robustness
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Reliability-Aware Multimodal Sentiment Analysis Using Speech, Visual and Textual Signals: A CMU-MOSEI Benchmark Re-analysis
Abstract: Multimodal sentiment analysis integrates linguistic, acoustic, and visual cues, but reported gains often assume that every modality is present and equally reliable. This study develops a reliability-aware multimodal sentiment-analysis framework and performs a quantitative re-analysis of verified CMU- MOSEI benchmark results to determine how modality availability changes predictive performance. The conceptual architecture combines text, acoustic, and visual encoders with quality-conditioned gated fusion. For the quantitative component, complete- and missing-modality results …
Published in Journal of Electronic Design Technology · Vol. 17, Issue 3, 2026 Read article
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Improving Reliability of Deep Learning System Under Distribution Shift Via Feature-Space Out-of-Distribution Detection
Abstract: Deep learning systems achieve strong predictive performance under independently and identically distributed conditions; however, their reliability can decline significantly when exposed to distribution shifts during deployment. This decline is often accompanied by overconfident predictions, which can introduce serious risks in safety-critical applications. In this work, we explore whether explicitly modeling the structure of the latent feature space can improve reliability through more effective out-ofdistribution detection. A statistically grounded framework based …
Published in International Journal of Bioinformatics and Computational Biology · Vol. 4, Issue 2, 2026 · pp. 66–76 Read article