Search Results for author:
Found 14 papers, 6 papers with code
Value of Exploration: Measurements, Findings and Algorithms
We conduct extensive live experiments on a large-scale commercial recommendation platform that serves billions of users to validate the new experiment designs, quantify the long-term values of exploration, and to verify the effectiveness of the adopted neural linear bandit algorithm for exploration.
Learning Disentangled Representations for Time Series
Motivated by the success of disentangled representation learning in computer vision, we study the possibility of learning semantic-rich time-series representations, which remains unexplored due to three main challenges: 1) sequential data structure introduces complex temporal correlations and makes the latent representations hard to interpret, 2) sequential models suffer from KL vanishing problem, and 3) interpretable semantic concepts for time-series often rely on multiple factors instead of individuals.
Probabilistic Simplex Component Analysis
PRISM uses a simple probabilistic model, namely, uniform simplex data distribution and additive Gaussian noise, and it carries out inference by maximum likelihood.
AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning
Outlier detection is an important data mining task with numerous practical applications such as intrusion detection, credit card fraud detection, and video surveillance.
Mitigating Gender Bias in Captioning Systems
Image captioning has made substantial progress with huge supporting image collections sourced from the web.
Towards Deeper Graph Neural Networks with Differentiable Group Normalization
Graph neural networks (GNNs), which learn the representation of a node by aggregating its neighbors, have become an effective computational tool in downstream applications.
Dual Policy Distillation
In this work, we introduce dual policy distillation(DPD), a student-student framework in which two learners operate on the same environment to explore different perspectives of the environment and extract knowledge from each other to enhance their learning.
PyODDS: An End-to-end Outlier Detection System with Automated Machine Learning
Outlier detection is an important task for various data mining applications.
XDeep: An Interpretation Tool for Deep Neural Networks
XDeep is an open-source Python package developed to interpret deep models for both practitioners and researchers.
PyODDS: An End-to-End Outlier Detection System
PyODDS is an end-to end Python system for outlier detection with database support.
Towards Generalizable Deepfake Detection with Locality-aware AutoEncoder
The analysis further shows that LAE outperforms the state-of-the-arts by 6. 52%, 12. 03%, and 3. 08% respectively on three deepfake detection tasks in terms of generalization accuracy on previously unseen manipulations.
SpecAE: Spectral AutoEncoder for Anomaly Detection in Attributed Networks
SpecAE leverages Laplacian sharpening to amplify the distances between representations of anomalies and the ones of the majority.
Deep Structured Cross-Modal Anomaly Detection
To this end, we propose a novel deep structured anomaly detection framework to identify the cross-modal anomalies embedded in the data.
Is a Single Vector Enough? Exploring Node Polysemy for Network Embedding
Network embedding models are powerful tools in mapping nodes in a network into continuous vector-space representations in order to facilitate subsequent tasks such as classification and link prediction.
