Publications

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 … (see 4 more)
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… (see more) 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.
2023-12-31
International Conference on Language Resources and Evaluation (published)
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
2023-12-31
RLC (published)
doi.org
openreview.net
Model-based graph reinforcement learning for inductive traffic signal control
François-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 … (see more)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.
2023-12-31
IEEE Open Journal of Intelligent Transportation Systems (published)
doi.org
arxiv.org
A Model-Based Solution to the Offline Multi-Agent Reinforcement Learning Coordination Problem
Paul Barde
Jakob Foerster
Derek Nowrouzezahrai
Amy Zhang
Training multiple agents to coordinate is an essential problem with applications in robotics, game theory, economics, and social sciences. H… (see more)owever, most existing Multi-Agent Reinforcement Learning (MARL) methods are online and thus impractical for real-world applications in which collecting new interactions is costly or dangerous. While these algorithms should leverage offline data when available, doing so gives rise to what we call the offline coordination problem. Specifically, we identify and formalize the strategy agreement (SA) and the strategy fine-tuning (SFT) coordination challenges, two issues at which current offline MARL algorithms fail. Concretely, we reveal that the prevalent model-free methods are severely deficient and cannot handle coordination-intensive offline multi-agent tasks in either toy or MuJoCo domains. To address this setback, we emphasize the importance of inter-agent interactions and propose the very first model-based offline MARL method. Our resulting algorithm, Model-based Offline Multi-Agent Proximal Policy Optimization (MOMA-PPO) generates synthetic interaction data and enables agents to converge on a strategy while fine-tuning their policies accordingly. This simple model-based solution solves the coordination-intensive offline tasks, significantly outperforming the prevalent model-free methods even under severe partial observability and with learned world models.
2023-12-31
AAMAS (published)
doi.org
arxiv.org
Motivating Users to Attend to Privacy: A Theory-Driven Design Study
Varun Shiri
Maggie Xiong
Jinghui Cheng
Jin L.C. Guo
In modern technology environments, raising users’ privacy awareness is crucial. Existing efforts largely focused on privacy policy present… (see more)ation and failed to systematically address a radical challenge of user motivation for initiating privacy awareness. Leveraging the Protection Motivation Theory (PMT), we proposed design ideas and categories dedicated to motivating users to engage with privacy-related information. Using these design ideas, we created a conceptual prototype, enhancing the current App Store product page. Results from an online experiment and follow-up interviews showed that our design effectively motivated participants to attend to privacy issues, raising both the threat appraisal and coping appraisal, two main factors in PMT. Our work indicated that effective design should consider combining PMT components, calibrating information content, and integrating other design elements, such as visual cues and user familiarity. Overall, our study contributes valuable design considerations driven by the PMT to amplify the motivational aspect of privacy communication.
2023-12-31
Conference on Designing Interactive Systems (published)
doi.org
arxiv.org
Multidomain Object Detection Framework Using Feature Domain Knowledge Distillation.
Da-Wei Jaw
Shih-Chia Huang
Zhihui Lu
Benjamin C. M. Fung
Sy-Yen Kuo
Object detection techniques have been widely studied, utilized in various works, and have exhibited robust performance on images with suffic… (see more)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.
