Language models pretrained on large collections of tabular data have
dem...
Large language models of code (Code-LLMs) have recently brought tremendo...
Pretrained code language models have enabled great progress towards prog...
The use of multilingual language models for tasks in low and high-resour...
Differentially private (DP) optimization is the standard paradigm to lea...
We study the problem of differentially private (DP) fine-tuning of large...
Per-example gradient clipping is a key algorithmic step that enables
pra...
Recent work has found that multi-task training with a large number of di...
The goal of meta-learning is to learn to adapt to a new task with only a...
BERT has recently attracted a lot of attention in natural language
under...
Statistical natural language inference (NLI) models are susceptible to
l...
We present GluonCV and GluonNLP, the deep learning toolkits for computer...
With an increasing demand for training powers for deep learning algorith...
Batching is an essential technique to improve computation efficiency in ...
Visual Question Answering (VQA) requires integration of feature maps wit...