Abstract: In interactions between automated vehicles (AVs) and crossing pedestrians, modeling implicit vehicle communication is crucial. In this work, we present a combined prediction and planning ...
Abstract: Attention-based Transformers have revolutionized natural language processing (NLP) and shown strong performance in computer vision (CV) tasks. However, as the input sequence varies, the ...
Abstract: Positron Emission Tomography (PET) has been a pivotal tool in brain research, enabling detailed analysis of cerebral flow, metabolism, and receptor occupancy. Spatial resolution is a ...
Abstract: In response to the problem that the existing service robots' semantic understanding relies on a single language input and the responses lack scene adaptability, this paper proposes a ...
Abstract: Generating realistic two-person interaction motions from text holds immense potential in computer vision and animations. While existing latent motion diffusion models offer compact and ...
Abstract: This work presents a 55nm speculative decoding-based LLM accelerator with bumpingbased face-to-face ReRAM-on-logic stacking technology. It features a local rotation unit for outlier-free low ...
ABSTRACT: As Artificial Intelligence (AI) becomes more common in professional and psychological contexts, concerns about human autonomy and epistemic vigilance have grown. Questions around the ...
Finding the right information at the right time is critical for solving complex problems. Researchers have developed an algorithm that helps individuals locate needed information more efficiently by ...
Abstract: State-space models (SSMs) and weight-only quantization alleviate huge external memory access with a minimal accuracy degradation. To support both efficiently, we propose LUTSSM, a LUT-based ...
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