ReceptorNet: Breast Cancer Therapy Decisions using AI
Nikhil Naik · #artificial intelligenceAI makes breast cancer therapy decisions more accurate, affordable and accessible using visual clues in pathology images invisible to the eye
ReceptorNet: Breast Cancer Therapy Decisions using AI
Nikhil Naik · #artificial intelligenceAI makes breast cancer therapy decisions more accurate, affordable and accessible using visual clues in pathology images invisible to the eye
Talk to Your Data: One Model, Any Relational Database
Victoria Lin · #natural language interfaceWe introduce Photon, a live demo of natural language interface to databases based on our latest research in neural semantic parsing. đź”— https://naturalsql.com/
How Salesforce Infuses Ethics into its AI
Katherine Siu · #artificial intelligenceFor all the good that AI can bring, responsible tech companies understand they must recognize, prepare for, and mitigate the potential unintended, harmful effects. That’s why Salesforce sees ethics as foundational to AI — and why we’re sharing a closer look at how we infuse an ethical process into
It’s Morphin’ Time! Combating Linguistic Discrimination with Inflectional Perturbations
Samson Tan · #researchMorpheus 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.
Double Hard-Debias: Tailoring Word Embeddings for Gender Bias Mitigation
Tianlu Wang · #researchWord 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: Pushing the Frontiers of Unsupervised Learning
Junnan Li · #artificial intelligencePrototypical Contrastive Learning unifies clustering and contrastive self-supervised learning to push the frontiers of unsupervised learning.
Explaining Solutions to Physical Reasoning Tasks
Nazneen Rajani · #researchWe 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.
Benchmarking Triton (TensorRT) Inference Server for Transformer Models
Nitish Shirish Keskar · #engineeringSummaryWe 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
Why Ethics Is Priority One for Making Voice Assistants Work in the Enterprise
Kathy Baxter · #ethicsLearn 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.