Soft Additive Attention Fusion Results over the 2s behavioral slice: MIT Dataset (top, results tabulated as (<i>μ</i> ± <i>σ</i>) values) and FICS Dataset (bottom).

Soft Additive Attention Fusion Results over the 2s behavioral slice: MIT Dataset (top, results tabulated as (<i>μ</i> ± <i>σ</i>) values) and FICS Dataset (bottom).

<p>Soft Additive Attention Fusion Results over the 2s behavioral slice: MIT Dataset (top, results tabulated as (<i>μ</i> ± <i>σ</i>) values) and FICS Dataset (bottom).</p>

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Bibliographic Details
Main Author:	Surbhi Madan (20576994) (author)
Other Authors:	Monika Gahalawat (20576997) (author), Tanaya Guha (20577000) (author), Roland Goecke (16178273) (author), Ramanathan Subramanian (10443347) (author)
Published:	2025
Subjects:	Neuroscience Science Policy Plant Biology Biological Sciences not elsewhere classified Information Systems not elsewhere classified short term memory second behavioral snippets examining various long empirical results confirm also enabling explainability utilize elementary head motion units named based fusion strategy speech features </ action units </ multimodal behavioral cues div >< p effective trait prediction >- specific traits efficient trait predictions head motion trait prediction specific behaviors multiple trait multimodal approaches level fusion fusing cues friendly traits explainable prediction centered traits slice </ personality </ interview </ three modalities relative importance plausible explanations implementation code highest pcc additive attention
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Summary:	<p>Soft Additive Attention Fusion Results over the 2s behavioral slice: MIT Dataset (top, results tabulated as (<i>μ</i> ± <i>σ</i>) values) and FICS Dataset (bottom).</p>

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