning model on the whole slide image (WSI) for AI-assisted pathology diag… (voir plus)nosis. The recent advance in attention-based MIL allows the model to find its region-of-interest (ROI) for interpretation by learning the attention weights for image patches of WSI slides. However, we empirically find that the interpretability of some related methods is either untrustworthy as the principle of MIL is violated or unsatisfactory as the high-attention regions are not consistent with experts’ annotations. In this paper, we propose Bayes-MIL to address the problem from a probabilistic perspective. The induced patch-level uncertainty is proposed as a new measure of MIL interpretability, which outperforms previous methods in matching doctors annotations. We design a slide-dependent patch regularizer (SDPR) for the attention, imposing constraints derived from the MIL assumption, on the attention distribution. SDPR explicitly constrains the model to generate correct attention values. The spatial information is further encoded by an approximate convolutional conditional random field (CRF), for better interpretability. Experimental results show Bayes-MIL outperforms the related methods in patch-level and slide-level metrics and provides much better interpretable ROI on several large-scale WSI datasets.
2022-12-31
ICLR (publié)
dblp.uni-trier.de
Benchmarking Graph Neural Networks
Vijay Prakash Dwivedi
Chaitanya K. Joshi
Thomas Laurent
Anh Tuan Luu
Yoshua Bengio
Xavier Bresson
2022-12-31
J. Mach. Learn. Res. (publié)
arxiv.org
arxiv.org
Benchmarking State-Merging Algorithms for Learning Regular Languages.
Adil Soubki
Jeffrey Heinz
François Coste
Faissal Ouardi
Guillaume Rabusseau
2022-12-31
International Conference on Graphics and Interaction (publié)
dblp.uni-trier.de