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Machine learning with big data: Challenges and approaches
The Big Data revolution promises to transform how we live, work, and think by enabling
process optimization, empowering insight discovery and improving decision making. The …
process optimization, empowering insight discovery and improving decision making. The …
The role of machine learning in cybersecurity
Machine Learning (ML) represents a pivotal technology for current and future information
systems, and many domains already leverage the capabilities of ML. However, deployment …
systems, and many domains already leverage the capabilities of ML. However, deployment …
Dsmt-net: Dual self-supervised multi-operator transformation for multi-source endoscopic ultrasound diagnosis
Pancreatic cancer has the worst prognosis of all cancers. The clinical application of
endoscopic ultrasound (EUS) for the assessment of pancreatic cancer risk and of deep …
endoscopic ultrasound (EUS) for the assessment of pancreatic cancer risk and of deep …
Domain generalization on medical imaging classification using episodic training with task augmentation
Medical imaging datasets usually exhibit domain shift due to the variations of scanner
vendors, imaging protocols, etc. This raises the concern about the generalization capacity of …
vendors, imaging protocols, etc. This raises the concern about the generalization capacity of …
Sok: Pragmatic assessment of machine learning for network intrusion detection
Machine Learning (ML) has become a valuable asset to solve many real-world tasks. For
Network Intrusion Detection (NID), however, scientific advances in ML are still seen with …
Network Intrusion Detection (NID), however, scientific advances in ML are still seen with …
Search result diversification
Ranking in information retrieval has been traditionally approached as a pursuit of relevant
information, under the assumption that the users' information needs are unambiguously …
information, under the assumption that the users' information needs are unambiguously …
A comprehensive review of trends, applications and challenges in out-of-distribution detection
N Ghassemi, E Fazl-Ersi - arxiv preprint arxiv:2209.12935, 2022 - arxiv.org
With recent advancements in artificial intelligence, its applications can be seen in every
aspect of humans' daily life. From voice assistants to mobile healthcare and autonomous …
aspect of humans' daily life. From voice assistants to mobile healthcare and autonomous …
TKAGFL: a federated communication framework under data heterogeneity
Federated learning still faces many problems from research to technology implementation
and the most critical problem is that the communication efficiency is not high. Therefore, the …
and the most critical problem is that the communication efficiency is not high. Therefore, the …
Non-iidness learning in behavioral and social data
L Cao - The Computer Journal, 2014 - ieeexplore.ieee.org
Most of the classic theoretical systems and tools in statistics, data mining and machine
learning are built on the fundamental assumption of IIDness, which assumes the …
learning are built on the fundamental assumption of IIDness, which assumes the …
Training keyword spotting models on non-iid data with federated learning
We demonstrate that a production-quality keyword-spotting model can be trained on-device
using federated learning and achieve comparable false accept and false reject rates to a …
using federated learning and achieve comparable false accept and false reject rates to a …