Publications

A Distributed ADMM-based Deep Learning Approach for Thermal Control in Multi-Zone Buildings
Vincent Taboga
Hanane Dagdougui
The surge in electricity use, coupled with the dependency on intermittent renewable energy sources, poses significant hurdles to effectively… (see more) managing power grids, particularly during times of peak demand. Demand Response programs and energy conservation measures are essential to operate energy grids while ensuring a responsible use of our resources This research combines distributed optimization using ADMM with Deep Learning models to plan indoor temperature setpoints effectively. A two-layer hierarchical structure is used, with a central building coordinator at the upper layer and local controllers at the thermal zone layer. The coordinator must limit the building's maximum power by translating the building's total power to local power targets for each zone. Local controllers can modify the temperature setpoints to meet the local power targets. The resulting control algorithm, called Distributed Planning Networks, is designed to be both adaptable and scalable to many types of buildings, tackling two of the main challenges in the development of such systems. The proposed approach is tested on an 18-zone building modeled in EnergyPlus. The algorithm successfully manages Demand Response peak events.
2023-12-08
ArXiv (preprint)
doi.org
arxiv.org
Filtering Pixel Latent Variables for Unmixing Noisy and Undersampled Volumetric Images
Catherine Bouchard
Andréanne Deschênes
Vincent Boulanger
Jean-Michel Bellavance
Flavie Lavoie-Cardinal
Christian Gagné
2023-12-08
ArXiv (preprint)
arxiv.org
arxiv.org
Harnessing Predictive Modeling and Software Analytics in the Age of LLM-Powered Software Development (Invited Talk)
Foutse Khomh
2023-12-08
International Conference on Predictive Models in Software Engineering (published)
doi.org
Pretrainable Geometric Graph Neural Network for Antibody Affinity Maturation
Huiyu Cai
Zuobai Zhang
Mingkai Wang
Bozitao Zhong
Yanling Wu
Tianlei Ying
Jian Tang
In the realm of antibody therapeutics development, increasing the binding affinity of an antibody to its target antigen is a crucial task. T… (see more)his paper presents GearBind, a pretrainable deep neural network designed to be effective for in silico affinity maturation. Leveraging multi-level geometric message passing alongside contrastive pretraining on protein structural data, GearBind capably models the complex interplay of atom-level interactions within protein complexes, surpassing previous state-of-the-art approaches on SKEMPI v2 in terms of Pearson correlation, mean absolute error (MAE) and root mean square error (RMSE). In silico experiments elucidate that pretraining helps GearBind become sensitive to mutation-induced binding affinity changes and reflective of amino acid substitution tendency. Using an ensemble model based on pretrained GearBind, we successfully optimize the affinity of CR3022 to the spike (S) protein of the SARS-CoV-2 Omicron strain. Our strategy yields a high success rate with up to 17-fold affinity increase. GearBind proves to be an effective tool in narrowing the search space for in vitro antibody affinity maturation, underscoring the utility of geometric deep learning and adept pre-training in macromolecule interaction modeling.
2023-12-07
bioRxiv (preprint)
doi.org
Pretrainable Geometric Graph Neural Network for Antibody Affinity Maturation
Huiyu Cai
Zuobai Zhang
Mingkai Wang
Bozitao Zhong
Yanling Wu
Tianlei Ying
Jian Tang
2023-12-07
bioRxiv (preprint)
doi.org
Anomaly Detection for Scalable Task Grouping in Reinforcement Learning-based RAN Optimization
Jimmy Li
Igor Kozlov
Di Wu
Xue (Steve) Liu
Gregory Dudek
The use of learning-based methods for optimizing cellular radio access networks (RAN) has received increasing attention in recent years. Thi… (see more)s coincides with a rapid increase in the number of cell sites worldwide, driven largely by dramatic growth in cellular network traffic. Training and maintaining learned models that work well across a large number of cell sites has thus become a pertinent problem. This paper proposes a scalable framework for constructing a reinforcement learning policy bank that can perform RAN optimization across a large number of cell sites with varying traffic patterns. Central to our framework is a novel application of anomaly detection techniques to assess the compatibility between sites (tasks) and the policy bank. This allows our framework to intelligently identify when a policy can be reused for a task, and when a new policy needs to be trained and added to the policy bank. Our results show that our approach to compatibility assessment leads to an efficient use of computational resources, by allowing us to construct a performant policy bank without exhaustively training on all tasks, which makes it applicable under real-world constraints.
