Publications

Optimizing Energy Saving for Wireless Networks Via Offline Decision Transformer
Yi Tian Xu
Di Wu
M. Jenkin
Seowoo Jang
Xue Liu
Gregory Dudek
With the global aim of reducing carbon emissions, energy saving for communication systems has gained tremendous attention. Efficient energy-… (voir plus)saving solutions are not only required to accommodate the fast growth in communication demand but solutions are also challenged by the complex nature of the load dynamics. Recent reinforcement learning (RL)-based methods have shown promising performance for network optimization problems, such as base station energy saving. However, a major limitation of these methods is the requirement of online exploration of potential solutions using a high-fidelity simulator or the need to perform exploration in a real-world environment. We circumvent this issue by proposing an offline reinforcement learning energy saving (ORES) framework that allows us to learn an efficient control policy using previously collected data. We first deploy a behavior energy-saving policy on base stations and generate a set of interaction experiences. Then, using a robust deep offline reinforcement learning algorithm, we learn an energy-saving control policy based on the collected experiences. Results from experiments conducted on a diverse collection of communication scenarios with different behavior policies showcase the effectiveness of the proposed energy-saving algorithms.
2024-06-08
ICC 2024 - IEEE International Conference on Communications (publié)
doi.org
PEOPLEx: PEdestrian Opportunistic Positioning LEveraging IMU, UWB, BLE and WiFi
Pierre-Yves Lajoie
Bobak H. Baghi
Sachini Herath
Francois Hogan
Xue Liu
Gregory Dudek
This paper advances the field of pedestrian localization by introducing a unifying framework for opportunistic positioning based on nonlinea… (voir plus)r factor graph optimization. While many existing approaches assume constant availability of one or multiple sensing signals, our methodology employs IMU-based pedestrian inertial navigation as the backbone for sensor fusion, opportunistically integrating Ultra-Wideband (UWB), Bluetooth Low Energy (BLE), and WiFi signals when they are available in the environment. The proposed PEOPLEx framework is designed to incorporate sensing data as it becomes available, operating without any prior knowledge about the environment (e.g. anchor locations, radio frequency maps, etc.). Our contributions are twofold: 1) we introduce an opportunistic multi-sensor and real-time pedestrian positioning framework fusing the available sensor measurements; 2) we develop novel factors for adaptive scaling and coarse loop closures, significantly improving the precision of indoor positioning. Experimental validation confirms that our approach achieves accurate localization estimates in real indoor scenarios using commercial smartphones.
2024-06-08
ICC 2024 - IEEE International Conference on Communications (publié)
doi.org
arxiv.org
Probabilistic Mobility Load Balancing for Multi-band 5G and Beyond Networks
Saria Al Lahham
Di Wu
Ekram Hossain
Xue Liu
Gregory Dudek
2024-06-08
2024 IEEE International Conference on Communications Workshops (ICC Workshops) (publié)
doi.org
arxiv.org
Promoting Exploration in Memory-Augmented Adam using Critical Momenta
Pranshu Malviya
Goncalo Mordido
Aristide Baratin
Reza Babanezhad Harikandeh
Jerry Huang
Simon Lacoste-Julien
Razvan Pascanu
Sarath Chandar
Adaptive gradient-based optimizers, notably Adam, have left their mark in training large-scale deep learning models, offering fast convergen… (voir plus)ce and robustness to hyperparameter settings. However, they often struggle with generalization, attributed to their tendency to converge to sharp minima in the loss landscape. To address this, we propose a new memory-augmented version of Adam that encourages exploration towards flatter minima by incorporating a buffer of critical momentum terms during training. This buffer prompts the optimizer to overshoot beyond narrow minima, promoting exploration. Through comprehensive analysis in simple settings, we illustrate the efficacy of our approach in increasing exploration and bias towards flatter minima. We empirically demonstrate that it can improve model performance for image classification on ImageNet and CIFAR10/100, language modelling on Penn Treebank, and online learning tasks on TinyImageNet and 5-dataset. Our code is available at https://github.com/chandar-lab/CMOptimizer.
2024-06-08
Transactions on Machine Learning Research (accepté)
doi.org
openreview.net
Tensor-based Space Debris Detection for Satellite Mega-constellations
Olivier Daoust
Hasan Nayir
Irfan Azam
Antoine Lesage-Landry
Gunes Karabulut Kurt
2024-06-08
2024 IEEE International Conference on Communications Workshops (ICC Workshops) (publié)
doi.org
arxiv.org
Why Don't Prompt-Based Fairness Metrics Correlate?
Abdelrahman Zayed
Goncalo Mordido
Ioana Baldini
A. Chandar
The widespread use of large language models has brought up essential questions about the potential biases these models might learn. This led… (voir plus) to the development of several metrics aimed at evaluating and mitigating these biases. In this paper, we first demonstrate that prompt-based fairness metrics exhibit poor agreement, as measured by correlation, raising important questions about the reliability of fairness assessment using prompts. Then, we outline six relevant reasons why such a low correlation is observed across existing metrics. Based on these insights, we propose a method called Correlated Fairness Output (CAIRO) to enhance the correlation between fairness metrics. CAIRO augments the original prompts of a given fairness metric by using several pre-trained language models and then selects the combination of the augmented prompts that achieves the highest correlation across metrics. We show a significant improvement in Pearson correlation from 0.3 and 0.18 to 0.90 and 0.98 across metrics for gender and religion biases, respectively. Our code is available at https://github.com/chandar-lab/CAIRO.
