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Found 11 papers, 2 papers with code
AIRIVA: A Deep Generative Model of Adaptive Immune Repertoires
Recent advances in immunomics have shown that T-cell receptor (TCR) signatures can accurately predict active or recent infection by leveraging the high specificity of TCR binding to disease antigens.
Repairing Neural Networks by Leaving the Right Past Behind
Prediction failures of machine learning models often arise from deficiencies in training data, such as incorrect labels, outliers, and selection biases.
Preferential Mixture-of-Experts: Interpretable Models that Rely on Human Expertise as much as Possible
We propose Preferential MoE, a novel human-ML mixture-of-experts model that augments human expertise in decision making with a data-based classifier only when necessary for predictive performance.
Towards Expressive Priors for Bayesian Neural Networks: Poisson Process Radial Basis Function Networks
While Bayesian neural networks have many appealing characteristics, current priors do not easily allow users to specify basic properties such as expected lengthscale or amplitude variance.
Challenges in Computing and Optimizing Upper Bounds of Marginal Likelihood based on Chi-Square Divergences
Variational inference based on chi-square divergence minimization (CHIVI) provides a way to approximate a model's posterior while obtaining an upper bound on the marginal likelihood.
Output-Constrained Bayesian Neural Networks
Bayesian neural network (BNN) priors are defined in parameter space, making it hard to encode prior knowledge expressed in function space.
Unsupervised Extraction of Phenotypes from Cancer Clinical Notes for Association Studies
To demonstrate the utility of our approach, we perform an association study of clinical features with somatic mutation profiles from 4, 007 cancer patients and their tumors.
Projected BNNs: Avoiding weight-space pathologies by learning latent representations of neural network weights
As machine learning systems get widely adopted for high-stake decisions, quantifying uncertainty over predictions becomes crucial.
Sparse Three-parameter Restricted Indian Buffet Process for Understanding International Trade
This paper presents a Bayesian nonparametric latent feature model specially suitable for exploratory analysis of high-dimensional count data.
General Latent Feature Modeling for Data Exploration Tasks
This paper introduces a general Bayesian non- parametric latent feature model suitable to per- form automatic exploratory analysis of heterogeneous datasets, where the attributes describing each object can be either discrete, continuous or mixed variables.
General Latent Feature Models for Heterogeneous Datasets
Second, its Bayesian nonparametric nature allows us to automatically infer the model complexity from the data, i. e., the number of features necessary to capture the latent structure in the data.
