Unsupervised label noise modeling and loss correction
Despite being robust to small amounts of label noise, convolutional neural networks trained
with stochastic gradient methods have been shown to easily fit random labels. When there …
with stochastic gradient methods have been shown to easily fit random labels. When there …
IoTBoT-IDS: A novel statistical learning-enabled botnet detection framework for protecting networks of smart cities
The rapid proliferation of the Internet of Things (IoT) systems, has enabled transforming
urban areas into smart cities. Smart cities' paradigm has resulted in improved quality of life …
urban areas into smart cities. Smart cities' paradigm has resulted in improved quality of life …
Mutational landscape and significance across 12 major cancer types
Abstract The Cancer Genome Atlas (TCGA) has used the latest sequencing and analysis
methods to identify somatic variants across thousands of tumours. Here we present data and …
methods to identify somatic variants across thousands of tumours. Here we present data and …
SciClone: inferring clonal architecture and tracking the spatial and temporal patterns of tumor evolution
The sensitivity of massively-parallel sequencing has confirmed that most cancers are
oligoclonal, with subpopulations of neoplastic cells harboring distinct mutations. A fine …
oligoclonal, with subpopulations of neoplastic cells harboring distinct mutations. A fine …
Novel geometric area analysis technique for anomaly detection using trapezoidal area estimation on large-scale networks
The prevalence of interconnected appliances and ubiquitous computing face serious threats
from the hostile activities of network attackers. Conventional Intrusion Detection Systems …
from the hostile activities of network attackers. Conventional Intrusion Detection Systems …
Multiple feature reweight densenet for image classification
Recent network research has demonstrated that the performance of convolutional neural
networks can be improved by introducing a learning block that captures spatial correlations …
networks can be improved by introducing a learning block that captures spatial correlations …
A Survey on Machine Learning‐Based Mobile Big Data Analysis: Challenges and Applications
This paper attempts to identify the requirement and the development of machine learning‐
based mobile big data (MBD) analysis through discussing the insights of challenges in the …
based mobile big data (MBD) analysis through discussing the insights of challenges in the …
Subclonal reconstruction of tumors by using machine learning and population genetics
Most cancer genomic data are generated from bulk samples composed of mixtures of cancer
subpopulations, as well as normal cells. Subclonal reconstruction methods based on …
subpopulations, as well as normal cells. Subclonal reconstruction methods based on …
Artificial intelligence enabled radio propagation for communications—Part I: Channel characterization and antenna-channel optimization
To provide higher data rates, as well as better coverage, cost efficiency, security,
adaptability, and scalability, the 5G and beyond 5G networks are developed with various …
adaptability, and scalability, the 5G and beyond 5G networks are developed with various …
Fast and robust early-exiting framework for autoregressive language models with synchronized parallel decoding
To tackle the high inference latency exhibited by autoregressive language models, previous
studies have proposed an early-exiting framework that allocates adaptive computation paths …
studies have proposed an early-exiting framework that allocates adaptive computation paths …