no code implementations • 10 Mar 2024 • Yun-Ang Wu, Yun-Da Tsai, Shou-De Lin
In this study, we delve into the Thresholding Linear Bandit (TLB) problem, a nuanced domain within stochastic Multi-Armed Bandit (MAB) problems, focusing on maximizing decision accuracy against a linearly defined threshold under resource constraints.
no code implementations • 12 Feb 2024 • Yun-Da Tsai, Ting-Yu Yen, Pei-Fu Guo, Zhe-Yan Li, Shou-De Lin
This research paper addresses the challenge of modality mismatch in multimodal learning, where the modalities available during inference differ from those available at training.
no code implementations • 29 Jan 2024 • Tzu-Hsien Tsai, Yun-Da Tsai, Shou-De Lin
We demonstrate that the sample complexity of the first $\lambda$ output arm in lil'HDoC is bounded by the original HDoC algorithm, except for one negligible term, when the distance between the expected reward and threshold is small.
no code implementations • 1 Nov 2023 • Eric L. Lee, Tsung-Ting Kuo, Shou-De Lin
We point out several challenges in applying the existing CF-models to build a course recommendation engine, including the lack of rating and meta-data, the imbalance of course registration distribution, and the demand of course dependency modeling.
no code implementations • 20 Oct 2023 • Felix Liawi, Yun-Da Tsai, Guan-Lun Lu, Shou-De Lin
Initially, we introduce a text-swapping network that seamlessly substitutes the original text with the desired replacement.
no code implementations • 8 Oct 2023 • Pei-Fu Guo, Ying-Hsuan Chen, Yun-Da Tsai, Shou-De Lin
In this work, we conduct an assessment of the optimization capabilities of LLMs across various tasks and data sizes.
no code implementations • 17 Aug 2023 • Bo-Wei Huang, Keng-Te Liao, Chang-Sheng Kao, Shou-De Lin
On this issue, a research direction, invariant learning, has been proposed to extract invariant features insensitive to the distributional changes.
1 code implementation • 19 May 2023 • Karandeep Singh, Yu-Che Tsai, Cheng-Te Li, Meeyoung Cha, Shou-De Lin
Custom officials across the world encounter huge volumes of transactions.
no code implementations • 27 Mar 2023 • Yi-Ting Lee, Da-Yi Wu, Chih-Chun Yang, Shou-De Lin
The goal of this paper is to report certain scientific discoveries about a Seq2Seq model.
no code implementations • 13 Mar 2023 • Yun-Da Tsai, Tzu-Hsien Tsai, Shou-De Lin
This paper targets a variant of the stochastic multi-armed bandit problem called good arm identification (GAI).
1 code implementation • 21 Feb 2022 • YunDa Tsai, Cayon Liow, Yin Sheng Siang, Shou-De Lin
This paper reveals a data bias issue that can severely affect the performance while conducting a machine learning model for malicious URL detection.
no code implementations • COLING 2020 • Keng-Te Liao, Zhihong Shen, Chiyuan Huang, Chieh-Han Wu, PoChun Chen, Kuansan Wang, Shou-De Lin
Provided with the interpretable concepts and knowledge encoded in a pre-trained neural model, we investigate whether the tagged concepts can be applied to a broader class of applications.
no code implementations • Asian Chapter of the Association for Computational Linguistics 2020 • Keng-Te Liao, Cheng-Syuan Lee, Zhong-Yu Huang, Shou-De Lin
Disentangled representations have attracted increasing attention recently.
no code implementations • ACL 2020 • Hong-You Chen, Sz-Han Yu, Shou-De Lin
Chinese NLP applications that rely on large text often contain huge amounts of vocabulary which are sparse in corpus.
no code implementations • IJCNLP 2019 • Chih-Te Lai, Yi-Te Hong, Hong-You Chen, Chi-Jen Lu, Shou-De Lin
The objective of non-parallel text style transfer, or controllable text generation, is to alter specific attributes (e. g. sentiment, mood, tense, politeness, etc) of a given text while preserving its remaining attributes and content.
no code implementations • IJCNLP 2019 • Liang-Hsin Shen, Pei-Lun Tai, Chao-Chung Wu, Shou-De Lin
An acrostic is a form of writing that the first token of each line (or other recurring features in the text) forms a meaningful sequence.
1 code implementation • 17 Sep 2019 • Yue Liu, Helena Lee, Palakorn Achananuparp, Ee-Peng Lim, Tzu-Ling Cheng, Shou-De Lin
Human beings are creatures of habit.
no code implementations • NAACL 2019 • Hong-You Chen, Chin-Hua Hu, Leila Wehbe, Shou-De Lin
Unsupervised document representation learning is an important task providing pre-trained features for NLP applications.
