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Found 15 papers, 5 papers with code
Deep Successor Reinforcement Learning
The successor map represents the expected future state occupancy from any given state and the reward predictor maps states to scalar rewards.
Nonparametric Spherical Topic Modeling with Word Embeddings
In this paper, we propose to use the von Mises-Fisher distribution to model the density of words over a unit sphere.
Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation
Learning goal-directed behavior in environments with sparse feedback is a major challenge for reinforcement learning algorithms.
Learning From Noisy Labels By Regularized Estimation Of Annotator Confusion
We provide a theoretical argument as to how the regularization is essential to our approach both for the case of single annotator and multiple annotators.
Treatment-RSPN: Recurrent Sum-Product Networks for Sequential Treatment Regimes
Models developed using our framework benefit from the full range of RSPN capabilities, including the abilities to model the full distribution of the data, to seamlessly handle latent variables, missing values and categorical data, and to efficiently perform marginal and conditional inference.
Multimodal Prediction and Personalization of Photo Edits with Deep Generative Models
Professional-grade software applications are powerful but complicated$-$expert users can achieve impressive results, but novices often struggle to complete even basic tasks.
The Segmented iHMM: A Simple, Efficient Hierarchical Infinite HMM
We propose the segmented iHMM (siHMM), a hierarchical infinite hidden Markov model (iHMM) that supports a simple, efficient inference scheme.
Variational Particle Approximations
Like Monte Carlo, DPVI can handle multiple modes, and yields exact results in a well-defined limit.
JUMP-Means: Small-Variance Asymptotics for Markov Jump Processes
We derive the small-variance asymptotics for parametric and nonparametric MJPs for both directly observed and hidden state models.
Automatic Inference for Inverting Software Simulators via Probabilistic Programming
Models of complex systems are often formalized as sequential software simulators: computationally intensive programs that iteratively build up probable system configurations given parameters and initial conditions.
Detailed Derivations of Small-Variance Asymptotics for some Hierarchical Bayesian Nonparametric Models
In this note we provide detailed derivations of two versions of small-variance asymptotics for hierarchical Dirichlet process (HDP) mixture models and the HDP hidden Markov model (HDP-HMM, a. k. a.
ExplainGAN: Model Explanation via Decision Boundary Crossing Transformations
We introduce a new method for interpreting computer vision models: visually perceptible, decision-boundary crossing transformations.
Discrepancy Ratio: Evaluating Model Performance When Even Experts Disagree on the Truth
In most machine learning tasks unambiguous ground truth labels can easily be acquired.
A Knowledge Distillation Approach for Sepsis Outcome Prediction from Multivariate Clinical Time Series
In this work, we use knowledge distillation via constrained variational inference to distill the knowledge of a powerful "teacher" neural network model with high predictive power to train a "student" latent variable model to learn interpretable hidden state representations to achieve high predictive performance for sepsis outcome prediction.
XAIQA: Explainer-Based Data Augmentation for Extractive Question Answering
Our method uses the idea of a classification model explainer to generate questions and answers about medical concepts corresponding to medical codes.
