Search Results for author: Hang Yin

Found 16 papers, 7 papers with code

Behavioral graph fraud detection in E-commerce

no code implementations13 Oct 2022 Hang Yin, Zitao Zhang, Zhurong Wang, Yilmazcan Ozyurt, Weiming Liang, Wenyu Dong, Yang Zhao, Yinan Shan

Our experiments show that embedding features learned from similarity based behavioral graph have achieved significant performance increase to the baseline fraud detection model in various business scenarios.

Fraud Detection graph construction +1

Dance Style Transfer with Cross-modal Transformer

no code implementations19 Aug 2022 Wenjie Yin, Hang Yin, Kim Baraka, Danica Kragic, Mårten Björkman

We present CycleDance, a dance style transfer system to transform an existing motion clip in one dance style to a motion clip in another dance style while attempting to preserve motion context of the dance.

Style Transfer

Back to the Manifold: Recovering from Out-of-Distribution States

no code implementations18 Jul 2022 Alfredo Reichlin, Giovanni Luca Marchetti, Hang Yin, Ali Ghadirzadeh, Danica Kragic

However, a major challenge is a distributional shift between the states in the training dataset and the ones visited by the learned policy at the test time.

On the Subspace Structure of Gradient-Based Meta-Learning

no code implementations8 Jul 2022 Gustaf Tegnér, Alfredo Reichlin, Hang Yin, Mårten Björkman, Danica Kragic

In this work we provide an analysis of the distribution of the post-adaptation parameters of Gradient-Based Meta-Learning (GBML) methods.

Few-Shot Learning Image Classification +1

Geometric Multimodal Contrastive Representation Learning

1 code implementation7 Feb 2022 Petra Poklukar, Miguel Vasco, Hang Yin, Francisco S. Melo, Ana Paiva, Danica Kragic

Learning representations of multimodal data that are both informative and robust to missing modalities at test time remains a challenging problem due to the inherent heterogeneity of data obtained from different channels.

reinforcement Learning Representation Learning

How to Sense the World: Leveraging Hierarchy in Multimodal Perception for Robust Reinforcement Learning Agents

1 code implementation7 Oct 2021 Miguel Vasco, Hang Yin, Francisco S. Melo, Ana Paiva

This work addresses the problem of sensing the world: how to learn a multimodal representation of a reinforcement learning agent's environment that allows the execution of tasks under incomplete perceptual conditions.

Atari Games reinforcement-learning +1

Benchmarking the Combinatorial Generalizability of Complex Query Answering on Knowledge Graphs

2 code implementations18 Sep 2021 ZiHao Wang, Hang Yin, Yangqiu Song

Besides, our work, for the first time, provides a benchmark to evaluate and analyze the impact of different operators and normal forms by using (a) 7 choices of the operator systems and (b) 9 forms of complex queries.

Complex Query Answering

On the Impact of Spurious Correlation for Out-of-distribution Detection

1 code implementation12 Sep 2021 Yifei Ming, Hang Yin, Yixuan Li

Modern neural networks can assign high confidence to inputs drawn from outside the training distribution, posing threats to models in real-world deployments.

OOD Detection Out-of-Distribution Detection

Graph-based Normalizing Flow for Human Motion Generation and Reconstruction

no code implementations7 Apr 2021 Wenjie Yin, Hang Yin, Danica Kragic, Mårten Björkman

Data-driven approaches for modeling human skeletal motion have found various applications in interactive media and social robotics.

Graph-based Task-specific Prediction Models for Interactions between Deformable and Rigid Objects

1 code implementation4 Mar 2021 Zehang Weng, Fabian Paus, Anastasiia Varava, Hang Yin, Tamim Asfour, Danica Kragic

In an ablation study, we show the benefits of the two-stage model for single time step prediction and the effectiveness of the mixed-horizon model for long-term prediction tasks.


Enabling Visual Action Planning for Object Manipulation through Latent Space Roadmap

1 code implementation3 Mar 2021 Martina Lippi, Petra Poklukar, Michael C. Welle, Anastasia Varava, Hang Yin, Alessandro Marino, Danica Kragic

We present a framework for visual action planning of complex manipulation tasks with high-dimensional state spaces, focusing on manipulation of deformable objects.

Gaussian Mixture Graphical Lasso with Application to Edge Detection in Brain Networks

no code implementations13 Jan 2021 Hang Yin, Xinyue Liu, Xiangnan Kong

Existing works mainly focus on unimodal distributions, where it is usually assumed that the observed activities aregenerated from asingleGaussian distribution (i. e., one graph). However, this assumption is too strong for many real-worldapplications.

Edge Detection

Explainable Deep Behavioral Sequence Clustering for Transaction Fraud Detection

no code implementations12 Jan 2021 Wei Min, Weiming Liang, Hang Yin, Zhurong Wang, Mei Li, Alok Lal

To utilize the behavior sequence data, we treat click stream data as event sequence, use time attention based Bi-LSTM to learn the sequence embedding in an unsupervised fashion, and combine them with intuitive features generated by risk experts to form a hybrid feature representation.

Fraud Detection Management

Latent Space Roadmap for Visual Action Planning of Deformable and Rigid Object Manipulation

1 code implementation19 Mar 2020 Martina Lippi, Petra Poklukar, Michael C. Welle, Anastasiia Varava, Hang Yin, Alessandro Marino, Danica Kragic

We present a framework for visual action planning of complex manipulation tasks with high-dimensional state spaces such as manipulation of deformable objects.

Probabilistic Model Learning and Long-term Prediction for Contact-rich Manipulation Tasks

no code implementations11 Sep 2019 Shahbaz Abdul Khader, Hang Yin, Pietro Falco, Danica Kragic

Learning dynamics models is an essential component of model-based reinforcement learning.


StableNet: Semi-Online, Multi-Scale Deep Video Stabilization

no code implementations24 Jul 2019 Chia-Hung Huang, Hang Yin, Yu-Wing Tai, Chi-Keung Tang

Video stabilization algorithms are of greater importance nowadays with the prevalence of hand-held devices which unavoidably produce videos with undesirable shaky motions.

Video Stabilization

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