Search Results for author:
Found 16 papers, 11 papers with code
WHOSe Heritage: Classification of UNESCO World Heritage Statements of "Outstanding Universal Value” with Soft Labels
A human study with expert evaluation on the model prediction shows that the models are sufficiently generalizable.
Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine
We find that prompting innovation can unlock deeper specialist capabilities and show that GPT-4 easily tops prior leading results for medical benchmarks.
Ranked #1 on
Question Answering
on MedQA
BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining
Pre-trained language models have attracted increasing attention in the biomedical domain, inspired by their great success in the general natural language domain.
Ranked #1 on
Document Classification
on HOC
(Micro F1 metric)
Analyzing and Mitigating Interference in Neural Architecture Search
In this paper, we investigate the interference issue by sampling different child models and calculating the gradient similarity of shared operators, and observe: 1) the interference on a shared operator between two child models is positively correlated with the number of different operators; 2) the interference is smaller when the inputs and outputs of the shared operator are more similar.
A Survey on Low-Resource Neural Machine Translation
Neural approaches have achieved state-of-the-art accuracy on machine translation but suffer from the high cost of collecting large scale parallel data.
NAS-BERT: Task-Agnostic and Adaptive-Size BERT Compression with Neural Architecture Search
The technical challenge of NAS-BERT is that training a big supernet on the pre-training task is extremely costly.
FastCorrect: Fast Error Correction with Edit Alignment for Automatic Speech Recognition
A straightforward solution to reduce latency, inspired by non-autoregressive (NAR) neural machine translation, is to use an NAR sequence generation model for ASR error correction, which, however, comes at the cost of significantly increased ASR error rate.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
WHOSe Heritage: Classification of UNESCO World Heritage "Outstanding Universal Value" Documents with Soft Labels
A human study with expert evaluation on the model prediction shows that the models are sufficiently generalizable.
LightSpeech: Lightweight and Fast Text to Speech with Neural Architecture Search
Text to speech (TTS) has been broadly used to synthesize natural and intelligible speech in different scenarios.
Task-Agnostic and Adaptive-Size BERT Compression
NAS-BERT trains a big supernet on a carefully designed search space containing various architectures and outputs multiple compressed models with adaptive sizes and latency.
Accuracy Prediction with Non-neural Model for Neural Architecture Search
Considering that most architectures are represented as sequences of discrete symbols which are more like tabular data and preferred by non-neural predictors, in this paper, we study an alternative approach which uses non-neural model for accuracy prediction.
Ranked #81 on
Neural Architecture Search
on ImageNet
Semi-Supervised Neural Architecture Search
On ImageNet, it achieves 23. 5% top-1 error rate (under 600M FLOPS constraint) using 4 GPU-days for search.
Ranked #81 on
Neural Architecture Search
on ImageNet
Microsoft Research Asia's Systems for WMT19
We Microsoft Research Asia made submissions to 11 language directions in the WMT19 news translation tasks.
Balanced One-shot Neural Architecture Optimization
One-shot NAS is proposed to reduce the expense but shows inferior performance against conventional NAS and is not adequately stable.
Neural Architecture Optimization
The performance predictor and the encoder enable us to perform gradient based optimization in the continuous space to find the embedding of a new architecture with potentially better accuracy.
Achieving Human Parity on Automatic Chinese to English News Translation
Machine translation has made rapid advances in recent years.
Ranked #3 on
Machine Translation
on WMT 2017 English-Chinese
