Publications

AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languages
Shamsuddeen Hassan Muhammad
Idris Abdulmumin
Abinew Ayele
Nedjma OUSIDHOUM
David Ifeoluwa Adelani
Seid Muhie Yimam
Ibrahim Ahmad
Meriem Beloucif
Saif Mohammad
Sebastian Ruder
Oumaima Hourrane
Alipio Jorge
Pavel Brazdil
Felermino Ali
Davis David
Salomey Osei
Bello Shehu-Bello
Falalu Lawan
Tajuddeen Gwadabe
Samuel Rutunda … (voir 7 de plus)
Tadesse Belay
Wendimu Baye Messelle
Hailu Balcha
Sisay Adugna Chala
Hagos Gebremichael
Bernard Opoku
Stephen Arthur
2022-12-31
EMNLP (publié)
doi.org
openreview.net
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
AmbieGen: A Search-based Framework for Autonomous Systems Testing
Dmytro Humeniuk
Foutse Khomh
Giuliano Antoniol
2022-12-31
Sci. Comput. Program. (publié)
doi.org
arxiv.org
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
Auxiliary Losses for Learning Generalizable Concept-based Models
Ivaxi Sheth
S Ebrahimi Kahou
2022-12-31
Advances in Neural Information Processing Systems 36 (NeurIPS 2023) (publié)
doi.org
openreview.net
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
Best-Case Retrieval Evaluation: Improving the Sensitivity of Reciprocal Rank with Lexicographic Precision
Fernando Diaz
Across a variety of ranking tasks, researchers use reciprocal rank to measure the effectiveness for users interested in exactly one relevant… (voir plus) item. Despite its widespread use, evidence suggests that reciprocal rank is brittle when discriminating between systems. This brittleness, in turn, is compounded in modern evaluation settings where current, high-precision systems may be difficult to distinguish. We address the lack of sensitivity of reciprocal rank by introducing and connecting it to the concept of best-case retrieval, an evaluation method focusing on assessing the quality of a ranking for the most satisfied possible user across possible recall requirements. This perspective allows us to generalize reciprocal rank and define a new preference-based evaluation we call lexicographic precision or lexiprecision. By mathematical construction, we ensure that lexiprecision preserves differences detected by reciprocal rank, while empirically improving sensitivity and robustness across a broad set of retrieval and recommendation tasks.
2022-12-31
EVIA (publié)
doi.org
arxiv.org
Bugs in the Data: How ImageNet Misrepresents Biodiversity
Alexandra Luccioni
David Rolnick
ImageNet-1k is a dataset often used for benchmarking machine learning (ML) models and evaluating tasks such as image recognition and object … (voir plus)detection. Wild animals make up 27% of ImageNet-1k but, unlike classes representing people and objects, these data have not been closely scrutinized. In the current paper, we analyze the 13,450 images from 269 classes that represent wild animals in the ImageNet-1k validation set, with the participation of expert ecologists. We find that many of the classes are ill-defined or overlapping, and that 12% of the images are incorrectly labeled, with some classes having >90% of images incorrect. We also find that both the wildlife-related labels and images included in ImageNet-1k present significant geographical and cultural biases, as well as ambiguities such as artificial animals, multiple species in the same image, or the presence of humans. Our findings highlight serious issues with the extensive use of this dataset for evaluating ML systems, the use of such algorithms in wildlife-related tasks, and more broadly the ways in which ML datasets are commonly created and curated.
2022-12-31
AAAI (publié)
doi.org
arxiv.org
Cache-Efficient Dynamic Programming MDP Solver
Jaël Champagne Gareau
Guillaume Gosset
Éric Beaudry
Vladimir Makarenkov
2022-12-31
European Conference on Artificial Intelligence (publié)
doi.org