6 accepted papers
Attention selects information from long contexts, but standard softmax attention scales as $O(N_qN_k)$ in the number of queries $N_q$ and keys $N_k$, making long-context training and inference expensive. We propose PLASH, an attention block with provably linear-time complexity in $N_k$ that preserve…
In this work, we design quantum algorithms that are more efficient than classical algorithms to solve time-dependent and finite-horizon Markov Decision Processes (MDPs) in two distinct settings: (1) In the exact dynamics setting, where the agent has full knowledge of the environment's dynamics (i.e.…
Pan-sharpening aims to increase the spatial resolution of the low-resolution multispectral (LrMS) image with the guidance of the corresponding panchromatic (PAN) image. Although deep learning (DL)-based pan-sharpening methods have achieved promising performance, most of them have a two-fold deficien…
Electrocardiogram (ECG) biometrics has been an emerging field, and off-the-person ECG biometrics capturing the ECG from fingertips is one of the new trends in this field. However, dynamic morphological variability in the same person and low signal-to-noise ratios pose great challenges for off-the-pe…
Electrocardiogram (ECG) biometrics has aroused extensive attention in the research field of biometric recognition. How-ever, most existing methods either only consider a single do-main (time domain or frequency domain) to extract features or extract multi-features while ignoring the specific proper-…
In recent years, Electrocardiogram (ECG) biometrics has gained extensive attention. However, most existing methods adopted offline batch learning, which means that they need to accumulate all data and retrain the model when new data comes. Therefore, it is inefficient and unpractical for them to han…