As language models (LMs) become increasingly powerful, it is important t...
Large Language Models (LLMs) exhibit remarkable fluency and competence a...
Deep neural networks often learn unintended biases during training, whic...
Deep Neural Networks (DNNs) are being used to solve a wide range of prob...
While Visual Question Answering (VQA) has progressed rapidly, previous w...
To address the communication bottleneck problem in distributed optimizat...
We consider feature selection for applications in machine learning where...
Data Parallelism (DP) and Model Parallelism (MP) are two common paradigm...
Serverless computing platforms currently rely on basic pricing schemes t...
Inexpensive cloud services, such as serverless computing, are often
vuln...
We propose Stochastic Weight Averaging in Parallel (SWAP), an algorithm ...
Motivated by recent developments in serverless systems for large-scale
m...
We propose OverSketch, an approximate algorithm for distributed matrix
m...
Building on the previous work of Lee et al. and Ferdinand et al. on code...