Search Results for author: Thanh Tran

Found 13 papers, 4 papers with code

An artificial neural network-based system for detecting machine failures using tiny sound data: A case study

no code implementations23 Sep 2022 Thanh Tran, Sebastian Bader, Jan Lundgren

The augmented dataset was created by combining these synthesized sounds with the original sounds.

Denoising Induction Motor Sounds Using an Autoencoder

no code implementations8 Aug 2022 Thanh Tran, Sebastian Bader, Jan Lundgren

In the scope of this paper, we demonstrate the removal of generated noise with Gaussian distribution and the environmental noise with a specific example of the water sink faucet noise from the induction motor sounds.


JAMES: Job Title Mapping with Multi-Aspect Embeddings and Reasoning

no code implementations22 Feb 2022 Michiharu Yamashita, Jia Tracy Shen, Hamoon Ekhtiari, Thanh Tran, Dongwon Lee

One of the most essential tasks needed for various downstream tasks in career analytics (e. g., career trajectory analysis, job mobility prediction, and job recommendation) is Job Title Mapping (JTM), where the goal is to map user-created (noisy and non-standard) job titles to predefined and standard job titles.

Logical Reasoning

HABERTOR: An Efficient and Effective Deep Hatespeech Detector

no code implementations EMNLP 2020 Thanh Tran, Yifan Hu, Changwei Hu, Kevin Yen, Fei Tan, Kyumin Lee, Serim Park

HABERTOR inherits BERT's architecture, but is different in four aspects: (i) it generates its own vocabularies and is pre-trained from the scratch using the largest scale hatespeech dataset; (ii) it consists of Quaternion-based factorized components, resulting in a much smaller number of parameters, faster training and inferencing, as well as less memory usage; (iii) it uses our proposed multi-source ensemble heads with a pooling layer for separate input sources, to further enhance its effectiveness; and (iv) it uses a regularized adversarial training with our proposed fine-grained and adaptive noise magnitude to enhance its robustness.

Quaternion-Based Self-Attentive Long Short-Term User Preference Encoding for Recommendation

no code implementations31 Aug 2020 Thanh Tran, Di You, Kyumin Lee

Quaternion space has brought several benefits over the traditional Euclidean space: Quaternions (i) consist of a real and three imaginary components, encouraging richer representations; (ii) utilize Hamilton product which better encodes the inter-latent interactions across multiple Quaternion components; and (iii) result in a model with smaller degrees of freedom and less prone to overfitting.

Recommendation Systems

Adversarial Mahalanobis Distance-based Attentive Song Recommender for Automatic Playlist Continuation

1 code implementation8 Jun 2019 Thanh Tran, Renee Sweeney, Kyumin Lee

Our first approach uses user-playlist-song interactions, and combines Mahalanobis distance scores between (i) a target user and a target song, and (ii) between a target playlist and the target song to account for both the user's preference and the playlist's theme.

Metric Learning

Signed Distance-based Deep Memory Recommender

1 code implementation1 May 2019 Thanh Tran, Xinyue Liu, Kyumin Lee, Xiangnan Kong

Personalized recommendation algorithms learn a user's preference for an item by measuring a distance/similarity between them.

Recommendation Systems

Regularizing Matrix Factorization with User and Item Embeddings for Recommendation

2 code implementations31 Aug 2018 Thanh Tran, Kyumin Lee, Yiming Liao, Dongwon Lee

Following recent successes in exploiting both latent factor and word embedding models in recommendation, we propose a novel Regularized Multi-Embedding (RME) based recommendation model that simultaneously encapsulates the following ideas via decomposition: (1) which items a user likes, (2) which two users co-like the same items, (3) which two items users often co-liked, and (4) which two items users often co-disliked.

Identifying On-time Reward Delivery Projects with Estimating Delivery Duration on Kickstarter

no code implementations12 Oct 2017 Thanh Tran, Kyumin Lee, Nguyen Vo, Hongkyu Choi

In Crowdfunding platforms, people turn their prototype ideas into real products by raising money from the crowd, or invest in someone else's projects.

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