A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
EI-MTD defends edge intelligence against adversarial attacks with dynamic scheduling.
A hybrid neural network optimizes AI deployment on edge and cloud for energy efficiency.
Many IoT applications at the network edge demand intelligent decisions in a real-time manner. The edge device alone, however, often cannot achieve real-time edge intelligence due to its constrained computing resources and limited local data. To tackle these challenges, we propose a platform-aided collaborative learning…
Federated edge learning improves with CSIT-free model aggregation using RIS.
Paper proposes efficient weight updates for edge nodes with minimal communication.
The paper addresses challenges in edge deep learning for IoT, proposing new directions.
Method certifies edge predictions with cloud-level reliability.
Paper proposes a multi-phase pruning pipeline for deep ensemble learning on IIoT devices.
Intelligent transportation systems (ITSs) will be a major component of tomorrow's smart cities. However, realizing the true potential of ITSs requires ultra-low latency and reliable data analytics solutions that can combine, in real-time, a heterogeneous mix of data stemming from the ITS network and its environment. Su…
Federated learning has been showing as a promising approach in paving the last mile of artificial intelligence, due to its great potential of solving the data isolation problem in large scale machine learning. Particularly, with consideration of the heterogeneity in practical edge computing systems, asynchronous edge-c…
In the future 6th generation networks, ultra-reliable and low-latency communications (URLLC) will lay the foundation for emerging mission-critical applications that have stringent requirements on end-to-end delay and reliability. Existing works on URLLC are mainly based on theoretical models and assumptions. The model-…
Energy-efficient DL inference for IoT devices reduces power consumption and improves performance.
This paper optimizes AI inference on edge devices with reduced communication and computation costs.
Resource-constrained IoT devices, such as sensors and actuators, have become ubiquitous in recent years. This has led to the generation of large quantities of data in real-time, which is an appealing target for AI systems. However, deploying machine learning models on such end-devices is nearly impossible. A typical so…
It has long been suggested that the biological brain operates at some critical point between two different phases, possibly order and chaos. Despite many indirect empirical evidence from the brain and analytical indication on simple neural networks, the foundation of this hypothesis on generic non-linear systems remain…
Researchers develop PAIN to improve self-driving safety through adversarial training.
LAD-BNet improves real-time energy forecasting on edge devices.
Sherpa.ai framework combines federated learning and differential privacy for edge AI services.
Intelligent Transportation Systems (ITSs) are envisioned to play a critical role in improving traffic flow and reducing congestion, which is a pervasive issue impacting urban areas around the globe. Rapidly advancing vehicular communication and edge cloud computation technologies provide key enablers for smart traffic …
AI algorithms outperform traditional trading methods in stock markets.
VTrackIt creates a synthetic dataset with infrastructure and vehicle info for AVs.
Edge language models show bias over time, especially on resource-constrained devices.
Paper proposes an AutoML framework for efficient device-edge co-inference.
Survey examines data quality challenges in edge ML.
The success of deep learning models is heavily tied to the use of massive amount of labeled data and excessively long training time. With the emergence of intelligent edge applications that use these models, the critical challenge is to obtain the same inference capability on a resource-constrained device while providi…
This article improves communication efficiency in distributed ML over wireless networks.
The Internet of Things (IoT) extends the Internet connectivity into billions of IoT devices around the world, where the IoT devices collect and share information to reflect status of the physical world. The Autonomous Control System (ACS), on the other hand, performs control functions on the physical systems without ex…
Traditionally, most complex intelligence architectures are extremely non-convex, which could not be well performed by convex optimization. However, this paper decomposes complex structures into three types of nodes: operators, algorithms and functions. Iteratively, propagating from node to node along edge, we prove tha…
pAElla detects malware in DCs/SCs with high accuracy.
Self-supervised VAEs improve data compression and generation.
The recent advent of `Internet of Things' (IOT) has increased the demand for enabling AI-based edge computing. This has necessitated the search for efficient implementations of neural networks in terms of both computations and storage. Although extreme quantization has proven to be a powerful tool to achieve significan…
Comorbid diseases co-occur and progress via complex temporal patterns that vary among individuals. In electronic health records we can observe the different diseases a patient has, but can only infer the temporal relationship between each co-morbid condition. Learning such temporal patterns from event data is crucial f…
iGCL preserves graph semantics in latent space augmentations.
Meta-ensemble scheme allocates queries to EC nodes for reduced latency.
AI learns market manipulation through simulation, suggesting regulation.
Spiking Neural Networks (SNNs) offer a promising alternative to conventional Artificial Neural Networks (ANNs) for the implementation of on-device low-power online learning and inference. On-device training is, however, constrained by the limited amount of data available at each device. In this paper, we propose to mit…
Fog learning distributes ML model training across heterogeneous devices and networks.
Mathematical framework using Riemannian geometry for intelligence and consciousness.
Designing deep learning models for highly-constrained hardware would allow imbuing many edge devices with intelligence. Microcontrollers (MCUs) are an attractive platform for building smart devices due to their low cost, wide availability, and modest power usage. However, they lack the computational resources to run ne…
Artificial intelligence has impacted many aspects of human life. This paper studies the impact of artificial intelligence on economic theory. In particular we study the impact of artificial intelligence on the theory of bounded rationality, efficient market hypothesis and prospect theory.
CoNNTrA trains DNNs with low-power, low-memory constraints.
The emergence of various intelligent mobile applications demands the deployment of powerful deep learning models at resource-constrained mobile devices. The device-edge co-inference framework provides a promising solution by splitting a neural network at a mobile device and an edge computing server. In order to balance…
AI models assess psychological risks in currency trading.
The growing number of low-power smart devices in the Internet of Things is coupled with the concept of "Edge Computing", that is moving some of the intelligence, especially machine learning, towards the edge of the network. Enabling machine learning algorithms to run on resource-constrained hardware, typically on low-p…
This thesis explores emergent intelligence in disordered systems like spin glasses and neural networks.
Deep neural networks near edge of chaos show universal scaling laws.
This paper compares LSTM, GRU, and Transformer models for stock price prediction.
This brief note highlights some basic concepts required toward understanding the evolution of machine learning and deep learning models. The note starts with an overview of artificial intelligence and its relationship to biological neuron that ultimately led to the evolution of todays intelligent models.