Robert Birke | DeepAI

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∙ 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...

0 Jeroen M. Galjaard, et al. ∙

∙ 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...

0 Cristina Silvano, et al. ∙

∙ 03/08/2023

Model-Agnostic Federated Learning

Since its debut in 2016, Federated Learning (FL) has been tied to the in...

0 Gianluca Mittone, et al. ∙

∙ 02/15/2023

Experimenting with Emerging ARM and RISC-V Systems for Decentralised Machine Learning

Decentralised Machine Learning (DML) enables collaborative machine learn...

0 Gianluca Mittone, et al. ∙

∙ 11/17/2022

Permutation-Invariant Tabular Data Synthesis

Tabular data synthesis is an emerging approach to circumvent strict regu...

0 Yujin Zhu, et al. ∙

∙ 10/12/2022

FCT-GAN: Enhancing Table Synthesis via Fourier Transform

Synthetic tabular data emerges as an alternative for sharing knowledge w...

0 Zilong Zhao, et al. ∙

∙ 04/01/2022

CTAB-GAN+: Enhancing Tabular Data Synthesis

While data sharing is crucial for knowledge development, privacy concern...

0 Zilong Zhao, et al. ∙

∙ 08/18/2021

Fed-TGAN: Federated Learning Framework for Synthesizing Tabular Data

Generative Adversarial Networks (GANs) are typically trained to synthesi...

0 Zilong Zhao, et al. ∙

∙ 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...

1 Cosmin Octavian Pene, et al. ∙

∙ 07/06/2021

DTGAN: Differential Private Training for Tabular GANs

Tabular generative adversarial networks (TGAN) have recently emerged to ...

0 Aditya Kunar, et al. ∙

∙ 03/19/2021

Enhancing Robustness of On-line Learning Models on Highly Noisy Data

Classification algorithms have been widely adopted to detect anomalies f...

14 Zilong Zhao, et al. ∙

∙ 02/16/2021

CTAB-GAN: Effective Table Data Synthesizing

While data sharing is crucial for knowledge development, privacy concern...

0 Zilong Zhao, et al. ∙

∙ 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...

3 Taraneh Younesian, et al. ∙

∙ 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 ...

0 Isabelly Rocha, et al. ∙

∙ 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...

20 Amirmasoud Ghiassi, et al. ∙

∙ 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...

0 Taraneh Younesian, et al. ∙

∙ 11/11/2019

RAD: On-line Anomaly Detection for Highly Unreliable Data

Classification algorithms have been widely adopted to detect anomalies f...

0 Zilong Zhao, et al. ∙

∙ 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...

0 Robert Birke, et al. ∙

∙ 02/16/2019

WiSE-VAE: Wide Sample Estimator VAE

Variational Auto-encoders (VAEs) have been very successful as methods fo...

1 Shuyu Lin, et al. ∙

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