Search Results for author: Jaeyoung Do

Found 7 papers, 0 papers with code

Data Augmentation for Improving Tail-traffic Robustness in Skill-routing for Dialogue Systems

no code implementations7 Jun 2023 Ting-Wei Wu, Fatemeh Sheikholeslami, Mohammad Kachuee, Jaeyoung Do, Sungjin Lee

Large-scale conversational systems typically rely on a skill-routing component to route a user request to an appropriate skill and interpretation to serve the request.

Data Augmentation Long-tail Learning

Scalable and Safe Remediation of Defective Actions in Self-Learning Conversational Systems

no code implementations17 May 2023 Sarthak Ahuja, Mohammad Kachuee, Fateme Sheikholeslami, Weiqing Liu, Jaeyoung Do

Off-Policy reinforcement learning has been a driving force for the state-of-the-art conversational AIs leading to more natural humanagent interactions and improving the user satisfaction for goal-oriented agents.

Off-policy evaluation regression +1

Grounding Counterfactual Explanation of Image Classifiers to Textual Concept Space

no code implementations CVPR 2023 Siwon Kim, Jinoh Oh, Sungjin Lee, Seunghak Yu, Jaeyoung Do, Tara Taghavi

In this paper, we propose counterfactual explanation with text-driven concepts (CounTEX), where the concepts are defined only from text by leveraging a pre-trained multi-modal joint embedding space without additional concept-annotated datasets.

counterfactual Counterfactual Explanation

Debiasing Neighbor Aggregation for Graph Neural Network in Recommender Systems

no code implementations18 Aug 2022 Minseok Kim, Jinoh Oh, Jaeyoung Do, Sungjin Lee

Graph neural networks (GNNs) have achieved remarkable success in recommender systems by representing users and items based on their historical interactions.

Recommendation Systems

Accelerating Large-Scale Graph-based Nearest Neighbor Search on a Computational Storage Platform

no code implementations12 Jul 2022 Ji-Hoon Kim, Yeo-Reum Park, Jaeyoung Do, Soo-Young Ji, Joo-Young Kim

In this paper, we propose a computational storage platform that can accelerate a large-scale graph-based nearest neighbor search algorithm based on SmartSSD CSD.

ALEX: An Updatable Adaptive Learned Index

no code implementations21 May 2019 Jialin Ding, Umar Farooq Minhas, JIA YU, Chi Wang, Jaeyoung Do, Yi-Nan Li, Hantian Zhang, Badrish Chandramouli, Johannes Gehrke, Donald Kossmann, David Lomet, Tim Kraska

The original work by Kraska et al. shows that a learned index beats a B+Tree by a factor of up to three in search time and by an order of magnitude in memory footprint.

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