29 citations · 36 across the 5 of their papers we have counts for
6 papers
Automated Code generation for Information Technology Tasks in YAML through Large Language Models
Saurabh Pujar, Luca Buratti, Xiaojie Guo +8
The recent improvement in code generation capabilities due to the use of large language models has mainly benefited general purpose programming languages. Domain specific languages…
CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks
Ruchir Puri, David S. Kung, Geert Janssen +14
Over the last several decades, software has been woven into the fabric of every aspect of our society. As software development surges and code infrastructure of enterprise applicat…
Model Agnostic Contrastive Explanations for Structured Data
Amit Dhurandhar, Tejaswini Pedapati, Avinash Balakrishnan +3
Recently, a method [7] was proposed to generate contrastive explanations for differentiable models such as deep neural networks, where one has complete access to the model. In this…
NeuNetS: An Automated Synthesis Engine for Neural Network Design
Atin Sood, Benjamin Elder, Benjamin Herta +17
Application of neural networks to a vast variety of practical applications is transforming the way AI is applied in practice. Pre-trained neural network models available through AP…
IBM Deep Learning Service
Bishwaranjan Bhattacharjee, Scott Boag, Chandani Doshi +15
Deep learning driven by large neural network models is overtaking traditional machine learning methods for understanding unstructured and perceptual data domains such as speech, te…
A Proximity Measure using Blink Model
Haifeng Qian, Hui Wan, Mark N. Wegman +2
This paper proposes a new graph proximity measure. This measure is a derivative of network reliability. By analyzing its properties and comparing it against other proximity measure…