6 accepted papers
Diffusion-based Large Language Models (dLLMs) parallelize text generation by framing decoding as a denoising process, but suffer from high computational overhead since they predict all future suffix tokens at each step while retaining only a small fraction. We propose $\textbf{Diffusion Scratchpad}…
Recently, anchor-based incomplete multi-view clustering (IMVC) has been widely adopted for fast clustering, but most existing approaches still encounter some issues: (1) They generally rely on the observed samples to construct anchor graphs, ignoring the potentially useful information of missing ins…
Temporal knowledge graph question answering (TKGQA) addresses time-sensitive queries using knowledge bases. Although large language models (LLMs) and LLM-based agents such as ReAct have shown potential for TKGQA, they often lack sufficient temporal constraints in the retrieval process. To tackle thi…
This paper presents Edge-based Mixture of Experts (MoE) Collaborative Computing (EMC2), an optimal computing system designed for autonomous vehicles (AVs) that simultaneously achieves low-latency and high-accuracy 3D object detection. Unlike existing works, the EMC2 introduces a novel scenario-aware…
Quantization of deep neural networks (DNN) has been proven effective for compressing and accelerating DNN models. Data-free quantization (DFQ) is a promising approach without the original datasets under privacy-sensitive and confidential scenarios. However, current DFQ solutions degrade accuracy, ne…
Recently, researchers have started decomposing deep neural network models according to their semantics or functions. Recent work has shown the effectiveness of decomposed functional blocks for defending adversarial attacks, which add small input perturbation to the input image to fool the DNN models…