Search Results for author:
Found 9 papers, 2 papers with code
Efficient semi-supervised inference for logistic regression under case-control studies
We find out that with the availability of the unlabeled data, the intercept parameter can be identified in semi-supervised learning setting.
MCE: Mixed Cantonese and English Audio Dataset
We proposed a novel evaluation metric called FAL, which assesses an Automatic Speech Recognition (ASR) system based on fidelity to the original audio, accuracy, and latency.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Test-Time Compensated Representation Learning for Extreme Traffic Forecasting
In this paper, we identify an underexplored problem in multivariate traffic series prediction: extreme events.
Asymptotic Statistical Analysis of $f$-divergence GAN
We show that for parametric generative models that are correctly specified, all $f$-divergence GANs with the same discriminator classes are asymptotically equivalent under suitable regularity conditions.
Time Series Generation with Masked Autoencoder
This paper shows that masked autoencoder with extrapolator (ExtraMAE) is a scalable self-supervised model for time series generation.
IDEA: Interpretable Dynamic Ensemble Architecture for Time Series Prediction
We enhance the accuracy and generalization of univariate time series point prediction by an explainable ensemble on the fly.
Bidirectional Generative Modeling Using Adversarial Gradient Estimation
This paper considers the general $f$-divergence formulation of bidirectional generative modeling, which includes VAE and BiGAN as special cases.
A General Pairwise Comparison Model for Extremely Sparse Networks
Under this setup, we show that the maximum likelihood estimator for the latent score vector of the subjects is uniformly consistent under a near-minimal condition on network sparsity.
Curiosity-Driven Recommendation Strategy for Adaptive Learning via Deep Reinforcement Learning
The design of recommendations strategies in the adaptive learning system focuses on utilizing currently available information to provide individual-specific learning instructions for learners.
