Publications

Sketch-guided Cage-based 3D Gaussian Splatting Deformation
Tianhao Xie
Noam Aigerman
Eugene Belilovsky
Tiberiu Popa
3D Gaussian Splatting (GS) is one of the most promising novel 3D representations that has received great interest in computer graphics and c… (see more)omputer vision. While various systems have introduced editing capabilities for 3D GS, such as those guided by text prompts, fine-grained control over deformation remains an open challenge. In this work, we present a novel sketch-guided 3D GS deformation system that allows users to intuitively modify the geometry of a 3D GS model by drawing a silhouette sketch from a single viewpoint. Our approach introduces a new deformation method that combines cage-based deformations with a variant of Neural Jacobian Fields, enabling precise, fine-grained control. Additionally, it leverages large-scale 2D diffusion priors and ControlNet to ensure the generated deformations are semantically plausible. Through a series of experiments, we demonstrate the effectiveness of our method and showcase its ability to animate static 3D GS models as one of its key applications.
2024-11-19
ArXiv (preprint)
arxiv.org
arxiv.org
Towards Understanding the Impact of Data Bugs on Deep Learning Models in Software Engineering
Mehil B. Shah
Mohammad Masudur Rahman
Foutse Khomh
Deep learning (DL) techniques have achieved significant success in various software engineering tasks (e.g., code completion by Copilot). Ho… (see more)wever, DL systems are prone to bugs from many sources, including training data. Existing literature suggests that bugs in training data are highly prevalent, but little research has focused on understanding their impacts on the models used in software engineering tasks. In this paper, we address this research gap through a comprehensive empirical investigation focused on three types of data prevalent in software engineering tasks: code-based, text-based, and metric-based. Using state-of-the-art baselines, we compare the models trained on clean datasets with those trained on datasets with quality issues and without proper preprocessing. By analysing the gradients, weights, and biases from neural networks under training, we identify the symptoms of data quality and preprocessing issues. Our analysis reveals that quality issues in code data cause biased learning and gradient instability, whereas problems in text data lead to overfitting and poor generalisation of models. On the other hand, quality issues in metric data result in exploding gradients and model overfitting, and inadequate preprocessing exacerbates these effects across all three data types. Finally, we demonstrate the validity and generalizability of our findings using six new datasets. Our research provides a better understanding of the impact and symptoms of data bugs in software engineering datasets. Practitioners and researchers can leverage these findings to develop better monitoring systems and data-cleaning methods to help detect and resolve data bugs in deep learning systems.
2024-11-19
ArXiv (preprint)
arxiv.org
arxiv.org
Evaluating Generative AI Systems is a Social Science Measurement Challenge
Hanna Wallach
Meera Desai
Nicholas Pangakis
A. F. Cooper
Angelina Wang
Solon Barocas
Alexandra Chouldechova
Chad Atalla
Su Lin Blodgett
Emily Corvi
P. A. Dow
Jean Garcia-Gathright
Alexandra Olteanu
Stefanie Reed
Emily Sheng
Dan Vann
Jennifer Wortman Vaughan
Matthew Vogel
Hannah Washington
Abigail Z. Jacobs … (see 1 more)
Microsoft Research
Across academia, industry, and government, there is an increasing awareness that the measurement tasks involved in evaluating generative AI … (see more)(GenAI) systems are especially difficult. We argue that these measurement tasks are highly reminiscent of measurement tasks found throughout the social sciences. With this in mind, we present a framework, grounded in measurement theory from the social sciences, for measuring concepts related to the capabilities, impacts, opportunities, and risks of GenAI systems. The framework distinguishes between four levels: the background concept, the systematized concept, the measurement instrument(s), and the instance-level measurements themselves. This four-level approach differs from the way measurement is typically done in ML, where researchers and practitioners appear to jump straight from background concepts to measurement instruments, with little to no explicit systematization in between. As well as surfacing assumptions, thereby making it easier to understand exactly what the resulting measurements do and do not mean, this framework has two important implications for evaluating evaluations: First, it can enable stakeholders from different worlds to participate in conceptual debates, broadening the expertise involved in evaluating GenAI systems. Second, it brings rigor to operational debates by offering a set of lenses for interrogating the validity of measurement instruments and their resulting measurements.
