Amortized-Precision Quantization (APQ) and the MAQEE bi-level framework jointly optimize bit-widths and exit thresholds for early-exit ViTs, cutting BOPs by up to 95% with maintained accuracy across vision tasks.
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The paper provides a task-driven benchmark comparing visual, acoustic, magnetic, and resistive tactile sensors on three manipulation tasks and concludes that sensor utility depends on modality, material friction, and task specifics.
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Amortized-Precision Quantization for Early-Exit Vision Transformers
Amortized-Precision Quantization (APQ) and the MAQEE bi-level framework jointly optimize bit-widths and exit thresholds for early-exit ViTs, cutting BOPs by up to 95% with maintained accuracy across vision tasks.
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TacO: Benchmarking Tactile Sensors for Object Manipulation
The paper provides a task-driven benchmark comparing visual, acoustic, magnetic, and resistive tactile sensors on three manipulation tasks and concludes that sensor utility depends on modality, material friction, and task specifics.