2023-12-06
ArXiv (preprint)
doi.org
arxiv.org
Parameter-Efficient Transfer Learning of Audio Spectrogram Transformers
Umberto Cappellazzo
Daniele Falavigna
Alessio Brutti
Mirco Ravanelli
The common modus operandi of fine-tuning large pre-trained Transformer models entails the adaptation of all their parameters (i.e., full fin… (see more)e-tuning). While achieving striking results on multiple tasks, this approach becomes unfeasible as the model size and the number of downstream tasks increase. In natural language processing and computer vision, parameter-efficient approaches like prompt-tuning and adapters have emerged as solid alternatives by fine-tuning only a small number of extra parameters, without sacrificing performance accuracy. For audio classification tasks, the Audio Spectrogram Transformer model shows impressive results. However, surprisingly, how to efficiently adapt it to several downstream tasks has not been tackled before. In this paper, we bridge this gap and present a detailed investigation of common parameter-efficient methods, revealing that adapters and LoRA consistently outperform the other methods across four benchmarks. Whereas adapters prove to be more efficient in few-shot learning settings, LoRA turns out to scale better as we increase the number of learnable parameters. We finally carry out ablation studies to find the best configuration for adapters and LoRA.
2023-12-06
ArXiv (preprint)
arxiv.org
arxiv.org
Bug characterization in machine learning-based systems
Mohammad Mehdi Morovati
Amin Nikanjam
Florian Tambon
Foutse Khomh
Z. Jiang
2023-12-05
Empirical Software Engineering (published)
doi.org
arxiv.org
Deep Neural Networks pruning via the Structured Perspective Regularization
Matteo Cacciola
Antonio Frangioni
Xinlin Li
Andrea Lodi
2023-12-05
SIAM Journal on Mathematics of Data Science (published)
doi.org
arxiv.org
Towards Causal Representations of Climate Model Data
Julien Boussard
Chandni Nagda
Julia Kaltenborn
Charlotte Emilie Elektra Lange
Philippe Brouillard
Yaniv Gurwicz
Peer Nowack
David Rolnick
Climate models, such as Earth system models (ESMs), are crucial for simulating future climate change based on projected Shared Socioeconomic… (see more) Pathways (SSP) greenhouse gas emissions scenarios. While ESMs are sophisticated and invaluable, machine learning-based emulators trained on existing simulation data can project additional climate scenarios much faster and are computationally efficient. However, they often lack generalizability and interpretability. This work delves into the potential of causal representation learning, specifically the \emph{Causal Discovery with Single-parent Decoding} (CDSD) method, which could render climate model emulation efficient \textit{and} interpretable. We evaluate CDSD on multiple climate datasets, focusing on emissions, temperature, and precipitation. Our findings shed light on the challenges, limitations, and promise of using CDSD as a stepping stone towards more interpretable and robust climate model emulation.
2023-12-05
ArXiv (preprint)
doi.org
arxiv.org
AdaTeacher: Adaptive Multi-Teacher Weighting for Communication Load Forecasting
Chengming Hu
Ju Wang
Di Wu
Yan Xin
Charlie Zhang
Xue (Steve) Liu
Gregory Dudek
To deal with notorious delays in communication systems, it is crucial to forecast key system characteristics, such as the communication load… (see more). Most existing studies aggregate data from multiple edge nodes for improving the forecasting accuracy. However, the bandwidth cost of such data aggregation could be unacceptably high from the perspective of system operators. To achieve both the high forecasting accuracy and bandwidth efficiency, this paper proposes an Adaptive Multi-Teacher Weighting in Teacher-Student Learning approach, namely AdaTeacher, for communication load forecasting of multiple edge nodes. Each edge node trains a local model on its own data. A target node collects multiple models from its neighbor nodes and treats these models as teachers. Then, the target node trains a student model from teachers via Teacher-Student (T-S) learning. Unlike most existing T-S learning approaches that treat teachers evenly, resulting in a limited performance, AdaTeacher introduces a bilevel optimization algorithm to dynamically learn an importance weight for each teacher toward a more effective and accurate T-S learning process. Compared to the state-of-the-art methods, Ada Teacher not only reduces the bandwidth cost by 53.85%, but also improves the load forecasting accuracy by 21.56% and 24.24% on two real-world datasets.
2023-12-04
Global Communications Conference (published)
doi.org
Energy Saving in Cellular Wireless Networks via Transfer Deep Reinforcement Learning
Di Wu
Yi Tian Xu
M. Jenkin
Seowoo Jang
Ekram Hossain
Xue (Steve) Liu
Gregory Dudek
With the increasing use of data-intensive mobile applications and the number of mobile users, the demand for wireless data services has been… (see more) increasing exponentially in recent years. In order to address this demand, a large number of new cellular base stations are being deployed around the world, leading to a significant increase in energy consumption and greenhouse gas emission. Consequently, energy consumption has emerged as a key concern in the fifth-generation (5G) network era and beyond. Reinforcement learning (RL), which aims to learn a control policy via interacting with the environment, has been shown to be effective in addressing network optimization problems. However, for reinforcement learning, especially deep reinforcement learning, a large number of interactions with the environment are required. This often limits its applicability in the real world. In this work, to better deal with dynamic traffic scenarios and improve real-world applicability, we propose a transfer deep reinforcement learning framework for energy optimization in cellular communication networks. Specifically, we first pre-train a set of RL-based energy-saving policies on source base stations and then transfer the most suitable policy to the given target base station in an unsupervised learning manner. Experimental results demonstrate that base station energy consumption can be reduced significantly using this approach.
2023-12-04
Global Communications Conference (published)
doi.org