2024-11-17
ArXiv (preprint)
arxiv.org
arxiv.org
Towards a General Recipe for Combinatorial Optimization with Multi-Filter GNNs
Frederik Wenkel
Semih Cantürk
Stefan Horoi
Michael Perlmutter
Guy Wolf
2024-11-16
logconference.io/LOG/2024/Conference (oral)
openreview.net
openreview.net
UTG: Towards a Unified View of Snapshot and Event Based Models for Temporal Graphs
Shenyang Huang
Farimah Poursafaei
Reihaneh Rabbany
Guillaume Rabusseau
Emanuele Rossi
2024-11-16
logconference.io/LOG/2024/Conference (poster)
doi.org
openreview.net
EDAI Framework for Integrating Equity, Diversity, and Inclusion Throughout the Lifecycle of AI to Improve Health and Oral Health Care: Qualitative Study
Samira Abbasgholizadeh-Rahimi
Richa Shrivastava
Anita Brown-Johnson
Pascale Caidor
Claire Davies
Amal Idrissi Janati
Pascaline Kengne Talla
Sreenath Madathil
Bettina M Willie
Elham Emami
2024-11-15
Journal of Medical Internet Research (published)
doi.org
Outcomes of guidelines from health technology assessment organizations in community-based primary care: a systematic mixed studies review
Ashkan Baradaran
Raymond Tolentino
Roland Grad
Isabelle Ganache
Genevieve Gore
Samira Abbasgholizadeh-Rahimi
Pierre Pluye
2024-11-14
International Journal of Technology Assessment in Health Care (published)
doi.org
Abstract 4142894: Multimorbidity Trajectories Across the Lifespan in Patients with Congenital Heart Disease
Chao Li
Aihua Liu
Solomon Bendayan
Liming Guo
Judith Therrien
Archer Yang
Robyn Tamblyn
Jay Brophy
Yue Li
Ariane Marelli
Background: Befitted from advances in medical care, patients with congenital heart disease (CHD) now survive to adulthood but face elevated… (see more) risks of both cardiac and non-cardiac complications. Understanding the trajectories of comorbidity development over a patient's lifespan is cornerstone to optimize care expected to improve long-term health outcomes. Research Aim: This study aims to investigate the temporal sequences and evolution of comorbidities in CHD patients across their lifespan. We hypothesize that multimorbidity trajectories in CHD patients are linked to CHD lesion severity and age at onset of specific comorbidities. Methods: Using the Quebec CHD database which comprised data in outpatient visits, hospitalization records and vital status from 1983 to 2017, we designed a longitudinal cohort study evaluating the development of 39 comorbidities coded using ICD-9/10. Temporal sequences were mapped using median age of onset. Associations between disease pairs were quantified by hazard ratios from Cox proportional hazard models adjusting for age, sex, genetic syndrome, competing risks of death, and taking into account the time-varying nature of the predictor diseases. Results: The cohort included 9,764 individuals with severe and 127,729 with non-severe CHD lesions. In severe CHD patients, most comorbidities developed between ages 25 and 40. Comorbidity progression began with childhood cardiovascular diseases, followed by systemic diseases such as diabetes, liver and kidney diseases, and advanced to heart failure and dementia in middle adulthood. In addition, mental disorders emerged in early adulthood and were associated with subsequent development of kidney diseases and dementia. Different trajectories were observed in non-severe CHD patients with 2-3 decades later disease onsets and non-differential onsets between cardiovascular and systemic complications (Figure). Conclusions: Distinct multimorbidity trajectories were observed in CHD patients by CHD lesion severity. In patients with severe CHD lesions, early systemic diseases significantly influenced subsequent complications. These findings highlight the need for well-timed surveillance guidelines and interventions to improve health outcomes.
2024-11-12
Circulation (published)
doi.org
Beyond the Safety Bundle: Auditing the Helpful and Harmless Dataset
Khaoula Chehbouni
Jonathan Colacco-Carr
Yash More
Jackie Ck Cheung
Golnoosh Farnadi
In an effort to mitigate the harms of large language models (LLMs), learning from human feedback (LHF) has been used to steer LLMs towards o… (see more)utputs that are intended to be both less harmful and more helpful. Despite the widespread adoption of LHF in practice, the quality of this feedback and its effectiveness as a safety mitigation technique remain unclear. This study addresses these issues by auditing the widely-used Helpful and Harmless (HH) dataset by Anthropic. Our work includes: (1) a thorough investigation of the dataset's content through both manual and automated evaluation; (2) experiments demonstrating the dataset's impact on models' safety; and (3) an analysis of the 100 most influential papers citing this dataset. Through our audit, we showcase how conceptualization failures and quality issues identified in the HH dataset can create additional harms by leading to disparate safety behaviors across demographic groups. Our findings highlight the need for more nuanced, context-sensitive approaches to safety mitigation in LLMs.
2024-11-12
ArXiv (preprint)
arxiv.org
arxiv.org
Fault Localization in Deep Learning-based Software: A System-level Approach
Mohammad Mehdi Morovati
Amin Nikanjam
Foutse Khomh
2024-11-12
ArXiv (preprint)
arxiv.org
arxiv.org
Investigating the Effectiveness of Explainability Methods in Parkinson's Detection from Speech
Eleonora Mancini
Francesco Paissan
Paolo Torroni
Mirco Ravanelli
Cem Subakan
Speech impairments in Parkinson's disease (PD) provide significant early indicators for diagnosis. While models for speech-based PD detectio… (see more)n have shown strong performance, their interpretability remains underexplored. This study systematically evaluates several explainability methods to identify PD-specific speech features, aiming to support the development of accurate, interpretable models for clinical decision-making in PD diagnosis and monitoring. Our methodology involves (i) obtaining attributions and saliency maps using mainstream interpretability techniques, (ii) quantitatively evaluating the faithfulness of these maps and their combinations obtained via union and intersection through a range of established metrics, and (iii) assessing the information conveyed by the saliency maps for PD detection from an auxiliary classifier. Our results reveal that, while explanations are aligned with the classifier, they often fail to provide valuable information for domain experts.
2024-11-12
ArXiv (preprint)
arxiv.org
arxiv.org
Refining SARS-CoV-2 Intra-host Variation by Leveraging Large-scale Sequencing Data
Fatima Mostefai
Jean-Christophe Grenier
Raphael Poujol
Julie Hussin
2024-11-12
NAR Genomics and Bioinformatics (published)
doi.org