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Found 17 papers, 5 papers with code
3D-Prover: Diversity Driven Theorem Proving With Determinantal Point Processes
We first demonstrate that it is possible to generate semantically aware tactic representations which capture the effect on the proving environment, likelihood of success and execution time.
Approximate Nearest Neighbour Search on Dynamic Datasets: An Investigation
For online feature learning, the Scalable Nearest Neighbours method is faster than baseline for recall rates below 75%.
BAIT: Benchmarking (Embedding) Architectures for Interactive Theorem-Proving
We also provide a qualitative analysis, illustrating that improved performance is associated with more semantically-aware embeddings.
Statistically Efficient Bayesian Sequential Experiment Design via Reinforcement Learning with Cross-Entropy Estimators
We propose the use of an alternative estimator based on the cross-entropy of the joint model distribution and a flexible proposal distribution.
Transformed Distribution Matching for Missing Value Imputation
The key to missing value imputation is to capture the data distribution with incomplete samples and impute the missing values accordingly.
The Contextual Lasso: Sparse Linear Models via Deep Neural Networks
With this capability gap in mind, we study a not-uncommon situation where the input features dichotomize into two groups: explanatory features, which are candidates for inclusion as variables in an interpretable model, and contextual features, which select from the candidate variables and determine their effects.
Bayesian Optimisation for Mixed-Variable Inputs using Value Proposals
Many real-world optimisation problems are defined over both categorical and continuous variables, yet efficient optimisation methods such asBayesian Optimisation (BO) are not designed tohandle such mixed-variable search spaces.
Optimizing Sequential Experimental Design with Deep Reinforcement Learning
Bayesian approaches developed to solve the optimal design of sequential experiments are mathematically elegant but computationally challenging.
TacticZero: Learning to Prove Theorems from Scratch with Deep Reinforcement Learning
We propose a novel approach to interactive theorem-proving (ITP) using deep reinforcement learning.
EAPS: Edge-Assisted Predictive Sleep Scheduling for 802.11 IoT Stations
Unfortunately, the main energy efficiency mechanisms of 802. 11, namely PSM and APSD, fall short when used in IoT applications.
Disentangled behavioural representations
Individual characteristics in human decision-making are often quantified by fitting a parametric cognitive model to subjects' behavior and then studying differences between them in the associated parameter space.
Integrated accounts of behavioral and neuroimaging data using flexible recurrent neural network models
One standard approach to this is model-based fMRI data analysis, in which a model is fitted to the behavioral data, i. e., a subject's choices, and then the neural data are parsed to find brain regions whose BOLD signals are related to the model's internal signals.
Variational Network Inference: Strong and Stable with Concrete Support
Traditional methods for the discovery of latent network structures are limited in two ways: they either assume that all the signal comes from the network (i. e. there is no source of signal outside the network) or they place constraints on the network parameters to ensure model or algorithmic stability.
Semi-parametric Network Structure Discovery Models
We propose a network structure discovery model for continuous observations that generalizes linear causal models by incorporating a Gaussian process (GP) prior on a network-independent component, and random sparsity and weight matrices as the network-dependent parameters.
Gray-box inference for structured Gaussian process models
We develop an automated variational inference method for Bayesian structured prediction problems with Gaussian process (GP) priors and linear-chain likelihoods.
Generic Inference in Latent Gaussian Process Models
We evaluate our approach quantitatively and qualitatively with experiments on small datasets, medium-scale datasets and large datasets, showing its competitiveness under different likelihood models and sparsity levels.
Scalable Inference for Gaussian Process Models with Black-Box Likelihoods
We propose a sparse method for scalable automated variational inference (AVI) in a large class of models with Gaussian process (GP) priors, multiple latent functions, multiple outputs and non-linear likelihoods.
