Sparse MERIT uses frame-wise sparse mixture-of-experts with task-specific gating on self-supervised speech features to jointly optimize enhancement and emotion recognition, reporting gains over baselines on MSP-Podcast at low SNR.
Modeling task relationships in multi-task learning with multi-gate mixture-of-experts,
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MTEEG uses task-specific LoRA modules to jointly adapt a pre-trained EEG model across multiple tasks, outperforming single-task baselines on most metrics in evaluations on six downstream tasks.
UMDA combines multi-objective learning with uncertainty modeling for RTA interception and applies distillation to enable single-pass aleatoric plus epistemic uncertainty with 10x inference speedup on JD and Criteo data.
citing papers explorer
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Joint Learning using Mixture-of-Expert-Based Representation for Speech Enhancement and Robust Emotion Recognition
Sparse MERIT uses frame-wise sparse mixture-of-experts with task-specific gating on self-supervised speech features to jointly optimize enhancement and emotion recognition, reporting gains over baselines on MSP-Podcast at low SNR.
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Towards Unified Multi-task EEG Analysis with Low-Rank Adaptation
MTEEG uses task-specific LoRA modules to jointly adapt a pre-trained EEG model across multiple tasks, outperforming single-task baselines on most metrics in evaluations on six downstream tasks.
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Uncertainty Modeling for Multi-Objective RTA Interception with Distillation Acceleration
UMDA combines multi-objective learning with uncertainty modeling for RTA interception and applies distillation to enable single-pass aleatoric plus epistemic uncertainty with 10x inference speedup on JD and Criteo data.