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Found 8 papers, 5 papers with code
Recent Advances in Neural Program Synthesis
In recent years, deep learning has made tremendous progress in a number of fields that were previously out of reach for artificial intelligence.
Practical Text Classification With Large Pre-Trained Language Models
Multi-emotion sentiment classification is a natural language processing (NLP) problem with valuable use cases on real-world data.
Ranked #3 on
Emotion Classification
on SemEval 2018 Task 1E-c
(Macro-F1 metric)
Adversarial Policies: Attacking Deep Reinforcement Learning
Deep reinforcement learning (RL) policies are known to be vulnerable to adversarial perturbations to their observations, similar to adversarial examples for classifiers.
Synthetic Datasets for Neural Program Synthesis
The goal of program synthesis is to automatically generate programs in a particular language from corresponding specifications, e. g. input-output behavior.
End-to-End Training of Neural Retrievers for Open-Domain Question Answering
We also explore two approaches for end-to-end supervised training of the reader and retriever components in OpenQA models.
PrefixRL: Optimization of Parallel Prefix Circuits using Deep Reinforcement Learning
Deep Convolutional RL agents trained on this environment produce prefix adder circuits that Pareto-dominate existing baselines with up to 16. 0% and 30. 2% lower area for the same delay in the 32b and 64b settings respectively.
HelpSteer: Multi-attribute Helpfulness Dataset for SteerLM
To alleviate this problem, we collect HelpSteer, a multi-attribute helpfulness dataset annotated for the various aspects that make responses helpful.
Polaris: A Safety-focused LLM Constellation Architecture for Healthcare
We train our models on proprietary data, clinical care plans, healthcare regulatory documents, medical manuals, and other medical reasoning documents.
