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View article: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Open
We present a class of efficient models called MobileNets for mobile and embedded vision applications. MobileNets are based on a streamlined architecture that uses depth-wise separable convolutions to build light weight deep neural networks…
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LoRA: Low-Rank Adaptation of Large Language Models Open
An important paradigm of natural language processing consists of large-scale pre-training on general domain data and adaptation to particular tasks or domains. As we pre-train larger models, full fine-tuning, which retrains all model param…
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A comparative study of LPWAN technologies for large-scale IoT deployment Open
By 2020, more than 50 billion devices will be connected through radio communications. In conjunction with the rapid growth of the Internet of Things (IoT) market, low power wide area networks (LPWAN) have become a popular low-rate long-ran…
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Algorand Open
© 2017 Copyright is held by the owner/author(s). Algorand is a new cryptocurrency that confirms transactions with latency on the order of a minute while scaling to many users. Algorand ensures that users never have divergent views of confi…
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ENet: A Deep Neural Network Architecture for Real-Time Semantic\n Segmentation Open
The ability to perform pixel-wise semantic segmentation in real-time is of\nparamount importance in mobile applications. Recent deep neural networks aimed\nat this task have the disadvantage of requiring a large number of floating\npoint o…
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Toward Massive, Ultrareliable, and Low-Latency Wireless Communication With Short Packets Open
Most of the recent advances in the design of highspeed wireless systems are based on information-theoretic principles that demonstrate how to efficiently transmit long data packets. However, the upcoming wireless systems, notably the fifth…
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Language Modeling with Gated Convolutional Networks Open
The pre-dominant approach to language modeling to date is based on recurrent neural networks. Their success on this task is often linked to their ability to capture unbounded context. In this paper we develop a finite context approach thro…
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ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation Open
The ability to perform pixel-wise semantic segmentation in real-time is of paramount importance in mobile applications. Recent deep neural networks aimed at this task have the disadvantage of requiring a large number of floating point oper…
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RapidChain Open
A major approach to overcoming the performance and scalability limitations of current blockchain protocols is to use sharding which is to split the overheads of processing transactions among multiple, smaller groups of nodes. These groups …
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A survey on LPWA technology: LoRa and NB-IoT Open
By 2020, more than twenty five billion devices would be connected through wireless communications. In accordance with the rapid growth of the internet of things (IoT) market, low power wide area (LPWA) technologies have become popular. In …
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A consensus guide to capturing the ability to inhibit actions and impulsive behaviors in the stop-signal task Open
Response inhibition is essential for navigating everyday life. Its derailment is considered integral to numerous neurological and psychiatric disorders, and more generally, to a wide range of behavioral and health problems. Response-inhibi…
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Massive machine-type communications in 5g: physical and MAC-layer solutions Open
MTC are expected to play an essential role within future 5G systems. In the FP7 project METIS, MTC has been further classified into mMTC and uMTC. While mMTC is about wireless connectivity to tens of billions of machine-type terminals, uMT…
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Once-for-All: Train One Network and Specialize it for Efficient\n Deployment Open
We address the challenging problem of efficient inference across many devices\nand resource constraints, especially on edge devices. Conventional approaches\neither manually design or use neural architecture search (NAS) to find a\nspecial…
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Efficient Memory Management for Large Language Model Serving with PagedAttention Open
High throughput serving of large language models (LLMs) requires batching sufficiently many requests at a time. However, existing systems struggle because the key-value cache (KV cache) memory for each request is huge and grows and shrinks…
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A Survey on 5G Usage Scenarios and Traffic Models Open
The fifth-generation mobile initiative, 5G, is a \ntremendous and collective effort to specify, standardize, design, \nmanufacture, and deploy the next cellular network generation. \n5G networks will support demanding services such as enha…
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MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices Open
