Search Results for author:
Found 7 papers, 2 papers with code
Large Language Models Encode Clinical Knowledge
To resolve this we introduce instruction prompt tuning, a parameter-efficient approach for aligning LLMs to new domains using a few exemplars.
Ranked #1 on
Question Answering
on MedQA-USMLE
Inferring latent neural sources via deep transcoding of simultaneously acquired EEG and fMRI
Simultaneous EEG-fMRI is a multi-modal neuroimaging technique that provides complementary spatial and temporal resolution.
Learning Better Visual Dialog Agents with Pretrained Visual-Linguistic Representation
Most existing work for Guesser encode the dialog history as a whole and train the Guesser models from scratch on the GuessWhat?!
Semi-supervised Learning for Multi-speaker Text-to-speech Synthesis Using Discrete Speech Representation
The experiment results demonstrate that with only an hour of paired speech data, no matter the paired data is from multiple speakers or a single speaker, the proposed model can generate intelligible speech in different voices.
A state-space model for inferring effective connectivity of latent neural dynamics from simultaneous EEG/fMRI
In this study, we develop a linear state-space model to infer the effective connectivity in a distributed brain network based on simultaneously recorded EEG and fMRI data.
Towards Unsupervised Speech Recognition and Synthesis with Quantized Speech Representation Learning
In this paper we propose a Sequential Representation Quantization AutoEncoder (SeqRQ-AE) to learn from primarily unpaired audio data and produce sequences of representations very close to phoneme sequences of speech utterances.
End-to-end Text-to-speech for Low-resource Languages by Cross-Lingual Transfer Learning
In this paper, we aim to build TTS systems for such low-resource (target) languages where only very limited paired data are available.
