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Samira Ebrahimi

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Articles by Samira Ebrahimi

Computer Science > Software Engineering

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By Samira Ebrahimi
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
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Computer Science > Machine Learning

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By Samira Ebrahimi
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
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Computer Science > Machine Learning

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By Samira Ebrahimi
| arXiv
Neuroscientific research has revealed that the brain encodes complex behaviors by leveraging structured, low-dimensional manifolds and dynamically fusing multiple sources of information through adaptive gating mechanisms.
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Computer Science > Graphics

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By Samira Ebrahimi
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
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Vector Quantized Latent Concepts: A Scalable Alternative to Clustering-Based Concept Discovery

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By Samira Ebrahimi
| arXiv
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[2508.00777] Zero-Shot Anomaly Detection with Dual-Branch Prompt Selection

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By Samira Ebrahimi
| arXiv
arXiv:2508.00777 (cs) [Submitted on 1 Aug 2025 (v1), last revised 15 Aug 2025 (this version, v2)] View PDF HTML (experimental) Comments: Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2508.00777 [cs.CV] (or arXiv:2508.00777v2 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2508.00777 Submission history From: Zihan Wang [ view email] [v1] Fri, 1 Aug 2025 17:00:12 UTC (34,553 KB) [v2] Fri, 15 Aug 2025 01:22:46 UTC (34,534 KB) Bibliographic Tools Bibliographic...
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GitChameleon: Evaluating AI Code Generation Against Python Library Version Incompatibilities

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By Samira Ebrahimi
| arXiv
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[2506.20040] Cross-Layer Discrete Concept Discovery for Interpreting Language Models

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By Samira Ebrahimi
| arXiv
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[2505.09503] Towards Fair In-Context Learning with Tabular Foundation Models

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By Samira Ebrahimi
| arXiv
Work on one of the world's most important websites and make an impact on open science.
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[2503.23478] Handling Delay in Real-Time Reinforcement Learning

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By Samira Ebrahimi
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
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[2503.14973] Behaviour Discovery and Attribution for Explainable Reinforcement Learning

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By Samira Ebrahimi
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[2411.14984] Adaptive Group Robust Ensemble Knowledge Distillation

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By Samira Ebrahimi
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
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[2410.19924] Prediction of Final Phosphorus Content of Steel in a Scrap-Based Electric Arc Furnace Using Artificial Neural Networks

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By Samira Ebrahimi, Patrice Ménard, Mohammad Jahazi, Valentin Hurel
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
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Empowering Clinicians with Medical Decision Transformers: A Framework for Sepsis Treatment

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By Aamer Abdul, Samira Ebrahimi
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
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[2407.03154] Reinforcement Learning for Sequence Design Leveraging Protein Language Models

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By Samira Ebrahimi
| arXiv
[Submitted on 3 Jul 2024] Title:Reinforcement Learning for Sequence Design Leveraging Protein Language Models View a PDF of the paper titled Reinforcement Learning for Sequence Design Leveraging Protein Language Models, by Jithendaraa Subramanian and 6 other authors View PDF HTML (experimental) Abstract:Protein sequence design, determined by amino acid sequences, are essential to protein engineering problems in drug discovery.
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[2405.18751] On the Limits of Multi-modal Meta-Learning with Auxiliary Task Modulation Using Conditional Batch Normalization

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By Samira Ebrahimi
| arXiv
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[2312.00966] Spectral Temporal Contrastive Learning

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By Samira Ebrahimi, Somjit Nath, Samira Ebrahimi Kahou, Guy Wolf
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
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Survey on AI Ethics: A Socio-technical Perspective

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By Samira Ebrahimi, Meghana Bhange, Maryam Babaei, Ivaxi Sheth
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
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[2307.13081] Fairness Under Demographic Scarce Regime

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By Patrik Joslin, Samira Ebrahimi
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
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[2307.05979] Transformers in Reinforcement Learning: A Survey

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By Simon J D, Samira Ebrahimi, Aamer Abdul
| arXiv
arXiv:2307.05979 (cs) Download a PDF of the paper titled Transformers in Reinforcement Learning: A Survey, by Pranav Agarwal and 4 other authors Download PDF Submission history From: Pranav Agarwal [ view email] [v1] Wed, 12 Jul 2023 07:51:12 UTC (27,281 KB) Bibliographic Tools Bibliographic Explorer Toggle Bibliographic Explorer () Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Links to Code Toggle CatalyzeX Code...
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CAMMARL: Conformal Action Modeling in Multi Agent Reinforcement Learning

