Search Results for author: Joonseok Lee

Found 11 papers, 5 papers with code

Session-aware Linear Item-Item Models for Session-based Recommendation

3 code implementations30 Mar 2021 Minjin Choi, jinhong Kim, Joonseok Lee, Hyunjung Shim, Jongwuk Lee

Session-based recommendation aims at predicting the next item given a sequence of previous items consumed in the session, e. g., on e-commerce or multimedia streaming services.

Session-Based Recommendations

Local Collaborative Autoencoders

1 code implementation30 Mar 2021 Minjin Choi, Yoonki Jeong, Joonseok Lee, Jongwuk Lee

Top-N recommendation is a challenging problem because complex and sparse user-item interactions should be adequately addressed to achieve high-quality recommendation results.

A Hierarchical Multi-Modal Encoder for Moment Localization in Video Corpus

no code implementations18 Nov 2020 BoWen Zhang, Hexiang Hu, Joonseok Lee, Ming Zhao, Sheide Chammas, Vihan Jain, Eugene Ie, Fei Sha

Identifying a short segment in a long video that semantically matches a text query is a challenging task that has important application potentials in language-based video search, browsing, and navigation.

Language Modelling Temporal Localization +1

Vid-ODE: Continuous-Time Video Generation with Neural Ordinary Differential Equation

1 code implementation16 Oct 2020 Sunghyun Park, Kangyeol Kim, Junsoo Lee, Jaegul Choo, Joonseok Lee, Sookyung Kim, Edward Choi

Video generation models often operate under the assumption of fixed frame rates, which leads to suboptimal performance when it comes to handling flexible frame rates (e. g., increasing the frame rate of the more dynamic portion of the video as well as handling missing video frames).

Video Generation

Saving Face: Investigating the Ethical Concerns of Facial Recognition Auditing

no code implementations3 Jan 2020 Inioluwa Deborah Raji, Timnit Gebru, Margaret Mitchell, Joy Buolamwini, Joonseok Lee, Emily Denton

Although essential to revealing biased performance, well intentioned attempts at algorithmic auditing can have effects that may harm the very populations these measures are meant to protect.

Computers and Society

N-GCN: Multi-scale Graph Convolution for Semi-supervised Node Classification

1 code implementation24 Feb 2018 Sami Abu-El-Haija, Amol Kapoor, Bryan Perozzi, Joonseok Lee

Graph Convolutional Networks (GCNs) have shown significant improvements in semi-supervised learning on graph-structured data.

General Classification Node Classification

Network of Graph Convolutional Networks Trained on Random Walks

no code implementations ICLR 2018 Sami Abu-El-Haija, Amol Kapoor, Bryan Perozzi, Joonseok Lee

Graph Convolutional Networks (GCNs) are a recently proposed architecture which has had success in semi-supervised learning on graph-structured data.

General Classification Node Classification

Personalized Academic Research Paper Recommendation System

no code implementations19 Apr 2013 Joonseok Lee, Kisung Lee, Jennifer G. Kim

To ease this difficulty, we propose a Personalized Academic Research Paper Recommendation System, which recommends related articles, for each researcher, that may be interesting to her/him.

Collaborative Filtering Recommendation Systems

Automatic Feature Induction for Stagewise Collaborative Filtering

no code implementations NeurIPS 2012 Joonseok Lee, Mingxuan Sun, Seungyeon Kim, Guy Lebanon

Recent approaches to collaborative filtering have concentrated on estimating an algebraic or statistical model, and using the model for predicting missing ratings.

Collaborative Filtering

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