research
∙
09/12/2023
BatMan-CLR: Making Few-shots Meta-Learners Resilient Against Label Noise
The negative impact of label noise is well studied in classical supervis...
share
research
∙
06/27/2023
A Survey on Deep Learning Hardware Accelerators for Heterogeneous HPC Platforms
Recent trends in deep learning (DL) imposed hardware accelerators as the...
research
∙
03/08/2023
Model-Agnostic Federated Learning
Since its debut in 2016, Federated Learning (FL) has been tied to the in...
research
∙
02/15/2023
Experimenting with Emerging ARM and RISC-V Systems for Decentralised Machine Learning
Decentralised Machine Learning (DML) enables collaborative machine learn...
research
∙
11/17/2022
Permutation-Invariant Tabular Data Synthesis
Tabular data synthesis is an emerging approach to circumvent strict regu...
research
∙
10/12/2022
FCT-GAN: Enhancing Table Synthesis via Fourier Transform
Synthetic tabular data emerges as an alternative for sharing knowledge w...
research
∙
04/01/2022
CTAB-GAN+: Enhancing Tabular Data Synthesis
While data sharing is crucial for knowledge development, privacy concern...
research
∙
08/18/2021
Fed-TGAN: Federated Learning Framework for Synthesizing Tabular Data
Generative Adversarial Networks (GANs) are typically trained to synthesi...
research
∙
08/04/2021
Multi-Label Gold Asymmetric Loss Correction with Single-Label Regulators
Multi-label learning is an emerging extension of the multi-class classif...
research
∙
07/06/2021
DTGAN: Differential Private Training for Tabular GANs
Tabular generative adversarial networks (TGAN) have recently emerged to ...
research
∙
03/19/2021
Enhancing Robustness of On-line Learning Models on Highly Noisy Data
Classification algorithms have been widely adopted to detect anomalies f...
research
∙
02/16/2021
CTAB-GAN: Effective Table Data Synthesizing
While data sharing is crucial for knowledge development, privacy concern...
research
∙
11/13/2020
End-to-End Learning from Noisy Crowd to Supervised Machine Learning Models
Labeling real-world datasets is time consuming but indispensable for sup...
research
∙
10/01/2020
PipeTune: Pipeline Parallelism of Hyper and System Parameters Tuning for Deep Learning Clusters
DNN learning jobs are common in today's clusters due to the advances in ...
research
∙
07/13/2020
TrustNet: Learning from Trusted Data Against (A)symmetric Label Noise
Robustness to label noise is a critical property for weakly-supervised c...
research
∙
01/28/2020
QActor: On-line Active Learning for Noisy Labeled Stream Data
Noisy labeled data is more a norm than a rarity for self-generated conte...
research
∙
11/11/2019
RAD: On-line Anomaly Detection for Highly Unreliable Data
Classification algorithms have been widely adopted to detect anomalies f...
research
∙
09/12/2019
Differential Approximation and Sprinting for Multi-Priority Big Data Engines
Today's big data clusters based on the MapReduce paradigm are capable of...
research
∙
02/16/2019