2024-06-08
ArXiv (prépublication)
doi.org
arxiv.org
A Deep Dive into the Trade-Offs of Parameter-Efficient Preference Alignment Techniques
Megh Thakkar
Quentin Fournier
Matthew D Riemer
Pin-Yu Chen
Amal Zouaq
Payel Das
A. Chandar
2024-06-06
ArXiv (prépublication)
doi.org
arxiv.org
Lifelong Learning of Video Diffusion Models From a Single Video Stream
Jinsoo Yoo
Yingchen He
Saeid Naderiparizi
Dylan Green
Gido M van de Ven
Geoff Pleiss
Frank N. Wood
2024-06-06
ArXiv (prépublication)
openreview.net
openreview.net
Online Continual Learning of Video Diffusion Models From a Single Video Stream
Jinsoo Yoo
Yingchen He
Saeid Naderiparizi
Dylan Green
Gido M van de Ven
Geoff Pleiss
Frank N. Wood
2024-06-06
ArXiv (prépublication)
doi.org
arxiv.org
Recurrent Policies Are Not Enough for Continual Reinforcement Learning
Nathan Samuel de Lara
Veronica Chelu
Doina Precup
Continual Reinforcement Learning (CRL) aims to develop algorithms that adapt to non-stationary sequences of tasks. A promising recent approa… (voir plus)ch utilizes Recurrent Neural Networks (RNNs) to learn contextual Markov Decision Process (MDP) embeddings. This enables a reinforcement learning (RL) agent to discern the optimality of actions across diverse tasks. In this study, we examine two critical failure modes in the learning of these contextual MDP embeddings. Specifically, we find that RNNs are prone to catastrophic forgetting, manifesting in two distinct ways: (i) embedding collapse---where agents initially learn a contextual task structure that later collapses to a single task, and (ii) embedding drift---where learning embeddings for new MDPs interferes with embeddings the RNN outputs for previous MDPs in the sequence, leading to suboptimal performance of downstream policy networks conditioned on stale embeddings. We explore the effects of various objective functions and network architectures concerning these failure modes, revealing that one of these modes consistently emerges across different setups.
2024-06-06
rl-conference.cc/RLC/2024/Workshop/ICBINB (poster)
openreview.net
openreview.net
Early Detection of an Invasive Alien Plant (Phragmites australis) Using Unoccupied Aerial Vehicles and Artificial Intelligence
Antoine Caron-Guay
Mickaël Germain
Étienne Laliberté
The combination of unoccupied aerial vehicles (UAVs) and artificial intelligence to map vegetation represents a promising new approach to im… (voir plus)prove the detection of invasive alien plant species (IAPS). The high spatial resolution achievable with UAVs and recent innovations in computer vision, especially with convolutional neural networks, suggest that early detection of IAPS could be possible, thus facilitating their management. In this study, we evaluated the suitability of this approach for mapping the location of common reed (Phragmites australis subsp. australis) within a national park located in southern Quebec, Canada. We collected data on six distinct dates during the growing season, covering environments with different levels of reed invasion. Overall, model performance was high for the different dates and zones, especially for recall (mean of 0.89). The results showed an increase in performance, reaching a peak following the appearance of the inflorescence in September (highest F1-score at 0.98). Furthermore, a decrease in spatial resolution negatively affected recall (18% decrease between a spatial resolution of 0.15 cm pixel−1 and 1.50 cm pixel−1) but did not have a strong impact on precision (2% decrease). Despite challenges associated with common reed mapping in a post-treatment monitoring context, the use of UAVs and deep learning shows great potential for IAPS detection when supported by a suitable dataset. Our results show that, from an operational point of view, this approach could be an effective tool for speeding up the work of biologists in the field and ensuring better management of IAPS.
2024-06-05
bioRxiv (prépublication)
doi.org
Transformers need glasses! Information over-squashing in language tasks
Federico Barbero
Andrea Banino
Steven Kapturowski
Dharshan Kumaran
João Guilherme Madeira Araújo
Alex Vitvitskyi
Razvan Pascanu
Petar Veličković
We study how information propagates in decoder-only Transformers, which are the architectural backbone of most existing frontier large langu… (voir plus)age models (LLMs). We rely on a theoretical signal propagation analysis -- specifically, we analyse the representations of the last token in the final layer of the Transformer, as this is the representation used for next-token prediction. Our analysis reveals a representational collapse phenomenon: we prove that certain distinct sequences of inputs to the Transformer can yield arbitrarily close representations in the final token. This effect is exacerbated by the low-precision floating-point formats frequently used in modern LLMs. As a result, the model is provably unable to respond to these sequences in different ways -- leading to errors in, e.g., tasks involving counting or copying. Further, we show that decoder-only Transformer language models can lose sensitivity to specific tokens in the input, which relates to the well-known phenomenon of over-squashing in graph neural networks. We provide empirical evidence supporting our claims on contemporary LLMs. Our theory also points to simple solutions towards ameliorating these issues.
2024-06-05
ArXiv (prépublication)
doi.org
arxiv.org