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Found 10 papers, 5 papers with code
On the Efficient Marginalization of Probabilistic Sequence Models
Real-world data often exhibits sequential dependence, across diverse domains such as human behavior, medicine, finance, and climate modeling.
Probabilistic Modeling for Sequences of Sets in Continuous-Time
In this work, we develop a general framework for modeling set-valued data in continuous-time, compatible with any intensity-based recurrent neural point process model.
Bayesian Online Learning for Consensus Prediction
Given a pre-trained classifier and multiple human experts, we investigate the task of online classification where model predictions are provided for free but querying humans incurs a cost.
Understanding Pathologies of Deep Heteroskedastic Regression
Deep, overparameterized regression models are notorious for their tendency to overfit.
Probabilistic Querying of Continuous-Time Event Sequences
Continuous-time event sequences, i. e., sequences consisting of continuous time stamps and associated event types ("marks"), are an important type of sequential data with many applications, e. g., in clinical medicine or user behavior modeling.
Predictive Querying for Autoregressive Neural Sequence Models
In reasoning about sequential events it is natural to pose probabilistic queries such as "when will event A occur next" or "what is the probability of A occurring before B", with applications in areas such as user modeling, medicine, and finance.
Structured Stochastic Gradient MCMC
Since practitioners face speed versus accuracy tradeoffs in these models, variational inference (VI) is often the preferable option.
Detecting and Adapting to Irregular Distribution Shifts in Bayesian Online Learning
We consider the problem of online learning in the presence of distribution shifts that occur at an unknown rate and of unknown intensity.
User-Dependent Neural Sequence Models for Continuous-Time Event Data
Modeling such data can be very challenging, in particular for applications with many different types of events, since it requires a model to predict the event types as well as the time of occurrence.
Large Scale Multi-Actor Generative Dialog Modeling
This work introduces the Generative Conversation Control model, an augmented and fine-tuned GPT-2 language model that conditions on past reference conversations to probabilistically model multi-turn conversations in the actor's persona.
