Introduces the Indic-CodecFake dataset for Indic codec deepfakes and SATYAM, a novel hyperbolic ALM that outperforms baselines through dual-stage semantic-prosodic fusion using Bhattacharya distance.
International Conference on Machine Learning , pages=
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A hyperbolic-space framework with geometric entailment constraints and a graph-aware Mamba module improves brain-network classification of ASD and MDD by explicitly modeling ROI-to-community-to-whole-brain hierarchy.
Audited MERU, HyCoCLIP, and PHyCLIP checkpoints operate near-Euclidean with saturated entailment cones, so they do not demonstrate active radial or cone-based hierarchy.
Derives novel generalization error bounds for multimodal pairwise metric learning showing that fine-grained modality features reduce hypothesis space complexity via enhanced complementarity.
citing papers explorer
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Indic-CodecFake meets SATYAM: Towards Detecting Neural Audio Codec Synthesized Speech Deepfakes in Indic Languages
Introduces the Indic-CodecFake dataset for Indic codec deepfakes and SATYAM, a novel hyperbolic ALM that outperforms baselines through dual-stage semantic-prosodic fusion using Bhattacharya distance.
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Navigating Hierarchy: Hyperbolic Learning on Brain Graphs for Disorder Diagnosis
A hyperbolic-space framework with geometric entailment constraints and a graph-aware Mamba module improves brain-network classification of ASD and MDD by explicitly modeling ROI-to-community-to-whole-brain hierarchy.
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Is the Geometry Doing the Work? An Operating-Point Audit of Hierarchy in Hyperbolic Vision-Language Models
Audited MERU, HyCoCLIP, and PHyCLIP checkpoints operate near-Euclidean with saturated entailment cones, so they do not demonstrate active radial or cone-based hierarchy.
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Quantifying Multimodal Capabilities: Formal Generalization Guarantees in Pairwise Metric Learning
Derives novel generalization error bounds for multimodal pairwise metric learning showing that fine-grained modality features reduce hypothesis space complexity via enhanced complementarity.