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CAE-DFKD: Bridging the Transferability Gap in Data-Free Knowledge Distillation by Zhang, Zherui, Wang, Changwei, Xu, Rongtao, Xu, Wenhao, Xu, Shibiao, Zhang, Yu, Zhou, Jie, Guo, Li

“…Data-Free Knowledge Distillation (DFKD) enables the knowledge transfer from the given pre-trained teacher network to the target student model without access to…”
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AppealNet: An Efficient and Highly-Accurate Edge/Cloud Collaborative Architecture for DNN Inference by Li, Min, Li, Yu, Tian, Ye, Jiang, Li, Xu, Qiang

“…This paper presents AppealNet, a novel edge/cloud collaborative architecture that runs deep learning (DL) tasks more efficiently than state-of-the-art…”
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Enabling On-Device Self-Supervised Contrastive Learning with Selective Data Contrast by Wu, Yawen, Wang, Zhepeng, Zeng, Dewen, Shi, Yiyu, Hu, Jingtong

“…After a model is deployed on edge devices, it is desirable for these devices to learn from unlabeled data to continuously improve accuracy. Contrastive…”
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IFHE: Intermediate-Feature Heterogeneity Enhancement for Image Synthesis in Data-Free Knowledge Distillation by Chen, Yi, Liu, Ning, Ren, Ao, Yang, Tao, Liu, Duo

“…Data-free knowledge distillation (DFKD) explores training a compact student network only by a pre-trained teacher without real data. Prevailing DFKD methods…”
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DistHD: A Learner-Aware Dynamic Encoding Method for Hyperdimensional Classification by Wang, Junyao, Huang, Sitao, Imani, Mohsen

“…The Internet of Things (IoT) has become an emerging trend that connects heterogeneous devices and enables them with new capabilities. Many applications exploit…”
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Shoggoth: Towards Efficient Edge-Cloud Collaborative Real-Time Video Inference via Adaptive Online Learning by Wang, Liang, Lu, Kai, Zhang, Nan, Qu, Xiaoyang, Wang, Jianzong, Wan, Jiguang, Li, Guokuan, Xiao, Jing

“…This paper proposes Shoggoth, an efficient edge-cloud collaborative architecture, for boosting inference performance on real-time video of changing scenes…”
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On-the-fly Improving Performance of Deep Code Models via Input Denoising by Tian, Zhao, Chen, Junjie, Zhang, Xiangyu

“…Deep learning has been widely adopted to tackle various code-based tasks by building deep code models based on a large amount of code snippets. While these…”
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LA-MTL: Latency-Aware Automated Multi-Task Learning by Sampath, Shambhavi Balamuthu, Sawani, Sami, Thoma, Moritz, Frickenstein, Lukas, Mori, Pierpaolo, Fasfous, Nael, Vemparala, Manoj Rohit, Frickenstein, Alexander, Schlichtmann, Ulf, Passerone, Claudio, Stechele, Walter

“…Multi-Task Learning (MTL) aims to unify a variety of tasks into a single network for improved training and inference efficiency. This is particularly…”
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MMDFL: Multi-Model-based Decentralized Federated Learning for Resource-Constrained AIoT Systems by Yan, Dengke, Yang, Yanxin, Hu, Ming, Fu, Xin, Chen, Mingsong

“…Along with the prosperity of Artificial Intelligence (AI) techniques, more and more Artificial Intelligence of Things (AIoT) applications adopt Federated…”
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Self-Supervised Representation Learning and Temporal-Spectral Feature Fusion for Bed Occupancy Detection by Song, Yingjian, Pitafi, Zaid Farooq, Dou, Fei, Sun, Jin, Zhang, Xiang, Phillips, Bradley G, Song, Wenzhan

“…In automated sleep monitoring systems, bed occupancy detection is the foundation or the first step before other downstream tasks, such as inferring sleep…”
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Dissecting Global Search: A Simple Yet Effective Method to Boost Individual Discrimination Testing and Repair by Quan, Lili, Li, Tianlin, Xie, Xiaofei, Chen, Zhenpeng, Chen, Sen, Jiang, Lingxiao, Li, Xiaohong

“…Deep Learning (DL) has achieved significant success in socially critical decision-making applications but often exhibits unfair behaviors, raising social…”
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NetBooster: Empowering Tiny Deep Learning By Standing on the Shoulders of Deep Giants by Yu, Zhongzhi, Fu, Yonggan, Yuan, Jiayi, You, Haoran, Lin, Yingyan Celine

“…Tiny deep learning has attracted increasing attention driven by the substantial demand for deploying deep learning on numerous intelligent Internet-of-Things…”
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CascadeHD: Efficient Many-Class Learning Framework Using Hyperdimensional Computing by Kim, Yeseong, Kim, Jiseung, Imani, Mohsen

“…The brain-inspired hyperdimensional computing (HDC) gains attention as a light-weight and extremely parallelizable learning solution alternative to deep neural…”
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Robust Tickets Can Transfer Better: Drawing More Transferable Subnetworks in Transfer Learning by Fu, Yonggan, Yuan, Ye, Wu, Shang, Yuan, Jiayi, Lin, Yingyan Celine

“…Transfer learning leverages feature representations of deep neural networks (DNNs) pretrained on source tasks with rich data to empower effective finetuning on…”
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Condense: A Framework for Device and Frequency Adaptive Neural Network Models on the Edge by Gong, Yifan, Zhao, Pu, Zhan, Zheng, Wu, Yushu, Wu, Chao, Kong, Zhenglun, Qin, Minghai, Ding, Caiwen, Wang, Yanzhi

“…With the popularity of battery-powered edge computing, an important yet under-explored problem is the supporting of DNNs for diverse edge devices. On the one…”
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Muffin: A Framework Toward Multi-Dimension AI Fairness by Uniting Off-the-Shelf Models by Sheng, Yi, Yang, Junhuan, Yang, Lei, Shi, Yiyu, Hu, Jingtong, Jiang, Weiwen

“…Model fairness (a.k.a., bias) has become one of the most critical problems in a wide range of AI applications. An unfair model in autonomous driving may cause…”
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Lightning Talk 6: Bringing Together Foundation Models and Edge Devices by Eliopoulos, Nick John, Lu, Yung-Hsiang

“…Deep learning models have been widely used in natural language processing and computer vision. These models require heavy computation, large memory, and…”
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Lightning Talk: Bridging Neuro-Dynamics and Cognition by Imani, Mohsen

“…Brain-inspired computing models have shown great potential to outperform today's deep learning solutions in terms of robustness and energy efficiency…”
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A Unified DNN Weight Pruning Framework Using Reweighted Optimization Methods by Zhang, Tianyun, Ma, Xiaolong, Zhan, Zheng, Zhou, Shanglin, Ding, Caiwen, Fardad, Makan, Wang, Yanzhi

“…To address the large model size and intensive computation requirement of deep neural networks (DNNs), weight pruning techniques have been proposed and…”
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Enabling On-Tiny-Device Model Personalization via Gradient Condensing and Alternant Partial Update by Jia, Zhenge, Shi, Yiyang, Bao, Zeyu, Wang, Zirui, Pang, Xin, Liu, Huiguo, Duan, Yu, Shen, Zhaoyan, Zhao, Mengying

“…On-device training enables the model to adapt to user-specific data by fine-tuning a pre-trained model locally. As embedded devices become ubiquitous,…”
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