Publications

Step-GRAND: A Low Latency Universal Soft-Input Decoder
Syed Mohsin Abbas
Marwan Jalaleddine
Chi-Ying Tsui
Warren J. Gross
GRAND features both soft-input and hard-input variants that are well suited to efficient hardware implementations that can be characterized … (see more)with achievable average and worst-case decoding latency. This paper introduces step-GRAND, a soft-input variant of GRAND that, in addition to achieving appealing average decoding latency, also reduces the worst-case decoding latency of the corresponding hardware implementation. The hardware implementation results demonstrate that the proposed step-GRAND can decode CA-polar code (128,105+11) with an average information throughput of 47.7 Gbps at the target FER of
Decision Diagrams in Space!
Isaac Rudich
Manuel L'opez-Ib'anez
Michael Romer
Louis-Martin Rousseau
Learning Lyapunov-Stable Polynomial Dynamical Systems Through Imitation
Imitation learning is a paradigm to address complex motion planning problems by learning a policy to imitate an expert’s behavior. However… (see more), relying solely on the expert’s data might lead to unsafe actions when the robot deviates from the demonstrated trajectories. Stability guarantees have previously been provided utilizing nonlinear dynamical systems, acting as high-level motion planners, in conjunction with the Lyapunov stability theorem. Yet, these methods are prone to inaccurate policies, high computational cost, sample inefficiency, or quasi stability when replicating complex and highly nonlinear trajectories. To mitigate this problem, we present an approach for learning a globally stable nonlinear dynamical system as a motion planning policy. We model the nonlinear dynamical system as a parametric polynomial and learn the polynomial’s coefficients jointly with a Lyapunov candidate. To showcase its success, we compare our method against the state of the art in simulation and conduct real-world experiments with the Kinova Gen3 Lite manipulator arm. Our experiments demonstrate the sample efficiency and reproduction accuracy of our method for various expert trajectories, while remaining stable in the face of perturbations.
Advancing Clinical Psychiatry: Integration of Clinical and Omics Data Using Machine Learning
Bill Qi
AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languages
Shamsuddeen Hassan Muhammad
Idris Abdulmumin
Abinew Ayele
Nedjma OUSIDHOUM
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 … (see 7 more)
Tadesse Belay
Wendimu Baye Messelle
Hailu Balcha
Sisay Adugna Chala
Hagos Gebremichael
Bernard Opoku
Stephen Arthur
Anastomotic leak rates after repair of mesenteric bucket-handle injuries: A multi-center retrospective cohort study.
Chathurika S. Dhanasekara
Brianna Marschke
Erin Morris
Bryan S. Bashrum
K. Shrestha
Robyn E. Richmond
S. Dissanaike
A. Ko
Lakshika Tennakoon
Eric M. Campion
Frank N. Wood
Maggie Brandt
Grace Ng
Justin L Regner
Stacey Keith
M. Mcnutt
Heather Kregel
R. Gandhi
Thomas J. Schroeppel
D. Margulies … (see 5 more)
Y. Hashim
Joseph A. Herrold
Mallory Goetz
LeRone Simpson
Doan Xuan-Lan
Automatic Head and Neck Tumor segmentation and outcome prediction relying on FDG-PET/CT images: Findings from the second edition of the HECKTOR challenge
Vincent Andrearczyk
Valentin Oreiller
Sarah Boughdad
Catherine Cheze Le Rest
Olena Tankyevych
Hesham M. Elhalawani
Mario Jreige
John O. Prior
Dimitris Visvikis
Mathieu Hatt
Adrien Depeursinge
Balaur: Language Model Pretraining with Lexical Semantic Relations
Jackie CK Cheung
Better Quality Pre-training Data and T5 Models for African Languages
Akintunde Oladipo
Mofetoluwa Adeyemi
Orevaoghene Ahia
Abraham Toluwase Owodunni
Odunayo Ogundepo
Jimmy Lin
In this study, we highlight the importance of enhancing the quality of pretraining data in multilingual language models. Existing web crawl… (see more)s have demonstrated quality issues, particularly in the context of low-resource languages. Consequently, we introduce a new multilingual pretraining corpus for
Can Retriever-Augmented Language Models Reason? The Blame Game Between the Retriever and the Language Model
Augmenting pretrained language models with retrievers to select the supporting documents has shown promise in effectively solving common NLP… (see more) problems, including language modeling and question answering, in an interpretable way. In this paper, we first study the strengths and weaknesses of different retriever-augmented language models (REALM,
Cross-lingual Open-Retrieval Question Answering for African Languages
Odunayo Ogundepo
Tajuddeen Gwadabe
Clara Rivera
Jonathan Clark
Sebastian Ruder
David Adelani
Abdou Diop
Claytone Sikasote
Gilles Hacheme
Happy Buzaaba
Ignatius Ezeani
Rooweither Mabuya
Salomey Osei
Albert Kahira
Shamsuddeen Muhammad
Akintunde Oladipo
Abraham Owodunni
Atnafu Tonja … (see 24 more)
Iyanuoluwa Shode
Akari Asai
Anuoluwapo Aremu
Ayodele Awokoya
Bernard Opoku
Chiamaka Chukwuneke
Christine Mwase
Clemencia Siro
Stephen Arthur
Tunde Ajayi
Verrah Otiende
Andre Rubungo
Boyd Sinkala
Daniel Ajisafe
Emeka Onwuegbuzia
Falalu Lawan
Ibrahim Ahmad
Jesujoba Alabi
Chinedu Mbonu
Mofetoluwa Adeyemi
Mofya Phiri
Orevaoghene Ahia
Ruqayya Iro
Sonia Adhiambo
DiPS: Discriminative Pseudo-Label Sampling with Self-Supervised Transformers for Weakly Supervised Object Localization
Shakeeb Murtaza
Soufiane Belharbi
Aydin Sarraf