2023-12-31
IEEE Trans. Cybern. (published)
doi.org
Multilayer meta-matching: Translating phenotypic prediction models from multiple datasets to small data
Pansheng Chen
Lijun An
Naren Wulan
Chen Zhang
Shaoshi Zhang
Leon Qi Rong Ooi
Ru Kong
Jianzhong Chen
Jianxiao Wu
Sidhant Chopra
Danilo Bzdok
Simon B. Eickhoff
Avram J. Holmes
B.T. Thomas Yeo
Resting-state functional connectivity (RSFC) is widely used to predict phenotypic traits in individuals. Large sample sizes can significantl… (see more)y improve prediction accuracies. However, for studies of certain clinical populations or focused neuroscience inquiries, small-scale datasets often remain a necessity. We have previously proposed a “meta-matching” approach to translate prediction models from large datasets to predict new phenotypes in small datasets. We demonstrated a large improvement over classical kernel ridge regression (KRR) when translating models from a single source dataset (UK Biobank) to the Human Connectome Project Young Adults (HCP-YA) dataset. In the current study, we propose two meta-matching variants (“meta-matching with dataset stacking” and “multilayer meta-matching”) to translate models from multiple source datasets across disparate sample sizes to predict new phenotypes in small target datasets. We evaluate both approaches by translating models trained from five source datasets (with sample sizes ranging from 862 participants to 36,834 participants) to predict phenotypes in the HCP-YA and HCP-Aging datasets. We find that multilayer meta-matching modestly outperforms meta-matching with dataset stacking. Both meta-matching variants perform better than the original “meta-matching with stacking” approach trained only on the UK Biobank. All meta-matching variants outperform classical KRR and transfer learning by a large margin. In fact, KRR is better than classical transfer learning when less than 50 participants are available for finetuning, suggesting the difficulty of classical transfer learning in the very small sample regime. The multilayer meta-matching model is publicly available athttps://github.com/ThomasYeoLab/Meta_matching_models/tree/main/rs-fMRI/v2.0.
2023-12-31
Imaging Neuroscience (published)
doi.org
Multi-objective PSO semi-supervised random forest method for dioxin soft sensor
Wen Xu
Jian Tang
Heng Xia
Wen Yu
JunFei Qiao
2023-12-31
Eng. Appl. Artif. Intell. (published)
doi.org
Multi-reservoir ESN-based prediction strategy for dynamic multi-objective optimization
Cuili Yang
Danlei Wang
Jian Tang
JunFei Qiao
Wen Yu
2023-12-31
Information Sciences (published)
doi.org
MVP: Minimal Viable Phrase for Long Text Understanding.
Louis Clouatre
Amal Zouaq
A. Chandar
2023-12-31
International Conference on Language Resources and Evaluation (published)
dblp.uni-trier.de
Nearest Neighbour Score Estimators for Diffusion Generative Models
Matthew Niedoba
Dylan Green
Saeid Naderiparizi
Vasileios Lioutas
Jonathan Wilder Lavington
Xiaoxuan Liang
Yunpeng Liu
Ke Zhang
Setareh Dabiri
Adam Ścibior
Berend Zwartsenberg
Frank N. Wood
Score function estimation is the cornerstone of both training and sampling from diffusion generative models. Despite this fact, the most com… (see more)monly used estimators are either biased neural network approximations or high variance Monte Carlo estimators based on the conditional score. We introduce a novel nearest neighbour score function estimator which utilizes multiple samples from the training set to dramatically decrease estimator variance. We leverage our low variance estimator in two compelling applications. Training consistency models with our estimator, we report a significant increase in both convergence speed and sample quality. In diffusion models, we show that our estimator can replace a learned network for probability-flow ODE integration, opening promising new avenues of future research. Code will be released upon paper acceptance.
2023-12-31
ICML (published)
doi.org
proceedings.mlr.press
Neural Implicit Reduced Fluid Simulation
Yuanyuan Tao
Ivan Puhachov
Derek Nowrouzezahrai
Paul Kry
High-fidelity simulation of fluid dynamics is challenging because of the high dimensional state data needed to capture fine details and the … (see more)large computational cost associated with advancing the system in time. We present neural implicit reduced fluid simulation (NIRFS), a reduced fluid simulation technique that combines an implicit neural representation of fluid shapes and a neural ordinary differential equation to model the dynamics of fluid in the reduced latent space. The latent trajectories are computed at very little cost in comparison to simulations for training, while preserving fine physical details. We show that this approach can work well, capturing the shapes and dynamics involved in a variety of scenarios with constrained initial conditions, e.g., droplet-droplet collisions, crown splashes, and fluid slosh in a container. In each scenario, we learn the latent implicit representation of fluid shapes with a deep-network signed distance function, as well as the energy function and parameters of a damped Hamiltonian system, which helps guarantee desirable properties of the latent dynamics. To ensure that latent shape representations form smooth and physically meaningful trajectories, we simultaneously learn the latent representation and dynamics. We evaluate novel simulations for conservation of volume and momentum conservation, discuss design decisions, and demonstrate an application of our method to fluid control.
2023-12-31
SIGGRAPH Asia (published)
doi.org