8 accepted papers
Diffusion Transformers (DiTs) achieve strong video generation performance but suffer from prohibitive computation cost due to dense spatiotemporal tokenization. Most existing works rely on uniform patchification, tokenizing non-overlapping spatiotemporal with a fixed patch size regardless of the und
Temporal graph neural networks (TGNN) have achieved significant momentum in many real-world dynamic graph tasks. While most existing TGNN attack methods assume worst-case scenarios where attackers have complete knowledge of the input graph, the assumption may not always hold in real-world situations…
Self-supervised learning (SSL) offers a compelling solution to the challenge of extensive labeled data requirements in traditional supervised learning. With the proven success of Vision Transformers (ViTs) in supervised tasks, there is increasing interest in adapting them for SSL frameworks. However…
Deep Neural Networks (DNNs), essential for diverse applications such as visual recognition and eldercare, often require a large amount of labeled data for training, making widespread deployment of DNNs a challenging task. Self-supervised learning (SSL) emerges as a promising approach, which leverage…
There has been a proliferation of artificial intelligence applications, where model training is key to promising high-quality services for these applications. However, the model training process is both time-intensive and energy-intensive, inevitably affecting the user's demand for application effic…
Recently, sparse training has emerged as a promising paradigm for efficient deep learning on edge devices. The current research mainly devotes the efforts to reducing training costs by further increasing model sparsity. However, increasing sparsity is not always ideal since it will inevitably introd…
"Stochastic rounding is a critical technique used in low-precision deep neural networks (DNNs) training to ensure good model accuracy. However, it requires a large number of random numbers generated on the fly. This is not a trivial task on the hardware platforms such as FPGA and ASIC. The widely us…
The rapid development and wide utilization of object detection techniques have aroused attention on both accuracy and speed of object detectors. However, the current state-of-the-art object detection works are either accuracy-oriented using a large model but leading to high latency or speed-oriented…