Publications

Improving clustering quality evaluation in noisy Gaussian mixtures
Renato Cordeiro De Amorim
Vladimir Makarenkov
2025-02-28
ArXiv (prépublication)
doi.org
arxiv.org
Interpretable deep learning for deconvolutional analysis of neural signals
Bahareh Tolooshams
Sara Matias
Hao Wu
Simona Temereanca
Naoshige Uchida
Venkatesh N. Murthy
Paul Masset
Demba Ba
The widespread adoption of deep learning to build models that capture the dynamics of neural populations is typically based on “black-box… (voir plus) approaches that lack an interpretable link between neural activity and network parameters. Here, we propose to apply algorithm unrolling, a method for interpretable deep learning, to design the architecture of sparse deconvolutional neural networks and obtain a direct interpretation of network weights in relation to stimulus-driven single-neuron activity through a generative model. We characterize our method, referred to as deconvolutional unrolled neural learning (DUNL), and show its versatility by applying it to deconvolve single-trial local signals across multiple brain areas and recording modalities. To exemplify use cases of our decomposition method, we uncover multiplexed salience and reward prediction error signals from midbrain dopamine neurons in an unbiased manner, perform simultaneous event detection and characterization in somatosensory thalamus recordings, and characterize the heterogeneity of neural responses in the piriform cortex and in the striatum during unstructured, naturalistic experiments. Our work leverages the advances in interpretable deep learning to gain a mechanistic understanding of neural activity.
2025-02-28
Neuron (publié)
doi.org
Interval Regression: A Comparative Study with Proposed Models
Tung L. Nguyen
Toby Dylan Hocking
Regression models are essential for a wide range of real-world applications. However, in practice, target values are not always precisely kn… (voir plus)own; instead, they may be represented as intervals of acceptable values. This challenge has led to the development of Interval Regression models. In this study, we provide a comprehensive review of existing Interval Regression models and introduce alternative models for comparative analysis. Experiments are conducted on both real-world and synthetic datasets to offer a broad perspective on model performance. The results demonstrate that no single model is universally optimal, highlighting the importance of selecting the most suitable model for each specific scenario.
2025-02-28
arXiv (publié)
doi.org
arxiv.org
Large language models deconstruct the clinical intuition behind diagnosing autism
Jack Stanley
Emmett Rabot
Siva Reddy
Eugene Belilovsky
Laurent Mottron
Danilo Bzdok
2025-02-28
Cell (publié)
doi.org
Learning adversarially robust kernel ensembles with kernel average pooling.
Pouya Bashivan
Reza Bayat
Adam Ibrahim
Amirozhan Dehghani
Yifei Ren
2025-02-28
Expert Systems with Applications (publié)
doi.org
LLM-Safety Evaluations Lack Robustness
Tim Beyer
Sophie Xhonneux
Simon Geisler
Gauthier Gidel
Leo Schwinn
Stephan Günnemann
In this paper, we argue that current safety alignment research efforts for large language models are hindered by many intertwined sources of… (voir plus) noise, such as small datasets, methodological inconsistencies, and unreliable evaluation setups. This can, at times, make it impossible to evaluate and compare attacks and defenses fairly, thereby slowing progress. We systematically analyze the LLM safety evaluation pipeline, covering dataset curation, optimization strategies for automated red-teaming, response generation, and response evaluation using LLM judges. At each stage, we identify key issues and highlight their practical impact. We also propose a set of guidelines for reducing noise and bias in evaluations of future attack and defense papers. Lastly, we offer an opposing perspective, highlighting practical reasons for existing limitations. We believe that addressing the outlined problems in future research will improve the field's ability to generate easily comparable results and make measurable progress.
2025-02-28
arXiv (publié)
doi.org
arxiv.org
Negotiative Alignment: Embracing Disagreement to Achieve Fairer Outcomes -- Insights from Urban Studies
Rashid A. Mushkani
Hugo Berard
Shin Koseki
2025-02-28
arXiv (publié)
doi.org
arxiv.org
Normalizing Spinal Cord Compression Measures in Degenerative Cervical Myelopathy.
Sandrine Bédard
Jan Valosek
Maryam Seif
Armin Curt
Simon Schading-Sassenhausen
Nikolai Pfender
P. Freund
Markus Hupp
Julien Cohen-Adad
2025-02-28
The spine journal (publié)
doi.org
RL4Med-DDPO: Reinforcement Learning for Controlled Guidance Towards Diverse Medical Image Generation using Vision-Language Foundation Models
Parham Saremi
Amar Kumar
Mohammed Mohammed
Zahra Tehrani Nasab
Tal Arbel
2025-02-28
arXiv (publié)
doi.org
arxiv.org
Self-adaptive cyber defense for sustainable IoT: A DRL-based IDS optimizing security and energy efficiency
Saeid Jamshidi
Ashkan Amirnia
Amin Nikanjam
Kawser Wazed Nafi
Foutse Khomh
Samira Keivanpour
2025-02-28
Journal of Network and Computer Applications (publié)
doi.org
SemEval-2025 Task 11: Bridging the Gap in Text-Based Emotion Detection
Shamsuddeen Hassan Muhammad
Nedjma OUSIDHOUM
Idris Abdulmumin
Seid Muhie Yimam
Jan Philip Wahle
Terry Lima Ruas
Meriem Beloucif
Christine de Kock
Tadesse Belay
Ibrahim Ahmad
Nirmal Surange
Daniela Teodorescu
David Ifeoluwa Adelani
Alham Fikri Aji
Felermino Ali
Vladimir Araujo
Abinew Ayele
Oana Ignat
Alexander Panchenko
Yi Zhou … (voir 1 de plus)
Saif M. Mohammad
2025-02-28
arXiv (publié)
doi.org
arxiv.org
A Taxonomy of Inefficiencies in LLM-Generated Python Code
Altaf Allah Abbassi
Leuson Da Silva
Amin Nikanjam
Foutse Khomh
Large Language Models (LLMs) are widely adopted for automated code generation with promising results. Although prior research has assessed L… (voir plus)LM-generated code and identified various quality issues- such as redundancy, poor maintainability, and sub-optimal performance- a systematic understanding and categorization of these inefficiencies remain unexplored. Therefore, we empirically investigate inefficiencies in LLM-generated Python code by state-of-the-art models, i.e., CodeLlama, DeepSeek-Coder, and CodeGemma. To do so, we manually analyze 492 generated Python code snippets in the HumanEval+ dataset. We then construct a taxonomy of inefficiencies in LLM-generated Python code that includes 5 categories (General Logic, Performance, Readability, Maintainability, and Errors) and 19 subcategories of inefficiencies. We validate the obtained taxonomy through an online survey with 58 LLM practitioners and researchers. The surveyed participants affirmed the completeness of the proposed taxonomy, and the relevance and the popularity of the identified code inefficiency patterns. Our qualitative findings indicate that inefficiencies are diverse and interconnected, affecting multiple aspects of code quality, with logic and performance-related inefficiencies being the most frequent and often co-occurring while impacting overall code quality. Our taxonomy provides a structured basis for evaluating the quality of LLM-generated code and guiding future research to improve code generation efficiency.
2025-02-28
arXiv (publié)
doi.org
arxiv.org