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By Samira Ebrahimi, Somjit Nath, Samira Ebrahimi Kahou
| arXiv
[Submitted on 19 Jun 2023 ( v1 ), last revised 9 Feb 2024 (this version, v2)] Title:CAMMARL: Conformal Action Modeling in Multi Agent Reinforcement Learning Download a PDF of the paper titled CAMMARL: Conformal Action Modeling in Multi Agent Reinforcement Learning, by Nikunj Gupta and 1 other authors Download PDF HTML (experimental) Abstract:Before taking actions in an environment with more than one intelligent agent, an autonomous agent may benefit from reasoning about the other agents and...
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[2304.13892] Discovering Object-Centric Generalized Value Functions From Pixels

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By Gopeshh Raaj, Samira Ebrahimi
| arXiv
arXiv:2304.13892 (cs) [Submitted on 27 Apr 2023 (v1), last revised 27 Jun 2023 (this version, v2)] Download a PDF of the paper titled Discovering Object-Centric Generalized Value Functions From Pixels, by Somjit Nath and 3 other authors Download PDF Submission history From: Somjit Nath [ view email] [v1] Thu, 27 Apr 2023 00:34:24 UTC (26,917 KB) [v2] Tue, 27 Jun 2023 14:19:56 UTC (26,915 KB) Bibliographic Tools Bibliographic Explorer Toggle Bibliographic Explorer () Litmaps Toggle Litmaps...
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[2209.09441] Locally Constrained Representations in Reinforcement Learning

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By Samira Ebrahimi
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
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Low-coherence quantitative differential phase-contrast microscopy using Talbot interferometry

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By Masoomeh Dashtdar, Samira Ebrahimi, Shahid Beheshti, Kiarash Tajbakhsh
| osapublishing.org
Abstract This paper presents a simple, cost-efficient, and highly stable quantitative differential phase-contrast (PC) microscopy based on Talbot interferometry. The proposed system is composed of an optical microscope coupled with a pair of Ronchi amplitude gratings that utilizes a light-emitting diode as a low temporal coherence light source.
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Lens-free digital holographic microscopy for cell imaging and tracking by Fresnel diffraction from a phase discontinuity

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By Masoomeh Dashtdar, Samira Ebrahimi, Shahid Beheshti
| osapublishing.org
Abstract In this Letter, a very simple, stable, and portable lensless digital holographic (DH) microscopy method is presented relying on the Fresnel diffraction (FD) of light from a phase discontinuity (PD). A phase plate in the transmission or a physical step in the reflection can be employed in the path of the divergent beam of a coherent light source as a component imposing the PD.
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Independent Component Analysis Applied on Pulsed Thermographic Data for Carbon Fiber Reinforced Plastic Inspection: A Comparative Study

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By Samira Ebrahimi, Clemente Ibarra-Castanedo, Julien R. Fleuret, Xavier P. V
| MDPI
6. Discussion The goal of this study was to investigated the performances of different ICA algorithms for processing PT data. We chose two state-of-the-art methods in order to provide a fair comparison. This study focus on three features, the contrast of each defects with its closest neighbourhood, the accuracy of detection for a given segmentation algorithm, and the time of execution.
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[1706.04261] The "something something" video database for learning and evaluating visual common sense

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By Samira Ebrahimi
| arXiv
(Submitted on 13 Jun 2017 (v1), last revised 15 Jun 2017 (this version, v2)) Submission history From: Raghav Goyal [view email] [v1] Tue, 13 Jun 2017 21:26:19 GMT (15311kb,D) [v2] Thu, 15 Jun 2017 21:15:13 GMT (4726kb,D)
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[1603.05691] Do Deep Convolutional Nets Really Need to be Deep and Convolutional?

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By Samira Ebrahimi
| arXiv
(Submitted on 17 Mar 2016 (v1), last revised 4 Mar 2017 (this version, v4)) Submission history From: Gregor Urban [view email] [v1] Thu, 17 Mar 2016 21:10:38 GMT (56kb,D) [v2] Fri, 27 May 2016 02:40:37 GMT (60kb,D) [v3] Fri, 4 Nov 2016 08:24:34 GMT (61kb,D) [v4] Sat, 4 Mar 2017 00:24:45 GMT (68kb,D)
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