Morpheus exposes the potential allocative harms of popular pretrained NLP models by simulating inflectional variation. We propose adversarial fine-tuning for mitigating the effects of training only on error-free Standard English data.

Word embeddings inherit strong gender bias in data which can be further amplified by downstream models. We propose to purify word embeddings against corpus regularities such as word frequency prior to inferring and removing the gender subspace, which significantly improves the debiasing performance.

Prototypical Contrastive Learning unifies clustering and contrastive self-supervised learning to push the frontiers of unsupervised learning.

We show that deep neural models can describe common sense physics in a valid and sufficient way that is also generalizable. Our ESPRIT framework is trained on a new dataset with physics simulations and descriptions that we collected and have open-sourced.

SummaryWe investigate NVIDIA's Triton (TensorRT) Inference Server as a way of hosting Transformer Language Models. The blog is roughly divided into two parts: (i) instructions for setting up your own inference server, and (ii) benchmarking experiments. The instructions are intended to be detailed and standalone, but readers interested solely in

Learn about the ethical implications of voice for business and how to make them an operational and strategic priority now—before you’re too far down the path.