Natural Language Processing (NLP) has recently achieved great success by using huge pre-trained models with hundreds of millions of parameters. However, these models suffer from heavy model sizes and high latency such that they cannot be d…
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Credit Card Fraud Detection: A Realistic Modeling and a Novel Learning Strategy Open
Detecting frauds in credit card transactions is perhaps one of the best testbeds for computational intelligence algorithms. In fact, this problem involves a number of relevant challenges, namely: concept drift (customers' habits evolve and…
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Toward Low-Latency and Ultra-Reliable Virtual Reality Open
Virtual Reality (VR) is expected to be one of the killer-applications in 5G networks. However, many technical bottlenecks and challenges need to be overcome to facilitate its wide adoption. In particular, VR requirements in terms of high-t…
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FedProto: Federated Prototype Learning across Heterogeneous Clients Open
Heterogeneity across clients in federated learning (FL) usually hinders the optimization convergence and generalization performance when the aggregation of clients' knowledge occurs in the gradient space. For example, clients may differ in…
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Latency Minimization for Intelligent Reflecting Surface Aided Mobile Edge Computing Open
Computation off-loading in mobile edge computing (MEC) systems constitutes an efficient paradigm of supporting resource-intensive applications on mobile devices. However, the benefit of MEC cannot be fully exploited, when the communication…
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Edge Assisted Real-time Object Detection for Mobile Augmented Reality Open
Most existing Augmented Reality (AR) and Mixed Reality (MR) systems are able to understand the 3D geometry of the surroundings but lack the ability to detect and classify complex objects in the real world. Such capabilities can be enabled …
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Sparse Signal Processing for Grant-Free Massive Connectivity: A Future Paradigm for Random Access Protocols in the Internet of Things Open
The next wave of wireless technologies will proliferate in connecting\nsensors, machines, and robots for myriad new applications, thereby creating the\nfabric for the Internet of Things (IoT). A generic scenario for IoT\nconnectivity invol…
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Efficient Deep Learning: A Survey on Making Deep Learning Models Smaller, Faster, and Better Open
Deep learning has revolutionized the fields of computer vision, natural language understanding, speech recognition, information retrieval, and more. However, with the progressive improvements in deep learning models, their number of parame…
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Searching for MobileNetV3 Open
We present the next generation of MobileNets based on a combination of complementary search techniques as well as a novel architecture design. MobileNetV3 is tuned to mobile phone CPUs through a combination of hardware-aware network archit…
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Dynamic Task Offloading and Resource Allocation for Ultra-Reliable Low-Latency Edge Computing Open
To overcome devices' limitations in performing computation-intense\napplications, mobile edge computing (MEC) enables users to offload tasks to\nproximal MEC servers for faster task computation. However, current MEC system\ndesign is based…
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Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent Open
Most distributed machine learning systems nowadays, including TensorFlow and CNTK, are built in a centralized fashion. One bottleneck of centralized algorithms lies on high communication cost on the central node. Motivated by this, we ask,…
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Mobile Edge Computing Potential in Making Cities Smarter Open
– This paper proposes an approach to enhance users’ experience of video streaming in the context of smart cities. The proposed approach relies on the concept of mobile edge computing (MEC) as a key factor in enhancing the Quality of Servic…
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Interference Management for Cellular-Connected UAVs: A Deep Reinforcement Learning Approach Open
In this paper, an interference-aware path planning scheme for a network of\ncellular-connected unmanned aerial vehicles (UAVs) is proposed. In particular,\neach UAV aims at achieving a tradeoff between maximizing energy efficiency and\nmin…
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Edge Computing in 5G: A Review Open
5G is the next generation cellular network that aspires to achieve substantial improvement on quality of service, such as higher throughput and lower latency. Edge computing is an emerging technology that enables the evolution to 5G by bri…
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Hierarchical federated learning across heterogeneous cellular networks Open
We consider federated edge learning (FEEL), where mobile users (MUs) collaboratively learn a global model by sharing local updates on the model parameters rather than their datasets, with the help of a mobile base station (MBS). We optimiz…