Publications

Hyperspherical Quantization: Toward Smaller and More Accurate Models
Dan Liu
Xi Chen
Chen Ma
Xue (Steve) Liu
Model quantization enables the deployment of deep neural networks under resource-constrained devices. Vector quantization aims at reducing t… (voir plus)he model size by indexing model weights with full-precision embeddings, i.e., codewords, while the index needs to be restored to 32-bit during computation. Binary and other low-precision quantization methods can reduce the model size up to 32×, however, at the cost of a considerable accuracy drop. In this paper, we propose an efficient framework for ternary quantization to produce smaller and more accurate compressed models. By integrating hyperspherical learning, pruning and reinitialization, our proposed Hyperspherical Quantization (HQ) method reduces the cosine distance between the full-precision and ternary weights, thus reducing the bias of the straight-through gradient estimator during ternary quantization. Compared with existing work at similar compression levels (~30×, ~40×), our method significantly improves the test accuracy and reduces the model size.
2023-01-02
2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (publié)
doi.org
arxiv.org
« Que notre cerveau soit constitué de neurones n’est pas un accident »
Guillaume Lajoie
Roman Ikonicoff
2023-01-02
Pour la science (publié)
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 Soriano
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.
2023-01-02
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 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.
2023-01-01
(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 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.
2023-01-01
(published)
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 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.
2023-01-01
(publié)
www.semanticscholar.org
Adjusting Machine Learning Decisions for Equal Opportunity and Counterfactual Fairness
Yixin Wang
Dhanya Sridhar
David Blei
Machine learning ( ml ) methods have the potential to automate high-stakes decisions, such as bail admissions or credit lending, by analyzin… (voir plus)g and learning from historical data. But these algorithmic decisions may be unfair: in learning from historical data, they may replicate discriminatory practices from the past. In this paper, we propose two algorithms that adjust fitted ML predictors to produce decisions that are fair. Our methods provide post-hoc adjustments to the predictors, without requiring that they be retrained. We consider a causal model of the ML decisions, define fairness through counterfactual decisions within the model, and then form algorithmic decisions that capture the historical data as well as possible, but are provably fair. In particular, we consider two definitions of fairness. The first is “equal counterfactual opportunity,” where the counterfactual distribution of the decision is the same regardless of the protected attribute; the second is counterfactual fairness. We evaluate the algorithms, and the trade-o � between accuracy and fairness, on datasets about admissions, income, credit, and recidivism.
2023-01-01
Trans. Mach. Learn. Res. (publié)
openreview.net
openreview.net
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 ).
2023-01-01
arXiv.org (prépublication)
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 Mokayede
Christine Mwase
Wangui Kimotho
Foutse Yuehgoh
Anuoluwapo Aremu
Jessica Ojo
Shamsuddeen Hassan Muhammad
Salomey Osei … (voir 37 de plus)
Abdul-Hakeem Omotayo
Chiamaka 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 A. Etori
Millicent A. Ochieng
Clemencia Siro
Samuel Njoroge
Eric Muchiri
Wangari Kimotho
Lyse Naomi Wamba Momo
Daud Abolade
Simbiat Ajao
Tosin P. Adewumi
Iyanuoluwa Shode
Ricky Macharm
Ruqayya Nasir Iro
Saheed Salahudeen Abdullahi
Stephen E. Moore
Bernard Opoku
Zainab Akinjobi
Abeeb Afolabi
Nnaemeka Casmir Obiefuna
Onyekachi Ogbu
Sam Brian
V. 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 ).
2023-01-01
arXiv.org (preprint)
doi.org
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
2023-01-01
EMNLP (publié)
doi.org
openreview.net
AI Agents Learn to Trust
Ardavan S. Nobandegani
Irina Rish
T. Shultz
2023-01-01
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
2023-01-01
Sci. Comput. Program. (publié)
doi.org
arxiv.org