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Found 12 papers, 4 papers with code
A Deep Decomposable Model for Disentangling Syntax and Semantics in Sentence Representation
Recently, disentanglement based on a generative adversarial network or a variational autoencoder has significantly advanced the performance of diverse applications in CV and NLP domains.
Towards Invariant Time Series Forecasting in Smart Cities
In the transformative landscape of smart cities, the integration of the cutting-edge web technologies into time series forecasting presents a pivotal opportunity to enhance urban planning, sustainability, and economic growth.
Dynamic Incremental Optimization for Best Subset Selection
Best subset selection is considered the `gold standard' for many sparse learning problems.
Causal Bayesian Optimization via Exogenous Distribution Learning
A new CBO method is developed by leveraging the learned exogenous distribution.
Word Embedding with Neural Probabilistic Prior
To improve word representation learning, we propose a probabilistic prior which can be seamlessly integrated with word embedding models.
Flow-based Perturbation for Cause-effect Inference
A new causal discovery method is introduced to solve the bivariate causal discovery problem.
Variational Flow Graphical Model
VFGs learn the representation of high dimensional data via a message-passing scheme by integrating flow-based functions through variational inference.
Best Subset Selection with Efficient Primal-Dual Algorithm
Best subset selection is considered the `gold standard' for many sparse learning problems.
Causal Discovery with Flow-based Conditional Density Estimation
Then we formulate a novel evaluation metric to infer the scores for each potential causal direction based on the variance of the conditional density estimation.
Thunder: a Fast Coordinate Selection Solver for Sparse Learning
L1 regularization has been broadly employed to pursue model sparsity.
Estimate the Implicit Likelihoods of GANs with Application to Anomaly Detection
The thriving of deep models and generative models provides approaches to model high dimensional distributions.
Safe Active Feature Selection for Sparse Learning
More critically, SAIF has the safe guarantee as it has the convergence guarantee to the optimal solution to the original full LASSO problem.
