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Found 10 papers, 2 papers with code
An Eager Satisfiability Modulo Theories Solver for Algebraic Datatypes
Specifically, our solver reduces ADT queries to a simpler logical theory, uninterpreted functions (UF), and then uses an existing solver on the reduced query.
Recovery of Meteorites Using an Autonomous Drone and Machine Learning
Those images can be analyzed using a machine learning classifier to identify meteorites in the field among many other features.
Urban Driving with Conditional Imitation Learning
As our main contribution, we present an end-to-end conditional imitation learning approach, combining both lateral and longitudinal control on a real vehicle for following urban routes with simple traffic.
Learning to Drive in a Day
We demonstrate the first application of deep reinforcement learning to autonomous driving.
Parallel Predictive Entropy Search for Batch Global Optimization of Expensive Objective Functions
We develop parallel predictive entropy search (PPES), a novel algorithm for Bayesian optimization of expensive black-box objective functions.
Unitary Evolution Recurrent Neural Networks
When the eigenvalues of the hidden to hidden weight matrix deviate from absolute value 1, optimization becomes difficult due to the well studied issue of vanishing and exploding gradients, especially when trying to learn long-term dependencies.
Ranked #26 on
Sequential Image Classification
on Sequential MNIST
Predictive Entropy Search for Multi-objective Bayesian Optimization
The results show that PESMO produces better recommendations with a smaller number of evaluations of the objectives, and that a decoupled evaluation can lead to improvements in performance, particularly when the number of objectives is large.
An Empirical Study of Stochastic Variational Algorithms for the Beta Bernoulli Process
Stochastic variational inference (SVI) is emerging as the most promising candidate for scaling inference in Bayesian probabilistic models to large datasets.
Student-t Processes as Alternatives to Gaussian Processes
We investigate the Student-t process as an alternative to the Gaussian process as a nonparametric prior over functions.
Determinantal Clustering Processes - A Nonparametric Bayesian Approach to Kernel Based Semi-Supervised Clustering
Semi-supervised clustering is the task of clustering data points into clusters where only a fraction of the points are labelled.
