Search Results for author: Tung-Yu Wu

Found 7 papers, 1 papers with code

DOrA: 3D Visual Grounding with Order-Aware Referring

no code implementations25 Mar 2024 Tung-Yu Wu, Sheng-Yu Huang, Yu-Chiang Frank Wang

3D visual grounding aims to identify the target object within a 3D point cloud scene referred to by a natural language description.

Visual Grounding

Model Extraction Attack against Self-supervised Speech Models

no code implementations29 Nov 2022 Tsu-Yuan Hsu, Chen-An Li, Tung-Yu Wu, Hung-Yi Lee

In the first stage, SSL is conducted on the large-scale unlabeled corpus to pre-train a small speech model.

Model extraction Self-Supervised Learning

The Efficacy of Self-Supervised Speech Models for Audio Representations

1 code implementation26 Sep 2022 Tung-Yu Wu, Chen-An Li, Tzu-Han Lin, Tsu-Yuan Hsu, Hung-Yi Lee

Extensive experiments on speech and non-speech audio datasets are conducted to investigate the representation abilities of our ensemble method and its single constituent model.

Pitch Classification Representation Learning +1

Mini-batch Metropolis-Hastings MCMC with Reversible SGLD Proposal

no code implementations8 Aug 2019 Tung-Yu Wu, Y. X. Rachel Wang, Wing H. Wong

To further extend the utility of the algorithm to high dimensional settings, we construct a proposal with forward and reverse moves using stochastic gradient and show that the construction leads to reasonable acceptance probabilities.

Convergence of Contrastive Divergence with Annealed Learning Rate in Exponential Family

no code implementations20 May 2016 Bai Jiang, Tung-Yu Wu, Wing H. Wong

In this paper, we establish consistency and convergence rate of CD with annealed learning rate $\eta_t$.

Convergence of Contrastive Divergence Algorithm in Exponential Family

no code implementations17 Mar 2016 Bai Jiang, Tung-Yu Wu, Yifan Jin, Wing H. Wong

\sum_{s=0}^{t-1} \theta_s \right/ t$ as $t \to \infty$ is a consistent estimate for the true parameter $\theta_\star$.

Learning Summary Statistic for Approximate Bayesian Computation via Deep Neural Network

no code implementations8 Oct 2015 Bai Jiang, Tung-Yu Wu, Charles Zheng, Wing H. Wong

Approximate Bayesian Computation (ABC) methods are used to approximate posterior distributions in models with unknown or computationally intractable likelihoods.

Computational Efficiency

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