13 accepted papers
In recent years, the long-range attention mechanism of vision transformers has driven significant performance breakthroughs across various computer vision tasks. However, these advancements come at the cost of inefficiency and substantial computational expense, especially when dealing with sparse da…
Diffusion language models offer parallel token generation and inherent bidirectionality, promising more efficient and powerful sequence modeling compared to autoregressive approaches. However, state-of-the-art diffusion models~(e.g., Dream 7B, LLaDA 8B) suffer from slow inference. While they match t…
Learning the visual representation via masked auto-encoder (MAE) training has been proven to be a powerful technique. Transferring the pre-trained vision transformer (ViT) to downstream tasks leads to superior performance compared to conventional task-by-task supervised learning. Recent research wo…
Neural Radiance Field (NeRF) has been widely investigated for high-quality 3D object rendering based on captured 2D images. Previous research works have continuously improved the rendering quality with various sample representation and encoding strategies. However, a common bottleneck of NeRF is the…
Large-scale text encoders in text-to-image (T2I) diffusion models have demonstrated exceptional performance in generating high-quality images from textual prompts. Unlike denoising modules that rely on multiple iterative steps, text encoders require only a single forward pass to produce text embeddi…
Conventional super-resolution methods suffer from two drawbacks: substantial computational cost in upscaling an entire large image, and the introduction of extraneous or potentially detrimental information for downstream computer vision tasks during the refinement of the background. To solve these i…
Contrastive learning (CL) has been widely investigated with various learning mechanisms and achieves strong capability in learning representations of data in a self-supervised manner using unlabeled data. A common fashion of contrastive learning on this line is employing mega-sized encoders to achie…
Contrastive learning (or its variants) has recently become a promising direction in the self-supervised learning domain, achieving similar performance as supervised learning with minimum fine-tuning. Despite the labeling efficiency, wide and large networks are required to achieve high accuracy, whic…
Recently, a new trend of exploring training sparsity has emerged, which remove parameters during training, leading to both training and inference efficiency improvement. This line of works primarily aims to obtain a single sparse model under a pre-defined large sparsity ratio. It leads to a static/f…
Training of deep Convolution Neural Networks (CNNs) requires a tremendous amount of computation and memory and thus, GPUs are widely used to meet the computation demands of these complex training tasks. However, lacking the flexibility to exploit architectural optimizations, GPUs have poor energy ef…
The usage of Deep Neural Networks (DNN) on resource-constrained edge devices has been limited due to their high computation and large memory requirement. In this work, we propose an algorithm to compress DNNs by jointly optimizing structured sparsity and quantization constraints in a single DNN trai…
Persistent cough is a symptom common to a number of respiratory disorders; however, reliable monitoring of cough frequency and cough severity over an extended period of time can be a challenge. Traditional methods involve subjective evaluation by care providers or patient self-reports. As an alterna…
The large number of parameters in deep neural networks (DNNs) often makes them prohibitive for low-power devices, such as field-programmable gate arrays (FPGA). In this paper, we propose a method to determine the relative importance of all network parameters by measuring the amount of information th…