Search Results for author: Hao Gong

Found 6 papers, 0 papers with code

Comparison of Methods in Human Skin Decomposition

no code implementations31 Mar 2024 Hao Gong, Michel Desvignes

Decomposition of skin pigment plays an important role in medical fields.

Dimensionality Reduction

UserBERT: Modeling Long- and Short-Term User Preferences via Self-Supervision

no code implementations14 Feb 2022 Tianyu Li, Ali Cevahir, Derek Cho, Hao Gong, DuyKhuong Nguyen, Bjorn Stenger

This paper extends the BERT model to e-commerce user data for pre-training representations in a self-supervised manner.

Representation Learning

UserBERT: Self-supervised User Representation Learning

no code implementations1 Jan 2021 Tianyu Li, Ali Cevahir, Derek Cho, Hao Gong, DuyKhuong Nguyen, Bjorn Stenger

This paper extends the BERT model to user data for pretraining user representations in a self-supervised way.

Multi-Task Learning Representation Learning

Learning to Profile: User Meta-Profile Network for Few-Shot Learning

no code implementations21 Aug 2020 Hao Gong, Qifang Zhao, Tianyu Li, Derek Cho, DuyKhuong Nguyen

1) Meta-learning model: In the context of representation learning with e-commerce user behavior data, we propose a meta-learning framework called the Meta-Profile Network, which extends the ideas of matching network and relation network for knowledge transfer and fast adaptation; 2) Encoding strategy: To keep high fidelity of large-scale long-term sequential behavior data, we propose a time-heatmap encoding strategy that allows the model to encode data effectively; 3) Deep network architecture: A multi-modal model combined with multi-task learning architecture is utilized to address the cross-domain knowledge learning and insufficient label problems.

Few-Shot Learning Multi-Task Learning +3

A Duality Approach for Regret Minimization in Average-Award Ergodic Markov Decision Processes

no code implementations L4DC 2020 Hao Gong, Mengdi Wang

In light of the Bellman duality, we propose a novel value-policy gradient algorithm to explore and act in infinite-horizon Average-reward Markov Decision Process (AMDP) and show that it has sublinear regret.

Learning low-dimensional state embeddings and metastable clusters from time series data

no code implementations NeurIPS 2019 Yifan Sun, Yaqi Duan, Hao Gong, Mengdi Wang

This paper studies how to find compact state embeddings from high-dimensional Markov state trajectories, where the transition kernel has a small intrinsic rank.

Clustering Time Series +1

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