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Found 10 papers, 3 papers with code
Solving Data-centric Tasks using Large Language Models
Large language models (LLMs) are rapidly replacing help forums like StackOverflow, and are especially helpful for non-professional programmers and end users.
Co-audit: tools to help humans double-check AI-generated content
Hence, we are seeing the emergence of tool-assisted experiences to help the user double-check a piece of AI-generated content.
What is it like to program with artificial intelligence?
Large language models, such as OpenAI's codex and Deepmind's AlphaCode, can generate code to solve a variety of problems expressed in natural language.
Type Inference as Optimization
We are the first to present a detailed algorithm for NTI that is validated with theorems and proofs.
Conditional independence by typing
The resulting program can be seen as a hybrid inference algorithm on the original program, where continuous parameters can be drawn using efficient gradient-based inference methods, while the discrete parameters are inferred using variable elimination.
OptTyper: Probabilistic Type Inference by Optimising Logical and Natural Constraints
OptTyper combines a continuous interpretation of logical constraints derived by classical static analysis of TypeScript code, with natural constraints obtained from a deep learning model, which learns naming conventions for types from a large codebase.
Probabilistic Programming with Densities in SlicStan: Efficient, Flexible and Deterministic
Stan is a probabilistic programming language that has been increasingly used for real-world scalable projects.
Deriving Probability Density Functions from Probabilistic Functional Programs
The probability density function of a probability distribution is a fundamental concept in probability theory and a key ingredient in various widely used machine learning methods.
A Lambda-Calculus Foundation for Universal Probabilistic Programming
Our second contribution is to formalize the implementation technique of trace Markov chain Monte Carlo (MCMC) for our calculus and to show its correctness.
Programming Languages
Measure Transformer Semantics for Bayesian Machine Learning
The Bayesian approach to machine learning amounts to computing posterior distributions of random variables from a probabilistic model of how the variables are related (that is, a prior distribution) and a set of observations of variables.
