Zobrazeno 1 - 10
of 23 568
pro vyhledávání: '"A, Bahri"'
M-CELS: Counterfactual Explanation for Multivariate Time Series Data Guided by Learned Saliency Maps
Over the past decade, multivariate time series classification has received great attention. Machine learning (ML) models for multivariate time series classification have made significant strides and achieved impressive success in a wide range of appl
Externí odkaz:
http://arxiv.org/abs/2411.02649
Autor:
Bahri, Ali, Yazdanpanah, Moslem, Noori, Mehrdad, Oghani, Sahar Dastani, Cheraghalikhani, Milad, Osowiech, David, Beizaee, Farzad, vargas-hakim, Gustavo adolfo., Ayed, Ismail Ben, Desrosiers, Christian
Test-Time Adaptation (TTA) addresses distribution shifts during testing by adapting a pretrained model without access to source data. In this work, we propose a novel TTA approach for 3D point cloud classification, combining sampling variation with w
Externí odkaz:
http://arxiv.org/abs/2411.01116
Autor:
Li, Peiyu, Bahri, Omar, Hosseinzadeh, Pouya, Boubrahimi, Soukaïna Filali, Hamdi, Shah Muhammad
As the demand for interpretable machine learning approaches continues to grow, there is an increasing necessity for human involvement in providing informative explanations for model decisions. This is necessary for building trust and transparency in
Externí odkaz:
http://arxiv.org/abs/2410.20539
Watermarking has recently emerged as an effective strategy for detecting the outputs of large language models (LLMs). Most existing schemes require \emph{white-box} access to the model's next-token probability distribution, which is typically not acc
Externí odkaz:
http://arxiv.org/abs/2410.02099
3D GAN inversion aims to project a single image into the latent space of a 3D Generative Adversarial Network (GAN), thereby achieving 3D geometry reconstruction. While there exist encoders that achieve good results in 3D GAN inversion, they are predo
Externí odkaz:
http://arxiv.org/abs/2409.20530
Autor:
Li, Anni, Bahri, Mehran, Gray, Robert M., Choi, Seowon, Hoseinkhani, Sajjad, Srivastava, Anchit, Marandi, Alireza, Fattahi, Hanieh
Frequency comb and field-resolved broadband absorption spectroscopy are promising techniques for rapid, precise, and sensitive detection of short-lived atmospheric pollutants on-site. Enhancing detection sensitivity in absorption spectroscopy hinges
Externí odkaz:
http://arxiv.org/abs/2407.13371
Autor:
Noori, Mehrdad, Cheraghalikhani, Milad, Bahri, Ali, Hakim, Gustavo Adolfo Vargas, Osowiechi, David, Yazdanpanah, Moslem, Ayed, Ismail Ben, Desrosiers, Christian
Domain Generalization techniques aim to enhance model robustness by simulating novel data distributions during training, typically through various augmentation or stylization strategies. However, these methods frequently suffer from limited control o
Externí odkaz:
http://arxiv.org/abs/2407.03588
Autor:
Osowiechi, David, Noori, Mehrdad, Hakim, Gustavo Adolfo Vargas, Yazdanpanah, Moslem, Bahri, Ali, Cheraghalikhani, Milad, Dastani, Sahar, Beizaee, Farzad, Ayed, Ismail Ben, Desrosiers, Christian
Vision-Language Models (VLMs) such as CLIP have yielded unprecedented performance for zero-shot image classification, yet their generalization capability may still be seriously challenged when confronted to domain shifts. In response, we present Weig
Externí odkaz:
http://arxiv.org/abs/2406.13875
Diffusion models have recently been shown to excel in many image reconstruction tasks that involve inverse problems based on a forward measurement operator. A common framework uses task-agnostic unconditional models that are later post-conditioned fo
Externí odkaz:
http://arxiv.org/abs/2406.09768
Recently, there has been a surge in interest in developing optimization algorithms for overparameterized models as achieving generalization is believed to require algorithms with suitable biases. This interest centers on minimizing sharpness of the o
Externí odkaz:
http://arxiv.org/abs/2406.03682