Publications

List Comprehension Versus for Loops Performance in Real Python Projects: Should we Care?

Cyrine Zid

François Belias

Massimiliano Di Penta

Giuliano Antoniol

List comprehensions are a Pythonic functional construct allowing developers to express in a concise way loops to build and manipulate lists.… (voir plus) Previous studies point to a gain in speed when list comprehensions are adopted. This paper reports the results of a study that compares the execution time performance of Python code written using list comprehensions as opposed to equivalent imperative programming. To this aim, we have developed a set of transformation rules to map Python for loops into list comprehensions. On the one hand, on artificial code snippets, we found list comprehensions to be faster than procedural code, with differences becoming evident if amplifying the tests, i.e., executing the code fragment thousands of times. On the other hand, this does not happen when executing real-world Python projects, where the performance may or may not improve, depending on the projects' features and the nature of the manipulated objects.

2024-01-01

SANER (publié)

doi.org

Machine Translation Hallucination Detection for Low and High Resource Languages using Large Language Models

Laura Gongas

Kenza Benkirane

Shahar Pelles

Naomi Fuchs

Joshua Darmon

Pontus Stenetorp

David Ifeoluwa Adelani

Eduardo Sánchez

Meta

2024-01-01

EMNLP (Findings) (publié)

doi.org

openreview.net

Maximum entropy GFlowNets with soft Q-learning

Sobhan Mohammadpour

Emmanuel Bengio

Emma Frejinger

Pierre-Luc Bacon

2024-01-01

AISTATS (publié)

doi.org

arxiv.org

Minimax Exploiter: A Data Efficient Approach for Competitive Self-Play

Daniel Bairamian

Philippe Marcotte

Joshua Romoff

Gabriel Robert

Derek Nowrouzezahrai

2024-01-01

AAMAS (publié)

doi.org

arxiv.org

Mirror Descent Algorithms with Nearly Dimension-Independent Rates for Differentially-Private Stochastic Saddle-Point Problems

Tom'as Gonz'alez

Crist'obal Guzm'an

Courtney Paquette

2024-01-01

COLT (publié)

doi.org

arxiv.org

Mitigating Translationese in Low-resource Languages: The Storyboard Approach

Garry Kuwanto

Eno-Abasi Urua

Priscilla A. Amuok

Shamsuddeen Hassan Muhammad

Aremu Anuoluwapo

Verrah Akinyi Otiende

Loice Emma Nanyanga

T. Nyoike

A. D. Akpan

Nsima Ab Udouboh

Idongesit Udeme Archibong

Idara Effiong Moses

Ifeoluwatayo A. Ige

Benjamin A. Ajibade

Olumide Benjamin Awokoya

Idris Abdulmumin

Saminu Mohammad Aliyu

Ruqayya Nasir Iro

Ibrahim Ahmad

Deontae Smith … (voir 4 de plus)

Praise-EL Michaels

David Ifeoluwa Adelani

Derry Tanti Wijaya

Anietie U Andy

Low-resource languages often face challenges in acquiring high-quality language data due to the reliance on translation-based methods, which… (voir plus) can introduce the translationese effect. This phenomenon results in translated sentences that lack fluency and naturalness in the target language. In this paper, we propose a novel approach for data collection by leveraging storyboards to elicit more fluent and natural sentences. Our method involves presenting native speakers with visual stimuli in the form of storyboards and collecting their descriptions without direct exposure to the source text. We conducted a comprehensive evaluation comparing our storyboard-based approach with traditional text translation-based methods in terms of accuracy and fluency. Human annotators and quantitative metrics were used to assess translation quality. The results indicate a preference for text translation in terms of accuracy, while our method demonstrates worse accuracy but better fluency in the language focused.

2024-01-01

International Conference on Language Resources and Evaluation (publié)

doi.org

arxiv.org

Mixture of Experts in a Mixture of RL settings

Timon Willi

Johan Samir Obando Ceron

Jakob Nicolaus Foerster

Gintare Karolina Dziugaite

Pablo Samuel Castro

2024-01-01

RLC (publié)

doi.org

openreview.net

Model-based graph reinforcement learning for inductive traffic signal control

FranÃ§ois-Xavier Devailly

Denis Larocque

Laurent Charlin

Most reinforcement learning methods for adaptive-traffic-signal-control require training from scratch to be applied on any new intersection … (voir plus)or after any modification to the road network, traffic distribution, or behavioral constraints experienced during training. Considering 1) the massive amount of experience required to train such methods, and 2) that experience must be gathered by interacting in an exploratory fashion with real road-network-users, such a lack of transferability limits experimentation and applicability. Recent approaches enable learning policies that generalize for unseen road-network topologies and traffic distributions, partially tackling this challenge. However, the literature remains divided between the learning of cyclic (the evolution of connectivity at an intersection must respect a cycle) and acyclic (less constrained) policies, and these transferable methods 1) are only compatible with cyclic constraints and 2) do not enable coordination. We introduce a new model-based method, MuJAM, which, on top of enabling explicit coordination at scale for the first time, pushes generalization further by allowing a generalization to the controllers' constraints. In a zero-shot transfer setting involving both road networks and traffic settings never experienced during training, and in a larger transfer experiment involving the control of 3,971 traffic signal controllers in Manhattan, we show that MuJAM, using both cyclic and acyclic constraints, outperforms domain-specific baselines as well as another transferable approach.

2024-01-01

IEEE Open Journal of Intelligent Transportation Systems (publié)

doi.org

arxiv.org

A Model-Based Solution to the Offline Multi-Agent Reinforcement Learning Coordination Problem

Paul Barde

Jakob Nicolaus Foerster

Derek Nowrouzezahrai

Amy Zhang

2024-01-01

AAMAS (publié)

doi.org

arxiv.org

Multidomain Object Detection Framework Using Feature Domain Knowledge Distillation.

Da-Wei Jaw

Shih-Chia Huang

Zhihui Lu

Benjamin Fung

Sy-Yen Kuo

Object detection techniques have been widely studied, utilized in various works, and have exhibited robust performance on images with suffic… (voir plus)ient luminance. However, these approaches typically struggle to extract valuable features from low-luminance images, which often exhibit blurriness and dim appearence, leading to detection failures. To overcome this issue, we introduce an innovative unsupervised feature domain knowledge distillation (KD) framework. The proposed framework enhances the generalization capability of neural networks across both low-and high-luminance domains without incurring additional computational costs during testing. This improvement is made possible through the integration of generative adversarial networks and our proposed unsupervised KD process. Furthermore, we introduce a region-based multiscale discriminator designed to discern feature domain discrepancies at the object level rather than from the global context. This bolsters the joint learning process of object detection and feature domain distillation tasks. Both qualitative and quantitative assessments shown that the proposed method, empowered by the region-based multiscale discriminator and the unsupervised feature domain distillation process, can effectively extract beneficial features from low-luminance images, outperforming other state-of-the-art approaches in both low-and sufficient-luminance domains.

2024-01-01

IEEE Trans. Cybern. (publié)

doi.org

MVP: Minimal Viable Phrase for Long Text Understanding.

Louis Clouâtre

Amal Zouaq

Sarath Chandar

2024-01-01

International Conference on Language Resources and Evaluation (publié)

dblp.uni-trier.de

Neural Semantic Surface Maps

Luca Morreale