In 2006, Geoffrey Hinton proposed the concept of training “Deep Neural Networks (DNNs)”
and an improved model training method to break the bottleneck of neural network …

Large language models (LLMs) have demonstrated tremendous capabilities in solving
complex tasks, from quantitative reasoning to understanding natural language. However …

Fundamental algorithms such as sorting or hashing are used trillions of times on any given
day. As demand for computation grows, it has become critical for these algorithms to be as …

Improving the efficiency of algorithms for fundamental computations can have a widespread
impact, as it can affect the overall speed of a large amount of computations. Matrix …

With the recent success of embeddings in natural language processing, research has been
conducted into applying similar methods to code analysis. Most works attempt to process the …

Due to its potential to improve programmer productivity and software quality, automated
program repair has been an active topic of research. Newer techniques harness neural …

Leveraging machine-learning (ML) techniques for compiler optimizations has been widely
studied and explored in academia. However, the adoption of ML in general-purpose …

Software optimization refines programs for resource efficiency while preserving functionality.
Traditionally, it is a process done by developers and compilers. This paper introduces a third …

Although large language models (LLMs) have been largely successful in generating
functionally correct programs, conditioning models to produce efficient solutions while …

A predictive compiler uses properties of a program to decide how to optimize it. The compiler
is trained on a collection of programs to derive a model which determines its actions in face …