no code implementations • 8 Oct 2023 • Shivam Mehta, Ruibo Tu, Simon Alexanderson, Jonas Beskow, Éva Székely, Gustav Eje Henter
As text-to-speech technologies achieve remarkable naturalness in read-aloud tasks, there is growing interest in multimodal synthesis of verbal and non-verbal communicative behaviour, such as spontaneous speech and associated body gestures.
Ranked #1 on Motion Synthesis on Trinity Speech-Gesture Dataset
1 code implementation • 6 Sep 2023 • Shivam Mehta, Ruibo Tu, Jonas Beskow, Éva Székely, Gustav Eje Henter
We introduce Matcha-TTS, a new encoder-decoder architecture for speedy TTS acoustic modelling, trained using optimal-transport conditional flow matching (OT-CFM).
Ranked #1 on Text-To-Speech Synthesis on LJSpeech (MOS metric)
no code implementations • 3 Apr 2023 • Wenjie Yin, Ruibo Tu, Hang Yin, Danica Kragic, Hedvig Kjellström, Mårten Björkman
Data-driven and controllable human motion synthesis and prediction are active research areas with various applications in interactive media and social robotics.
no code implementations • 24 Jan 2023 • Ruibo Tu, Chao Ma, Cheng Zhang
ChatGPT has demonstrated exceptional proficiency in natural language conversation, e. g., it can answer a wide range of questions while no previous large language models can.
no code implementations • ICLR 2022 • Ruibo Tu, Kun Zhang, Hedvig Kjellström, Cheng Zhang
With this criterion, we propose a novel optimal transport-based algorithm for ANMs which is robust to the choice of models and extend it to post-nonlinear models.
no code implementations • 12 Oct 2021 • Carles Balsells-Rodas, Ruibo Tu, Hedvig Kjellstrom, Yingzhen Li
Causal discovery, i. e., inferring underlying causal relationships from observational data, has been shown to be highly challenging for AI systems.
1 code implementation • RC 2020 • Ci Li, Ruibo Tu, HUI ZHANG
FixMatch is a semi-supervised learning method, which achieves comparable results with fully supervised learning by leveraging a limited number of labeled data (pseudo labelling technique) and taking a good use of the unlabeled data (consistency regularization ).
1 code implementation • NeurIPS 2020 • Xueru Zhang, Ruibo Tu, Yang Liu, Mingyan Liu, Hedvig Kjellström, Kun Zhang, Cheng Zhang
Our results show that static fairness constraints can either promote equality or exacerbate disparity depending on the driving factor of qualification transitions and the effect of sensitive attributes on feature distributions.
1 code implementation • NeurIPS 2019 • Ruibo Tu, Kun Zhang, Bo Christer Bertilson, Hedvig Kjellström, Cheng Zhang
We show that the data generated from our simulator have similar statistics as real-world data.
no code implementations • 8 Sep 2018 • Charles Hamesse, Ruibo Tu, Paul Ackermann, Hedvig Kjellström, Cheng Zhang
However, it is challenging to train an automatic method for predicting the ATR rehabilitation outcome from treatment data, due to a massive amount of missing entries in the data recorded from ATR patients, as well as complex nonlinear relations between measurements and outcomes.
1 code implementation • 11 Jul 2018 • Ruibo Tu, Kun Zhang, Paul Ackermann, Bo Christer Bertilson, Clark Glymour, Hedvig Kjellström, Cheng Zhang
When these data entries are not missing completely at random, the (conditional) independence relations in the observed data may be different from those in the complete data generated by the underlying causal process.