Publications

Wasserstein Distributionally Robust Optimization through the Lens of Structural Causal Models and Individual Fairness
Ahmad Reza Ehyaei
Golnoosh Farnadi
Samira Samadi
2024-09-25
NeurIPS.cc/2024/Conference (poster)
openreview.net
openreview.net
When is an Embedding Model More Promising than Another?
Maxime DARRIN
Philippe Formont
Ismail Ben Ayed
Jackie Cheung
Pablo Piantanida
2024-09-25
NeurIPS.cc/2024/Conference (poster)
doi.org
openreview.net
Frequency-based View Selection in Gaussian Splatting Reconstruction
Monica Li
Pierre-Yves Lajoie
Giovanni Beltrame
Three-dimensional reconstruction is a fundamental problem in robotics perception. We examine the problem of active view selection to perform… (voir plus) 3D Gaussian Splatting reconstructions with as few input images as possible. Although 3D Gaussian Splatting has made significant progress in image rendering and 3D reconstruction, the quality of the reconstruction is strongly impacted by the selection of 2D images and the estimation of camera poses through Structure-from-Motion (SfM) algorithms. Current methods to select views that rely on uncertainties from occlusions, depth ambiguities, or neural network predictions directly are insufficient to handle the issue and struggle to generalize to new scenes. By ranking the potential views in the frequency domain, we are able to effectively estimate the potential information gain of new viewpoints without ground truth data. By overcoming current constraints on model architecture and efficacy, our method achieves state-of-the-art results in view selection, demonstrating its potential for efficient image-based 3D reconstruction.
2024-09-24
ArXiv (prépublication)
doi.org
arxiv.org
Deciphering lineage-relevant gene regulatory networks during endoderm formation by InPheRNo-ChIP.
Chen Su
William A Pastor
Amin Emad
Deciphering the underlying gene regulatory networks (GRNs) that govern early human embryogenesis is critical for understanding developmental… (voir plus) mechanisms yet remains challenging due to limited sample availability and the inherent complexity of the biological processes involved. To address this, we developed InPheRNo-ChIP, a computational framework that integrates multimodal data, including RNA-seq, transcription factor (TF)-specific ChIP-seq, and phenotypic labels, to reconstruct phenotype-relevant GRNs associated with endoderm development. The core of this method is a probabilistic graphical model that models the simultaneous effect of TFs on their putative target genes to influence a particular phenotypic outcome. Unlike the majority of existing GRN inference methods that are agnostic to the phenotypic outcomes, InPheRNo-ChIP directly incorporates phenotypic information during GRN inference, enabling the distinction between lineage-specific and general regulatory interactions. We integrated data from three experimental studies and applied InPheRNo-ChIP to infer the GRN governing the differentiation of human embryonic stem cells into definitive endoderm. Benchmarking against a scRNA-seq CRISPRi study demonstrated InPheRNo-ChIP's ability to identify regulatory interactions involving endoderm markers FOXA2, SMAD2, and SOX17, outperforming other methods. This highlights the importance of incorporating the phenotypic context during network inference. Furthermore, an ablation study confirms the synergistic contribution of ChIP-seq, RNA-seq, and phenotypic data, highlighting the value of multimodal integration for accurate phenotype-relevant GRN reconstruction.
2024-09-23
Briefings in Bioinformatics (publié)
doi.org
A neuronal least-action principle for real-time learning in cortical circuits
Walter Senn
Dominik Dold
Akos F. Kungl
Benjamin Ellenberger
Jakob Jordan
Yoshua Bengio
João Sacramento
Mihai A. Petrovici
One of the most fundamental laws of physics is the principle of least action. Motivated by its predictive power, we introduce a neuronal lea… (voir plus)st-action principle for cortical processing of sensory streams to produce appropriate behavioural outputs in real time. The principle postulates that the voltage dynamics of cortical pyramidal neurons prospectively minimize the local somato-dendritic mismatch error within individual neurons. For motor output neurons, it implies minimizing an instantaneous behavioural error. For deep network neurons, it implies a prospective firing to overcome integration delays and correct for possible output errors right in time. The neuron-specific errors are extracted in the apical dendrites of pyramidal neurons through a cortical microcircuit that tries to explain away the feedback from the periphery, and correct the trajectory on the fly. Any motor output is in a moving equilibrium with the sensory inputs and the motor feedback during the whole sensory-motor trajectory. Ongoing synaptic plasticity reduces the somato-dendritic mismatch error within each cortical neuron and performs gradient descent on the output cost at any moment in time. The neuronal least-action principle offers an axiomatic framework to derive local neuronal and synaptic dynamics for global real-time computation and learning in the brain and in physical substrates in general.
