no code implementations • ICCV 2023 • Erdong Hu, Yuxin Tang, Anastasios Kyrillidis, Chris Jermaine
We carefully evaluate a number of algorithms for learning in a federated environment, and test their utility for a variety of image classification tasks.
no code implementations • 31 May 2023 • Yuxin Tang, Zhimin Ding, Dimitrije Jankov, Binhang Yuan, Daniel Bourgeois, Chris Jermaine
The relational data model was designed to facilitate large-scale data management and analytics.
no code implementations • 25 May 2023 • Abhinav Jain, Chima Adiole, Swarat Chaudhuri, Thomas Reps, Chris Jermaine
Our experiments show that RLCF raises the odds that an LLM-generated program compiles, is executable, and produces the right output on tests, often allowing LLMs to match the performance of 2x-8x larger LLMs.
no code implementations • 28 Oct 2022 • Qihan Wang, Chen Dun, Fangshuo Liao, Chris Jermaine, Anastasios Kyrillidis
\textsc{LoFT} is a model-parallel pretraining algorithm that partitions convolutional layers by filters to train them independently in a distributed setting, resulting in reduced memory and communication costs during pretraining.
no code implementations • 28 Oct 2022 • Chen Dun, Mirian Hipolito, Chris Jermaine, Dimitrios Dimitriadis, Anastasios Kyrillidis
Asynchronous learning protocols have regained attention lately, especially in the Federated Learning (FL) setup, where slower clients can severely impede the learning process.
no code implementations • NeurIPS 2021 • Rohan Mukherjee, Yeming Wen, Dipak Chaudhari, Thomas W. Reps, Swarat Chaudhuri, Chris Jermaine
State-of-the-art neural models of source code tend to be evaluated on the generation of individual expressions and lines of code, and commonly fail on long-horizon tasks such as the generation of entire method bodies.
1 code implementation • ICCV 2021 • Arkabandhu Chowdhury, Mingchao Jiang, Swarat Chaudhuri, Chris Jermaine
Recent papers have suggested that transfer learning can outperform sophisticated meta-learning methods for few-shot image classification.
no code implementations • 1 Sep 2020 • Binhang Yuan, Dimitrije Jankov, Jia Zou, Yuxin Tang, Daniel Bourgeois, Chris Jermaine
This implementation abstraction provides little built-in support for ML systems to scale past a single machine, or for handling large models with matrices or tensors that do not easily fit into the RAM of an ASIC.
1 code implementation • 17 Apr 2020 • Arkabandhu Chowdhury, Dipak Chaudhari, Swarat Chaudhuri, Chris Jermaine
We present a new approach, called meta-meta classification, to learning in small-data settings.
no code implementations • 25 Apr 2019 • Dimitrije Jankov, Shangyu Luo, Binhang Yuan, Zhuhua Cai, Jia Zou, Chris Jermaine, Zekai J. Gao
A number of popular systems, most notably Google's TensorFlow, have been implemented from the ground up to support machine learning tasks.
1 code implementation • ICLR 2018 • Vijayaraghavan Murali, Letao Qi, Swarat Chaudhuri, Chris Jermaine
We study the problem of generating source code in a strongly typed, Java-like programming language, given a label (for example a set of API calls or types) carrying a small amount of information about the code that is desired.