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Found 9 papers, 4 papers with code
Lifelong Generative Modeling
Lifelong learning is the problem of learning multiple consecutive tasks in a sequential manner, where knowledge gained from previous tasks is retained and used to aid future learning over the lifetime of the learner.
GrannGAN: Graph annotation generative adversarial networks
The model we propose tackles the problem of generating the data features constrained by the specific graph structure of each data point by splitting the task into two phases.
Learning Predictive Leading Indicators for Forecasting Time Series Systems with Unknown Clusters of Forecast Tasks
We present a new method for forecasting systems of multiple interrelated time series.
Learning Leading Indicators for Time Series Predictions
We consider the problem of learning models for forecasting multiple time-series systems together with discovering the leading indicators that serve as good predictors for the system.
Continual Classification Learning Using Generative Models
Continual learning is the ability to sequentially learn over time by accommodating knowledge while retaining previously learned experiences.
Sparse Learning for Variable Selection with Structures and Nonlinearities
In this thesis we discuss machine learning methods performing automated variable selection for learning sparse predictive models.
Permutation Equivariant Generative Adversarial Networks for Graphs
One solution consists of using equivariant generative functions, which ensure the ordering invariance.
Discrete Graph Auto-Encoder
Despite advances in generative methods, accurately modeling the distribution of graphs remains a challenging task primarily because of the absence of predefined or inherent unique graph representation.
Diffusion-based Visual Counterfactual Explanations -- Towards Systematic Quantitative Evaluation
Latest methods for visual counterfactual explanations (VCE) harness the power of deep generative models to synthesize new examples of high-dimensional images of impressive quality.
