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Found 25 papers, 9 papers with code
SFDDM: Single-fold Distillation for Diffusion models
While diffusion models effectively generate remarkable synthetic images, a key limitation is the inference inefficiency, requiring numerous sampling steps.
DALLMi: Domain Adaption for LLM-based Multi-label Classifier
In this paper, we design DALLMi, Domain Adaptation Large Language Model interpolator, a first-of-its-kind semi-supervised domain adaptation method for text data models based on LLMs, specifically BERT.
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.
BatMan-CLR: Making Few-shots Meta-Learners Resilient Against Label Noise
We show that the accuracy of Reptile, iMAML, and foMAML drops by up to 42% on the Omniglot and CifarFS datasets when meta-training is affected by label noise.
A Survey on Deep Learning Hardware Accelerators for Heterogeneous HPC Platforms
Recent trends in deep learning (DL) imposed hardware accelerators as the most viable solution for several classes of high-performance computing (HPC) applications such as image classification, computer vision, and speech recognition.
Model-Agnostic Federated Learning
MAFL marries a model-agnostic FL algorithm, AdaBoost. F, with an open industry-grade FL framework: Intel OpenFL.
Experimenting with Emerging RISC-V Systems for Decentralised Machine Learning
Federated Learning (FL) and Edge Inference are examples of DML.
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.
CTAB-GAN+: Enhancing Tabular Data Synthesis
We extensively evaluate CTAB-GAN+ on data similarity and analysis utility against state-of-the-art tabular GANs.
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.
Multi-Label Gold Asymmetric Loss Correction with Single-Label Regulators
Multi-label learning is an emerging extension of the multi-class classification where an image contains multiple labels.
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.
Robust Learning via Golden Symmetric Loss of (un)Trusted Labels
In this paper, we propose to construct a golden symmetric loss (GSL) based on the estimated confusion matrix as to avoid overfitting to noisy labels and learn effectively from hard classes.
End-to-End Learning from Noisy Crowd to Supervised Machine Learning Models
Furthermore, relabeling only 10% of the data using the expert's results in over 90% classification accuracy with SVM.
TrustNet: Learning from Trusted Data Against (A)symmetric Label Noise
Based on the insights, we design TrustNet that first adversely learns the pattern of noise corruption, being it both symmetric or asymmetric, from a small set of trusted data.
ExpertNet: Adversarial Learning and Recovery Against Noisy Labels
Today's available datasets in the wild, e. g., from social media and open platforms, present tremendous opportunities and challenges for deep learning, as there is a significant portion of tagged images, but often with noisy, i. e. erroneous, labels.
Anomaly Detection for Time Series Using VAE-LSTM Hybrid Model
In this work, we propose a VAE-LSTM hybrid model as an unsupervised approach for anomaly detection in time series.
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.
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.
WiSE-ALE: Wide Sample Estimator for Aggregate Latent Embedding
In this paper, we present a new generative model for learning latent embeddings.
WiSE-ALE: Wide Sample Estimator for Approximate Latent Embedding
Variational Auto-encoders (VAEs) have been very successful as methods for forming compressed latent representations of complex, often high-dimensional, data.
Online Label Aggregation: A Variational Bayesian Approach
Our evaluation results on various online scenarios show that BiLA can effectively infer the true labels, with an error rate reduction of at least 10 to 1. 5 percent points for synthetic and real-world datasets, respectively.
