Tackling Inter-class Similarity and Intra-class Variance for Microscopic Image-Based Classification. - Aishwarya Venkataramanan; Martin Laviale; Cécile Figus; Philippe Usseglio-Polatera; Cédric Pradalier (ICVS, 2021)

A data driven approach to generate realistic 3D tree barks. - Aishwarya Venkataramanan; Antoine Richard; Cédric Pradalier (Graph. Model., 2022)

Gaussian Latent Representations for Uncertainty Estimation using Mahalanobis Distance in Deep Classifiers. - Aishwarya Venkataramanan; Assia Benbihi; Martin Laviale; Cédric Pradalier (ICCV (Workshops), 2023)

Integrating Visual and Semantic Similarity Using Hierarchies for Image Retrieval. - Aishwarya Venkataramanan; Martin Laviale; Cédric Pradalier (ICVS, 2023)

Self-Supervised Gaussian Regularization of Deep Classifiers for Mahalanobis-Distance-Based Uncertainty Estimation. - Aishwarya Venkataramanan; Assia Benbihi; Martin Laviale; Cédric Pradalier (CoRR, 2023)

Usefulness of synthetic datasets for diatom automatic detection using a deep-learning approach. - Aishwarya Venkataramanan; Pierre Faure-Giovagnoli; Cyril Regan; David Heudre; Cécile Figus; Philippe Usseglio-Polatera; Cédric Pradalier; Martin Laviale (Eng. Appl. Artif. Intell., 2023)

Automatic identification of diatoms using deep learning to improve ecological diagnosis of aquatic environments. (Identification automatique des diatomées par apprentissage profond pour l'amélioration du diagnostic écologique des milieux aquatiques). - Aishwarya Venkataramanan (2023)

Probabilistic Embeddings for Frozen Vision-Language Models: Uncertainty Quantification with Gaussian Process Latent Variable Models. - Aishwarya Venkataramanan; Paul Bodesheim; Joachim Denzler (UAI, 2025)