Publications

Unsupervised Discovery of Steerable Factors When Graph Deep Generative Models Are Entangled

Shengchao Liu

Chengpeng Wang

Jiarui Lu

Weili Nie

Hanchen Wang

Zhuoxinran Li

Bolei Zhou

Jian Tang

2024-01-30

TMLR (accepté)

openreview.net

Asymmetric stimulus representations bias visual perceptual learning

Pooya Laamerad

Asmara Awada

Christopher C. Pack

Shahab Bakhtiari

The primate visual cortex contains various regions that exhibit specialization for different stimulus properties, such as motion, shape, and… (voir plus) color. Within each region there is often further specialization, such that particular stimulus features, such as horizontal and vertical orientations, are overrepresented. These asymmetries are associated with well-known perceptual biases, but little is known about how they influence visual learning. Most theories would predict that learning is optimal, in the sense that it is unaffected by these asymmetries. But other approaches to learning would result in specific patterns of perceptual biases. To distinguish between these possibilities, we trained human observers to discriminate between expanding and contracting motion patterns, which have a highly asymmetrical representation in visual cortex. Observers exhibited biased percepts of these stimuli, and these biases were affected by training in ways that were often suboptimal. We simulated different neural network models and found that a learning rule that involved only adjustments to decision criteria, rather than connection weights, could account for our data. These results suggest that cortical asymmetries influence visual perception and that human observers often rely on suboptimal strategies for learning.

2024-01-29

Journal of Vision (publié)

doi.org

A comparison of RL-based and PID controllers for 6-DOF swimming robots: hybrid underwater object tracking

Faraz Lotfi

Khalil Virji

Nicholas Dudek

Gregory Dudek

2024-01-29

ArXiv (prépublication)

doi.org

arxiv.org

Trait‐matching models predict pairwise interactions across regions, not food web properties

Dominique Caron

Ulrich Brose

Miguel Lurgi

F. Guillaume Blanchet

Dominique Gravel

Laura J. Pollock

2024-01-29

Global Ecology and Biogeography (publié)

doi.org

Efficient Data-Driven MPC for Demand Response of Commercial Buildings

Marie-Christine Par'e

Vasken Dermardiros

Antoine Lesage-Landry

Model predictive control (MPC) has been shown to significantly improve the energy efficiency of buildings while maintaining thermal comfort.… (voir plus) Data-driven approaches based on neural networks have been proposed to facilitate system modelling. However, such approaches are generally nonconvex and result in computationally intractable optimization problems. In this work, we design a readily implementable energy management method for small commercial buildings. We then leverage our approach to formulate a real-time demand bidding strategy. We propose a data-driven and mixed-integer convex MPC which is solved via derivative-free optimization given a limited computational time of 5 minutes to respect operational constraints. We consider rooftop unit heating, ventilation, and air conditioning systems with discrete controls to accurately model the operation of most commercial buildings. Our approach uses an input convex recurrent neural network to model the thermal dynamics. We apply our approach in several demand response (DR) settings, including a demand bidding, a time-of-use, and a critical peak rebate program. Controller performance is evaluated on a state-of-the-art building simulation. The proposed approach improves thermal comfort while reducing energy consumption and cost through DR participation, when compared to other data-driven approaches or a set-point controller.

2024-01-28

ArXiv (prépublication)

doi.org

arxiv.org

Graphylo: A deep learning approach for predicting regulatory DNA and RNA sites from whole-genome multiple alignments

Dongjoon Lim

Changhyun Baek

Mathieu Blanchette

2024-01-26

iScience (publié)

doi.org

MiRGraph: A transformer-based feature learning approach to identify microRNA-target interactions by integrating heterogeneous graph network and sequence information

Pei Liu

Ying Liu

Jiawei Luo

Yue Li

MicroRNAs (miRNAs) play a crucial role in the regulation of gene expression by targeting specific mRNAs. They can function as both tumor sup… (voir plus)pressors and oncogenes depending on the specific miRNA and its target genes. Detecting miRNA-target interactions (MTIs) is critical for unraveling the complex mechanisms of gene regulation and identifying therapeutic targets and diagnostic markers. There is currently a lack of MTIs prediction method that simultaneously performs feature learning on heterogeneous graph network and sequence information. To improve the prediction performance of MTIs, we present a novel transformer-based multi-view feature learning method, named MiRGraph. It consists of two main modules for learning the sequence and heterogeneous graph network, respectively. For learning the sequence-based feaature embedding, we utilize the mature miRNA sequence and the complete 3’UTR sequence of the target mRNAs to encode sequence features. Specifically, a transformer-based CNN (TransCNN) module is designed for miRNAs and genes respectively to extract their personalized sequence features. For learning the network-based feature embedding, we utilize a heterogeneous graph transformer (HGT) module to extract the relational and structural information in a heterogeneous graph consisting of miRNA-miRNA, gene-gene and miRNA-target interactions. We learn the TransCNN and HGT modules end-to-end by utilizing a feedforward network, which takes the combined embedded features of the miRNA-gene pair to predict MTIs. Comparisons with other existing MTIs prediction methods illustrates the superiority of MiRGraph under standard criteria. In a case study on breast cancer, we identified plausible target genes of an oncomir hsa-MiR-122-5p and plausible miRNAs that regulate the oncogene BRCA1.

2024-01-26

bioRxiv (prépublication)

doi.org

Human-Centered AI

Catherine Régis

Jean-Louis Denis

Maria Luciana Axente

Atsuo Kishimoto

2024-01-25

(publié)

doi.org

Neighbor-Aware Calibration of Segmentation Networks with Penalty-Based Constraints

Balamurali Murugesan

Sukesh Adiga Vasudeva

Bingyuan Liu

Hervé Lombaert

Ismail Ben Ayed

Jose Dolz

Ensuring reliable confidence scores from deep neural networks is of paramount significance in critical decision-making systems, particularly… (voir plus) in real-world domains such as healthcare. Recent literature on calibrating deep segmentation networks has resulted in substantial progress. Nevertheless, these approaches are strongly inspired by the advancements in classification tasks, and thus their uncertainty is usually modeled by leveraging the information of individual pixels, disregarding the local structure of the object of interest. Indeed, only the recent Spatially Varying Label Smoothing (SVLS) approach considers pixel spatial relationships across classes, by softening the pixel label assignments with a discrete spatial Gaussian kernel. In this work, we first present a constrained optimization perspective of SVLS and demonstrate that it enforces an implicit constraint on soft class proportions of surrounding pixels. Furthermore, our analysis shows that SVLS lacks a mechanism to balance the contribution of the constraint with the primary objective, potentially hindering the optimization process. Based on these observations, we propose NACL (Neighbor Aware CaLibration), a principled and simple solution based on equality constraints on the logit values, which enables to control explicitly both the enforced constraint and the weight of the penalty, offering more flexibility. Comprehensive experiments on a wide variety of well-known segmentation benchmarks demonstrate the superior calibration performance of the proposed approach, without affecting its discriminative power. Furthermore, ablation studies empirically show the model agnostic nature of our approach, which can be used to train a wide span of deep segmentation networks.

2024-01-25

ArXiv (prépublication)

doi.org

arxiv.org

Beyond Predictive Algorithms in Child Welfare

Erina Seh-Young Moon

Erin Moon

Devansh Saxena

Tegan Maharaj

Shion Guha

2024-01-23

graphicsinterface.org/Graphics_Interface/2024/Conference (publié)