Publications

Feature learning as alignment: a structural property of gradient descent in non-linear neural networks
Daniel Beaglehole
Ioannis Mitliagkas
Atish Agarwala
Understanding the mechanisms through which neural networks extract statistics from input-label pairs through feature learning is one of the … (voir plus)most important unsolved problems in supervised learning. Prior works demonstrated that the gram matrices of the weights (the neural feature matrices, NFM) and the average gradient outer products (AGOP) become correlated during training, in a statement known as the neural feature ansatz (NFA). Through the NFA, the authors introduce mapping with the AGOP as a general mechanism for neural feature learning. However, these works do not provide a theoretical explanation for this correlation or its origins. In this work, we further clarify the nature of this correlation, and explain its emergence. We show that this correlation is equivalent to alignment between the left singular structure of the weight matrices and the newly defined pre-activation tangent features at each layer. We further establish that the alignment is driven by the interaction of weight changes induced by SGD with the pre-activation features, and analyze the resulting dynamics analytically at early times in terms of simple statistics of the inputs and labels. We prove the derivative alignment occurs with high probability in specific high dimensional settings. Finally, motivated by the observation that the NFA is driven by this centered correlation, we introduce a simple optimization rule that dramatically increases the NFA correlations at any given layer and improves the quality of features learned.
2024-11-11
TMLR (accepté)
openreview.net
openreview.net
Impact of LLM-based Review Comment Generation in Practice: A Mixed Open-/Closed-source User Study
Doriane Olewicki
Léuson M. P. Da Silva
Suhaib Mujahid
Arezou Amini
Benjamin Mah
Marco Castelluccio
Sarra Habchi
Foutse Khomh
Bram Adams
2024-11-11
ArXiv (prépublication)
arxiv.org
arxiv.org
Non-Adversarial Inverse Reinforcement Learning via Successor Feature Matching
Arnav Kumar Jain
Harley Wiltzer
Jesse Farebrother
Irina Rish
Glen Berseth
Sanjiban Choudhury
2024-11-11
ArXiv (prépublication)
arxiv.org
arxiv.org
Specific inhibition and disinhibition in the higher-order structure of a cortical connectome
Michael W. Reimann
Daniela Egas Santander
András Ecker
Eilif Benjamin Muller
Neuronal network activity is thought to be structured around the activation of assemblies, or low-dimensional manifolds describing states of… (voir plus) activity. Both views describe neurons acting not independently, but in concert, likely facilitated by strong recurrent excitation between them. The role of inhibition in these frameworks – if considered at all – is often reduced to blanket inhibition with no specificity with respect to which excitatory neurons are targeted. We analyzed the structure of excitation and inhibition in the MICrONS 1mm3 dataset, an electron microscopic reconstruction of a piece of cortical tissue. We found that excitation was structured around a feed-forward flow in non-random motifs of seven or more neurons. This revealed a structure of information flow from a small number of sources to a larger number of potential targets that became only visible when larger motifs were considered instead of individual pairs. Inhibitory neurons targeted and were targeted by neurons in specific sequential positions of these motifs. Additionally, disynaptic inhibition was strongest between target motifs excited by the same group of source neurons, implying competition between them. The structure of this inhibition was also highly specific and symmetrical, contradicting the idea of non-specific blanket inhibition. None of these trends are detectable in only pairwise connectivity, demonstrating that inhibition is specifically structured by these large motifs. Further, we found that these motifs represent higher order connectivity patterns which are present, but to a lesser extent in a recently released, detailed computational model, and not at all in a distance-dependent control. These findings have important implications for how synaptic plasticity reorganizes neocortical connectivity to implement learning and for the specific role of inhibition in this process.
2024-11-11
Cerebral Cortex (New York, NY) (publié)
doi.org
Robustness of Neural Ratio and Posterior Estimators to Distributional Shifts for Population-Level Dark Matter Analysis in Strong Gravitational Lensing
Andreas Filipp
Yashar Hezaveh
Laurence Perreault-Levasseur
2024-11-08
ArXiv (prépublication)
arxiv.org
arxiv.org
Solving Hidden Monotone Variational Inequalities with Surrogate Losses
Ryan D'Orazio
Danilo Vucetic
Zichu Liu
Junhyung Lyle Kim
Ioannis Mitliagkas
Gauthier Gidel
Deep learning has proven to be effective in a wide variety of loss minimization problems. However, many applications of interest, like minim… (voir plus)izing projected Bellman error and min-max optimization, cannot be modelled as minimizing a scalar loss function but instead correspond to solving a variational inequality (VI) problem. This difference in setting has caused many practical challenges as naive gradient-based approaches from supervised learning tend to diverge and cycle in the VI case. In this work, we propose a principled surrogate-based approach compatible with deep learning to solve VIs. We show that our surrogate-based approach has three main benefits: (1) under assumptions that are realistic in practice (when hidden monotone structure is present, interpolation, and sufficient optimization of the surrogates), it guarantees convergence, (2) it provides a unifying perspective of existing methods, and (3) is amenable to existing deep learning optimizers like ADAM. Experimentally, we demonstrate our surrogate-based approach is effective in min-max optimization and minimizing projected Bellman error. Furthermore, in the deep reinforcement learning case, we propose a novel variant of TD(0) which is more compute and sample efficient.
