Search Results for author:
Found 9 papers, 5 papers with code
Towards Validating Long-Term User Feedbacks in Interactive Recommendation Systems
In this work, we revisited experiments on IRS with review datasets and compared RL-based models with a simple reward model that greedily recommends the item with the highest one-step reward.
ST-RAP: A Spatio-Temporal Framework for Real Estate Appraisal
In this paper, we introduce ST-RAP, a novel Spatio-Temporal framework for Real estate APpraisal.
On the Importance of Feature Decorrelation for Unsupervised Representation Learning in Reinforcement Learning
To address this issue, we propose a novel URL framework that causally predicts future states while increasing the dimension of the latent manifold by decorrelating the features in the latent space.
Enemy Spotted: in-game gun sound dataset for gunshot classification and localization
By exploiting the advantage of the game environment, we construct a gunshot dataset, namely BGG, for the firearm classification and gunshot localization tasks.
DraftRec: Personalized Draft Recommendation for Winning in Multi-Player Online Battle Arena Games
To alleviate this problem, we propose DraftRec, a novel hierarchical model which recommends characters by considering each player's champion preferences and the interaction between the players.
JORLDY: a fully customizable open source framework for reinforcement learning
Recently, Reinforcement Learning (RL) has been actively researched in both academic and industrial fields.
Confidential Machine Learning Computation in Untrusted Environments: A Systems Security Perspective
Hence, researchers have leveraged the Trusted Execution Environments (TEEs) to build confidential ML computation systems.
MOI-Mixer: Improving MLP-Mixer with Multi Order Interactions in Sequential Recommendation
Successful sequential recommendation systems rely on accurately capturing the user's short-term and long-term interest.
Learning Representations by Contrasting Clusters While Bootstrapping Instances
Learning visual representations using large-scale unlabelled images is a holy grail for most of computer vision tasks.
