Search Results for author: Ruibo Tu

Found 11 papers, 5 papers with code

Unified speech and gesture synthesis using flow matching

no code implementations8 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.

Audio Synthesis Motion Synthesis +1

Matcha-TTS: A fast TTS architecture with conditional flow matching

1 code implementation6 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)

Acoustic Modelling Speech Synthesis +1

Controllable Motion Synthesis and Reconstruction with Autoregressive Diffusion Models

no code implementations3 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.

Motion Synthesis

Causal-Discovery Performance of ChatGPT in the context of Neuropathic Pain Diagnosis

no code implementations24 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.

Causal Discovery

Optimal transport for causal discovery

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.

Causal Discovery

Causal Discovery from Conditionally Stationary Time Series

no code implementations12 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.

Causal Discovery Causal Inference +3

[Re] Reimplementation of FixMatch and Investigation on Noisy (Pseudo) Labels and Confirmation Errors of FixMatch

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 ).

Semi-Supervised Image Classification

How Do Fair Decisions Fare in Long-term Qualification?

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.

Decision Making Fairness

Simultaneous Measurement Imputation and Outcome Prediction for Achilles Tendon Rupture Rehabilitation

no code implementations8 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.

Imputation

Causal Discovery in the Presence of Missing Data

1 code implementation11 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.

Causal Discovery

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