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Found 16 papers, 7 papers with code
Behavioral graph fraud detection in E-commerce
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.
Dance Style Transfer with Cross-modal Transformer
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.
Back to the Manifold: Recovering from Out-of-Distribution States
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
In this work we provide an analysis of the distribution of the post-adaptation parameters of Gradient-Based Meta-Learning (GBML) methods.
Geometric Multimodal Contrastive Representation Learning
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.
How to Sense the World: Leveraging Hierarchy in Multimodal Perception for Robust Reinforcement Learning Agents
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.
Benchmarking the Combinatorial Generalizability of Complex Query Answering on Knowledge Graphs
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.
On the Impact of Spurious Correlation for Out-of-distribution Detection
Modern neural networks can assign high confidence to inputs drawn from outside the training distribution, posing threats to models in real-world deployments.
Graph-based Normalizing Flow for Human Motion Generation and Reconstruction
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
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.
Robotics
Enabling Visual Action Planning for Object Manipulation through Latent Space Roadmap
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
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.
Explainable Deep Behavioral Sequence Clustering for Transaction Fraud Detection
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.
Latent Space Roadmap for Visual Action Planning of Deformable and Rigid Object Manipulation
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
Learning dynamics models is an essential component of model-based reinforcement learning.
Robotics
StableNet: Semi-Online, Multi-Scale Deep Video Stabilization
Video stabilization algorithms are of greater importance nowadays with the prevalence of hand-held devices which unavoidably produce videos with undesirable shaky motions.
