Public articles linked to the same research event.
arXiv The work introduces ECHO-k, a task-agnostic and self-supervised principle for modality acquisition that uses a deep model's internal pretrained representations (e.g., from a foundation model) as proxy targets summarizing cross-modal information, provides theoretical guarantees in a stylized linear setting that motivate a reinforcement learning policy for sequential modality selection, and, across task-agnostic and label-free acquisition baselines, consistently improves budgeted downstream performance across diverse foundation-model backends.
The work introduces ECHO-k, a task-agnostic and self-supervised principle for modality acquisition that uses a deep model's internal pretrained representations (e.g., from a foundation model) as proxy targets summarizing cross-modal information, provides theoretical guarantees in a stylized linear setting that motivate a reinforcement learning policy for sequential modality selection, and, across task-agnostic and label-free acquisition baselines, consistently improves budgeted downstream performance across diverse foundation-model backends.
The work introduces ECHO-k, a task-agnostic and self-supervised principle for modality acquisition that uses a deep model's internal pretrained representations (e.g., from a foundation model) as proxy targets summarizing cross-modal information, provides theoretical guarantees in a stylized linear setting that motivate a reinforcement learning policy for sequential modality selection, and, across task-agnostic and label-free acquisition baselines, consistently improves budgeted downstream performance across diverse foundation-model backends.
The work introduces ECHO-k, a task-agnostic and self-supervised principle for modality acquisition that uses a deep model's internal pretrained representations (e.g., from a foundation model) as proxy targets summarizing cross-modal information, provides theoretical guarantees in a stylized linear setting that motivate a reinforcement learning policy for sequential modality selection, and, across task-agnostic and label-free acquisition baselines, consistently improves budgeted downstream performance across diverse foundation-model backends.