5 code implementations • 13 May 2019 • Szu-Wei Fu, Chien-Feng Liao, Yu Tsao, Shou-De Lin
Adversarial loss in a conditional generative adversarial network (GAN) is not designed to directly optimize evaluation metrics of a target task, and thus, may not always guide the generator in a GAN to generate data with improved metric scores.
Ranked #21 on Speech Enhancement on VoiceBank + DEMAND
no code implementations • ICLR 2019 • Chih-Kuan Yeh, Ian E. H. Yen, Hong-You Chen, Chun-Pei Yang, Shou-De Lin, Pradeep Ravikumar
State-of-the-art deep neural networks (DNNs) typically have tens of millions of parameters, which might not fit into the upper levels of the memory hierarchy, thus increasing the inference time and energy consumption significantly, and prohibiting their use on edge devices such as mobile phones.
no code implementations • 29 Jan 2019 • Fan-Yun Sun, Yen-Yu Chang, Yueh-Hua Wu, Shou-De Lin
If artificially intelligent (AI) agents make decisions on behalf of human beings, we would hope they can also follow established regulations while interacting with humans or other AI agents.
no code implementations • NeurIPS 2018 • Ian En-Hsu Yen, Wei-Cheng Lee, Kai Zhong, Sung-En Chang, Pradeep K. Ravikumar, Shou-De Lin
We consider a generalization of mixed regression where the response is an additive combination of several mixture components.
no code implementations • 20 Oct 2018 • Wen-Hao Chen, Chin-Chi Hsu, Yi-An Lai, Vincent Liu, Mi-Yen Yeh, Shou-De Lin
Attribute-aware CF models aims at rating prediction given not only the historical rating from users to items, but also the information associated with users (e. g. age), items (e. g. price), or even ratings (e. g. rating time).
no code implementations • EMNLP 2018 • Hong-You Chen, Cheng-Syuan Lee, Keng-Te Liao, Shou-De Lin
Lexicon relation extraction given distributional representation of words is an important topic in NLP.
no code implementations • 6 Sep 2018 • Yueh-Hua Wu, Fan-Yun Sun, Yen-Yu Chang, Shou-De Lin
This work provides a thorough study on how reward scaling can affect performance of deep reinforcement learning agents.
2 code implementations • 6 Sep 2018 • Yen-Yu Chang, Fan-Yun Sun, Yueh-Hua Wu, Shou-De Lin
Inspired by Memory Network proposed for solving the question-answering task, we propose a deep learning based model named Memory Time-series network (MTNet) for time series forecasting.
1 code implementation • 12 Dec 2017 • Yueh-Hua Wu, Shou-De Lin
This paper proposes a low-cost, easily realizable strategy to equip a reinforcement learning (RL) agent the capability of behaving ethically.
1 code implementation • NeurIPS 2017 • Yi-An Lai, Chin-Chi Hsu, Wen Hao Chen, Mi-Yen Yeh, Shou-De Lin
We investigate an unsupervised generative approach for network embedding.
no code implementations • 21 Nov 2017 • Jia-Yun Jiang, Cheng-Te Li, Shou-De Lin
This paper proposes a privacy-preserving distributed recommendation framework, Secure Distributed Collaborative Filtering (SDCF), to preserve the privacy of value, model and existence altogether.
no code implementations • ICML 2017 • Ian En-Hsu Yen, Wei-Cheng Lee, Sung-En Chang, Arun Sai Suggala, Shou-De Lin, Pradeep Ravikumar
The latent feature model (LFM), proposed in (Griffiths \& Ghahramani, 2005), but possibly with earlier origins, is a generalization of a mixture model, where each instance is generated not from a single latent class but from a combination of latent features.
no code implementations • 28 Oct 2016 • Guang-He Lee, Shao-Wen Yang, Shou-De Lin
Specifically, by modeling and learning the deviation of data, we design a novel matrix factorization model.
no code implementations • NeurIPS 2015 • Ian En-Hsu Yen, Shan-Wei Lin, Shou-De Lin
In past few years, several techniques have been proposed for training of linear Support Vector Machine (SVM) in limited-memory setting, where a dual block-coordinate descent (dual-BCD) method was used to balance cost spent on I/O and computation.
no code implementations • NeurIPS 2014 • Ian En-Hsu Yen, Ting-Wei Lin, Shou-De Lin, Pradeep K. Ravikumar, Inderjit S. Dhillon
In this paper, we propose a Sparse Random Feature algorithm, which learns a sparse non-linear predictor by minimizing an $\ell_1$-regularized objective function over the Hilbert Space induced from kernel function.