Hypernetwork-conditioned RL policies improve robustness to actuator failures in fixed-wing aircraft control and generalize to time-varying failure modes not seen during training.
Gra- dient surgery for multi-task learning
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Hydra stabilizes multi-concept backdoor attacks in diffusion models via evolutionary trigger search in text encoder space and trigger-clean regularization during multi-task fine-tuning, achieving high attack success while preserving clean image quality.
VECTOR-DRIVE uses shared self-attention with semantic-aware expert routing of tokens to VL and trajectory experts plus flow-matching action decoding to reach 88.91 driving score on Bench2Drive.
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.
citing papers explorer
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Hypernetwork-Conditioned Reinforcement Learning for Robust Control of Fixed-Wing Aircraft under Actuator Failures
Hypernetwork-conditioned RL policies improve robustness to actuator failures in fixed-wing aircraft control and generalize to time-varying failure modes not seen during training.
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Awakening the Hydra: Stabilizing Multi-Concept Backdoor Injection in Text-to-Image Diffusion Models
Hydra stabilizes multi-concept backdoor attacks in diffusion models via evolutionary trigger search in text encoder space and trigger-clean regularization during multi-task fine-tuning, achieving high attack success while preserving clean image quality.
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VECTOR-Drive: Tightly Coupled Vision-Language and Trajectory Expert Routing for End-to-End Autonomous Driving
VECTOR-DRIVE uses shared self-attention with semantic-aware expert routing of tokens to VL and trajectory experts plus flow-matching action decoding to reach 88.91 driving score on Bench2Drive.
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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.