11 accepted papers
Unlike traditional bit-level data transmission methods, semantic communication focuses on conveying the meaning behind the data. Though promising results have been achieved, existing end-to-end learning-based semantic communication frameworks often require a synchronization of deep models between th…
Monitoring multimodal signals provides a more comprehensive understanding of health conditions compared to singlemode monitoring. In the face of the significant volumes of multimodal signals, existing IoT health monitoring systems primarily focus on high-fidelity signal transmission by encoding mult…
In recent years, semantic communication based on deep learning for source-channel joint encoding has garnered significant attention. It utilizes network models trained end-to-end to represent signals as embedding vectors and has demonstrated superior performance compared to traditional methods. Howe…
"This work presents a simple vision transformer design as a strong baseline for object localization and instance segmentation tasks. Transformers recently demonstrate competitive performance in image classification tasks. To adopt ViT to object detection and dense prediction tasks, many works inheri…
This work targets automated designing and scaling of Vision Transformers (ViTs). The motivation comes from two pain spots: 1) the lack of efficient and principled methods for designing and scaling ViTs; 2) the tremendous computational cost of training ViT that is much heavier than its convolution co…
Transformer-based models generally allocate the same amount of computation for each token in a given sequence. We develop a simple but effective “token dropping” method to accelerate the pretraining of transformer models, such as BERT, without degrading its performance on downstream tasks. In partic…
Neural architecture search (NAS) methods have shown promising results discovering models that are both accurate and fast. For NAS, training a one-shot model has became a popular strategy to rank the relative quality of different architectures (child models) using a single set of shared weights. Howe…
Recently, SpineNet has demonstrated promising results on object detection and image classification over ResNet model. However, it is unclear if the improvement adds up when combining scale-permuted backbone with advanced efficient operations and compound scaling. Furthermore, SpineNet is built with…
For deploying a deep learning model into production, it needs to be both accurate and compact to meet the latency and memory constraints. This usually results in a network that is deep (to ensure performance) and yet thin (to improve computational efficiency). In this paper, we propose an efficient…
Training large deep neural networks on massive datasets is computationally very challenging. There has been recent surge in interest in using large batch stochastic optimization methods to tackle this issue. The most prominent algorithm in this line of research is LARS, which by employing layerwis…
Convolutional neural networks typically encode an input image into a series of intermediate features with decreasing resolutions. While this structure is suited to classification tasks, it does not perform well for tasks requiring simultaneous recognition and localization (e.g., object detection). T…