Contributed Talks Part 2/2
2022 Contributed Talks
in
Workshop: Synthetic Data for Empowering ML Research
in
Workshop: Synthetic Data for Empowering ML Research
Abstract
Papers:
- SynBench: Task-Agnostic Benchmarking of Pretrained Representations using Synthetic Data
- MAQA: A Multimodal QA Benchmark for Negation
- Generating Realistic Synthetic Relational Data through Graph Variational Autoencoders
- Mitigating Health Data Poverty: Generative Approaches versus Resampling for Time-series Clinical Data
- Private GANs, Revisited
- Contrastive Learning on Synthetic Videos for GAN Latent Disentangling
- HandsOff: Labeled Dataset Generation with No Additional Human Annotations
- Secure Multiparty Computation for Synthetic Data Generation from Distributed Data
- Counterfactual Fairness in Synthetic Data Generation
- HyperTime: Implicit Neural Representations for Time Series
- Generic and Privacy-free Synthetic Data Generation for Pretraining GANs
- Importance of Synthesizing High-quality Data for Text-to-SQL Parsing
- Fast Learning of Multidimensional Hawkes Processes via Frank-Wolfe
- TAPAS: a Toolbox for Adversarial Privacy Auditing of Synthetic Data
- On the legal nature of synthetic data
- Synthesizing Informative Training Samples with GAN
- Unsupervised Anomaly Detection for Auditing Data and Impact of Categorical Encodings.
- C-GATS: Conditional Generation of Anomalous Time Series
- Medical Scientific Table-to-Text Generation with Synthetic Data under Data Sparsity Constraint
- Mind Your Step: Continuous Conditional GANs with Generator Regularization
- Federated Learning on Patient Data for Privacy-Protecting Polycystic Ovary Syndrome Treatment
- Random Walk based Conditional Generative Model for Temporal Networks with Attributes
- Generating High Fidelity Synthetic Data via Coreset selection and Entropic Regularization
- Conditional Progressive Generative Adversarial Network for satellite image generation
- Multi-Modal Conditional GAN: Data Synthesis in the Medical Domain
- Hypothesis Testing using Causal and Causal Variational Generative Models
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