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Found 5 papers, 2 papers with code
Knowledge Transfer from High-Resource to Low-Resource Programming Languages for Code LLMs
We apply this approach to generate tens of thousands of validated training items for Julia, Lua, OCaml, R, and Racket.
MultiPL-E: A Scalable and Extensible Approach to Benchmarking Neural Code Generation
Using these new parallel benchmarks, we evaluate the multi-language performance of three state-of-the-art code generation models: Codex, CodeGen, and InCoder.
Breaking the Computation and Communication Abstraction Barrier in Distributed Machine Learning Workloads
Therefore, we present CoCoNeT, with a DSL to express a program with both computation and communication.
Accelerating Graph Sampling for Graph Machine Learning using GPUs
Several representation learning algorithms for graph data, such as DeepWalk, node2vec, and GraphSAGE, sample the graph to produce mini-batches that are suitable for training a DNN.
Predicting Variable Types in Dynamically Typed Programming Languages
We predict the types of all the identifiers given the Abstract Syntax Tree by performing just two passes over the tree, bottom-up and top-down, keeping both the content and context representation for all the nodes of the tree.