2024-09-23
bioRxiv (prépublication)
doi.org
Not Only the Last-Layer Features for Spurious Correlations: All Layer Deep Feature Reweighting
Humza Wajid Hameed
G'eraldin Nanfack
Eugene Belilovsky
Spurious correlations are a major source of errors for machine learning models, in particular when aiming for group-level fairness. It has b… (voir plus)een recently shown that a powerful approach to combat spurious correlations is to re-train the last layer on a balanced validation dataset, isolating robust features for the predictor. However, key attributes can sometimes be discarded by neural networks towards the last layer. In this work, we thus consider retraining a classifier on a set of features derived from all layers. We utilize a recently proposed feature selection strategy to select unbiased features from all the layers. We observe this approach gives significant improvements in worst-group accuracy on several standard benchmarks.
2024-09-23
ArXiv (prépublication)
doi.org
arxiv.org
Protein Language Models: Is Scaling Necessary?
Quentin Fournier
Robert M. Vernon
Almer van der Sloot
Benjamin Schulz
Sarath Chandar
Christopher James Langmead
2024-09-23
bioRxiv (prépublication)
doi.org
A Toolbox for Surfacing Health Equity Harms and Biases in Large Language Models
Stephen R. Pfohl
Heather Cole-Lewis
Rory A Sayres
Darlene Neal
Mercy Nyamewaa Asiedu
Awa Dieng
Nenad Tomašev
Qazi Mamunur Rashid
Shekoofeh Azizi
Negar Rostamzadeh
Liam G. McCoy
L. A. Celi
Yun Liu
Mike Schaekermann
Alanna Walton
Alicia Parrish
Chirag Nagpal
Preeti Singh
Akeiylah Dewitt
P. A. Mansfield … (voir 10 de plus)
Sushant Prakash
Katherine Heller
Alan Karthikesalingam
Christopher Semturs
Joelle Barral
Greg C. Corrado
Yossi Matias
Jamila Smith-Loud
Ivor Horn
Karan Singhal
2024-09-23
Nature Medicine (publié)
doi.org
arxiv.org
What Are They Doing? Joint Audio-Speech Co-Reasoning
Yingzhi Wang
Pooneh Mousavi
Artem Ploujnikov
Mirco Ravanelli
2024-09-22
ArXiv (prépublication)
doi.org
arxiv.org
AI content detection in the emerging information ecosystem: new obligations for media and tech companies
Alistair Knott
Dino Pedreschi
Toshiya Jitsuzumi
Susan Leavy
D. Eyers
Tapabrata Chakraborti
Andrew Trotman
Sundar Sundareswaran
Ricardo Baeza-Yates
Przemyslaw Biecek
Adrian Weller
Paul D. Teal
Subhadip Basu
Mehmet Haklidir
Virginia Morini
Stuart Russell
Yoshua Bengio
2024-09-21
Ethics and Information Technology (publié)
doi.org
ToxiSight: Insights Towards Detected Chat Toxicity
Zachary Yang
Domenico Tullo
Reihaneh Rabbany
We present a comprehensive explainability dashboard designed for in-game chat toxicity. This dashboard integrates various existing explainab… (voir plus)le AI (XAI) techniques, including token importance analysis, model output visualization, and attribution to the training dataset. It also provides insights through the closest positive and negative examples, facilitating a deeper understanding and potential correction of the training data. Additionally, the dashboard includes word sense analysis—particularly useful for new moderators—and offers free-text explanations for both positive and negative predictions. This multi-faceted approach enhances the interpretability and transparency of toxicity detection models.
2024-09-21
EMNLP/2024/Workshop/BlackBoxNLP (accepté)
openreview.net
openreview.net
ChainBuddy: An AI Agent System for Generating LLM Pipelines
Jingyue Zhang
Ian Arawjo
2024-09-20
ArXiv (prépublication)
doi.org
arxiv.org