2024-11-07
ArXiv (prépublication)
arxiv.org
arxiv.org
Unlearning in- vs. out-of-distribution data in LLMs under gradient-based method
Teodora Băluță
Pascal Lamblin
Daniel Tarlow
Fabian Pedregosa
Gintare Karolina Dziugaite
Machine unlearning aims to solve the problem of removing the influence of selected training examples from a learned model. Despite the incre… (voir plus)asing attention to this problem, it remains an open research question how to evaluate unlearning in large language models (LLMs), and what are the critical properties of the data to be unlearned that affect the quality and efficiency of unlearning. This work formalizes a metric to evaluate unlearning quality in generative models, and uses it to assess the trade-offs between unlearning quality and performance. We demonstrate that unlearning out-of-distribution examples requires more unlearning steps but overall presents a better trade-off overall. For in-distribution examples, however, we observe a rapid decay in performance as unlearning progresses. We further evaluate how example's memorization and difficulty affect unlearning under a classical gradient ascent-based approach.
2024-11-07
ArXiv (prépublication)
arxiv.org
arxiv.org
Boosting Latent Diffusion with Perceptual Objectives
Tariq Berrada
Pietro Astolfi
Jakob Verbeek
Melissa Hall
Marton Havasi
Michal Drozdzal
Yohann Benchetrit
Adriana Romero Soriano
Karteek Alahari
2024-11-06
ArXiv (prépublication)
arxiv.org
arxiv.org
A Capacitated Collection-and-Delivery-Point Location Problem with Random Utility Maximizing Customers
David Pinzon Ulloa
Ammar Metnan
Emma Frejinger
2024-11-06
ArXiv (prépublication)
arxiv.org
arxiv.org
GAPS Phase III: incorporation of capacity based weighting in the global assessment for pediatric surgery
Yasmine Yousef
Emmanuel Ameh
Luc Malemo Kalisya
Dan Poenaru
2024-11-06
Pediatric Surgery International (publié)
doi.org
SCIseg: Automatic Segmentation of Intramedullary Lesions in Spinal Cord Injury on T2-weighted MRI Scans.
Enamundram Naga Karthik
Jan Valošek
Andrew C. Smith
Dario Pfyffer
Simon Schading-Sassenhausen
Lynn Farner
KA Weber
Kenneth A. Weber
Patrick Freund
Julien Cohen-Adad
"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This ar… (voir plus)ticle will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To develop a deep learning tool for the automatic segmentation of the spinal cord and intramedullary lesions in spinal cord injury (SCI) on T2-weighted MRI scans. Materials and Methods This retrospective study included MRI data acquired between July 2002 and February 2023 from 191 patients with SCI (mean age, 48.1 years ± 17.9 [SD]; 142 males). The data consisted of T2-weighted MRI acquired using different scanner manufacturers with various image resolutions (isotropic and anisotropic) and orientations (axial and sagittal). Patients had different lesion etiologies (traumatic, ischemic, and hemorrhagic) and lesion locations across the cervical, thoracic and lumbar spine. A deep learning model, SCIseg, was trained in a three-phase process involving active learning for the automatic segmentation of intramedullary SCI lesions and the spinal cord. The segmentations from the proposed model were visually and quantitatively compared with those from three other open-source methods (PropSeg, DeepSeg and contrast-agnostic, all part of the Spinal Cord Toolbox). Wilcoxon signed-rank test was used to compare quantitative MRI biomarkers of SCI (lesion volume, lesion length, and maximal axial damage ratio) derived from the manual reference standard lesion masks and biomarkers obtained automatically with SCIseg segmentations. Results SCIseg achieved a Dice score of 0.92 ± 0.07 (mean ± SD) and 0.61 ± 0.27 for spinal cord and SCI lesion segmentation, respectively. There was no evidence of a difference between lesion length (P = .42) and maximal axial damage ratio (P = .16) computed from manually annotated lesions and the lesion segmentations obtained using SCIseg. Conclusion SCIseg accurately segmented intramedullary lesions on a diverse dataset of T2-weighted MRI scans and extracted relevant lesion biomarkers (namely, lesion volume, lesion length, and maximal axial damage ratio). SCIseg is open-source and accessible through the Spinal Cord Toolbox (v6.2 and above). Published under a CC BY 4.0 license.
2024-11-06
Radiology: Artificial Intelligence (publié)
doi.org
Spinal cord evaluation in multiple sclerosis: clinical and radiological associations, present and future
B Mark Keegan
Martina Absinta
Julien Cohen-Adad
Eoin P Flanagan
Roland G Henry
Eric C Klawiter
Shannon Kolind
Stephen Krieger
Cornelia Laule
John A Lincoln
Steven Messina
Jiwon Oh
Nico Papinutto
Seth Aaron Smith
Anthony Traboulsee
2024-11-06
Brain Communications (publié)
doi.org