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Found 15 papers, 6 papers with code
Quiver: Supporting GPUs for Low-Latency, High-Throughput GNN Serving with Workload Awareness
Quiver's key idea is to exploit workload metrics for predicting the irregular computation of GNN requests, and governing the use of GPUs for graph sampling and feature aggregation: (1) for graph sampling, Quiver calculates the probabilistic sampled graph size, a metric that predicts the degree of parallelism in graph sampling.
Assessing the Socio-economic Impacts of Secure Texting and Anti-Jamming Technologies in Non-Cooperative Networks
Consequently, this paper presents two open-source simulation models for assessing the socio-economic impacts of operating in untrusted non-cooperative networks.
Distributed Swarm Learning for Internet of Things at the Edge: Where Artificial Intelligence Meets Biological Intelligence
With the proliferation of versatile Internet of Things (IoT) services, smart IoT devices are increasingly deployed at the edge of wireless networks to perform collaborative machine learning tasks using locally collected data, giving rise to the edge learning paradigm.
Learned Index with Dynamic $\epsilon$
We theoretically analyze prediction error bounds that link $\epsilon$ with data characteristics for an illustrative learned index method.
Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation
Therefore, we propose a new metric P-Error to evaluate the performance of CardEst methods, which overcomes the limitation of Q-Error and is able to reflect the overall end-to-end performance of CardEst methods.
A Unified Transferable Model for ML-Enhanced DBMS
We propose to explore the transferabilities of the ML methods both across tasks and across DBs to tackle these fundamental drawbacks.
A Pluggable Learned Index Method via Sampling and Gap Insertion
In this paper, we propose a formal machine learning based framework to quantify the index learning objective, and study two general and pluggable techniques to enhance the learning efficiency and learning effectiveness for learned indexes.
BayesCard: Revitilizing Bayesian Frameworks for Cardinality Estimation
Recently proposed deep learning based methods largely improve the estimation accuracy but their performance can be greatly affected by data and often difficult for system deployment.
FSPN: A New Class of Probabilistic Graphical Model
We introduce factorize sum split product networks (FSPNs), a new class of probabilistic graphical models (PGMs).
FLAT: Fast, Lightweight and Accurate Method for Cardinality Estimation
Despite decades of research, existing methods either over simplify the models only using independent factorization which leads to inaccurate estimates, or over complicate them by lossless conditional factorization without any independent assumption which results in slow probability computation.
MicroRec: Efficient Recommendation Inference by Hardware and Data Structure Solutions
MicroRec accelerates recommendation inference by (1) redesigning the data structures involved in the embeddings to reduce the number of lookups needed and (2) taking advantage of the availability of High-Bandwidth Memory (HBM) in FPGA accelerators to tackle the latency by enabling parallel lookups.
Intelligent Policing Strategy for Traffic Violation Prevention
Police officer presence at an intersection discourages a potential traffic violator from violating the law.
Multi-stage Deep Classifier Cascades for Open World Recognition
At present, object recognition studies are mostly conducted in a closed lab setting with classes in test phase typically in training phase.
Who is Smarter? Intelligence Measure of Learning-based Cognitive Radios
Then we apply factor analysis on the performance data to identify and quantize the intelligence factors and cognitive capabilities of the CR.
Online Learning with Randomized Feedback Graphs for Optimal PUE Attacks in Cognitive Radio Networks
In this paper, for the first time, we study optimal PUE attack strategies by formulating an online learning problem where the attacker needs to dynamically decide the attacking channel in each time slot based on its attacking experience.
