Siddhi Patil — accepted AI-conference papers

2026

A Bayesian Nonparametric Framework For Learning Disentangled Representations

Vaishnavi S Patil, Siddhi Patil, Matthew Evanusa, Amit Kumar Kundu, Cornelia Fermuller, Joseph JaJa

ICLR 2026poster

Disentangled representation learning aims to identify and organize the underlying sources of variation in observed data. However, learning disentangled representations without any additional supervision necessitates inductive biases to solve the fundamental identifiability problem of uniquely recove…

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2026

MMAU-Pro: A Challenging and Comprehensive Benchmark for Holistic Evaluation of Audio General Intelligence

Sonal Kumar, Šimon Sedláček, Vaibhavi Lokegaonkar, Fernando López, Wenyi Yu, Nishit Anand +28

AAAI 2026technical

Audio comprehension—including speech, non-speech sounds, and music—is essential for achieving human-level intelligence. Consequently, AI agents must demonstrate holistic audio understanding to qualify as generally intelligent. However, evaluating auditory intelligence comprehensively remains challen

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