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Found 18 papers, 6 papers with code
Stepwise Self-Consistent Mathematical Reasoning with Large Language Models
SSC-CoT employs a strategy of selecting intermediate steps based on the intersection of various reasoning chains.
TabuLa: Harnessing Language Models for Tabular Data Synthesis
Results show that Tabula averagely reduces 46. 2% training time per epoch comparing to current LLMs-based state-of-the-art algorithm and consistently achieves even higher synthetic data utility.
GTV: Generating Tabular Data via Vertical Federated Learning
Conditional vector for tabular GANs is a valuable tool to control specific features of generated data.
Permutation-Invariant Tabular Data Synthesis
We show that changing the input column order worsens the statistical difference between real and synthetic data by up to 38. 67% due to the encoding of tabular data and the network architectures.
FCT-GAN: Enhancing Table Synthesis via Fourier Transform
Mainstream state-of-the-art tabular data synthesizers draw methodologies from Generative Adversarial Networks (GANs), which are composed of a generator and a discriminator.
UGformer for Robust Left Atrium and Scar Segmentation Across Scanners
an enhanced transformer module with deformable convolutions to improve the blending of the transformer information with convolutional information and help predict irregular LAs and scar shapes.
CTAB-GAN+: Enhancing Tabular Data Synthesis
We extensively evaluate CTAB-GAN+ on data similarity and analysis utility against state-of-the-art tabular GANs.
Fabricated Flips: Poisoning Federated Learning without Data
Consequently, we design REFD, a defense specifically crafted to protect against data-free attacks.
Attacks and Defenses for Free-Riders in Multi-Discriminator GAN
To mitigate the model degradation, we propose a defense strategy against free-riders in MD-GAN, termed DFG.
ComicGAN: Text-to-Comic Generative Adversarial Network
No existing machine learning algorithms have been developed to create comic illustrations based on descriptions of illustrations, or the dialogue in comics.
Fed-TGAN: Federated Learning Framework for Synthesizing Tabular Data
And, while learning GANs to synthesize images on FL systems has just been demonstrated, it is unknown if GANs for tabular data can be learned from decentralized data sources.
DTGAN: Differential Private Training for Tabular GANs
Additionally, we rigorously evaluate the theoretical privacy guarantees offered by DP empirically against membership and attribute inference attacks.
Enhancing Robustness of On-line Learning Models on Highly Noisy Data
Classification algorithms have been widely adopted to detect anomalies for various systems, e. g., IoT, cloud and face recognition, under the common assumption that the data source is clean, i. e., features and labels are correctly set.
CTAB-GAN: Effective Table Data Synthesizing
In this paper, we develop CTAB-GAN, a novel conditional table GAN architecture that can effectively model diverse data types, including a mix of continuous and categorical variables.
Event-Based Control for Online Training of Neural Networks
During its training the learning rate and the gradient are two key factors to tune for influencing the convergence speed of the model.
QActor: On-line Active Learning for Noisy Labeled Stream Data
A central feature of QActor is to dynamically adjust the query limit according to the learning loss for each data batch.
Feedback Control for Online Training of Neural Networks
Convolutional neural networks (CNNs) are commonly used for image classification tasks, raising the challenge of their application on data flows.
RAD: On-line Anomaly Detection for Highly Unreliable Data
Classification algorithms have been widely adopted to detect anomalies for various systems, e. g., IoT, cloud and face recognition, under the common assumption that the data source is clean, i. e., features and labels are correctly set.
