Search Results for author: Peter Wu

Found 8 papers, 8 papers with code

Cross-lingual Transfer for Speech Processing using Acoustic Language Similarity

1 code implementation2 Nov 2021 Peter Wu, Jiatong Shi, Yifan Zhong, Shinji Watanabe, Alan W Black

We demonstrate the effectiveness of our approach in language family classification, speech recognition, and speech synthesis tasks.

Cross-Lingual Transfer Speech Recognition +1

MultiBench: Multiscale Benchmarks for Multimodal Representation Learning

2 code implementations15 Jul 2021 Paul Pu Liang, Yiwei Lyu, Xiang Fan, Zetian Wu, Yun Cheng, Jason Wu, Leslie Chen, Peter Wu, Michelle A. Lee, Yuke Zhu, Ruslan Salakhutdinov, Louis-Philippe Morency

In order to accelerate progress towards understudied modalities and tasks while ensuring real-world robustness, we release MultiBench, a systematic and unified large-scale benchmark spanning 15 datasets, 10 modalities, 20 prediction tasks, and 6 research areas.

Representation Learning

Cross-Modal Generalization: Learning in Low Resource Modalities via Meta-Alignment

1 code implementation4 Dec 2020 Paul Pu Liang, Peter Wu, Liu Ziyin, Louis-Philippe Morency, Ruslan Salakhutdinov

In this work, we propose algorithms for cross-modal generalization: a learning paradigm to train a model that can (1) quickly perform new tasks in a target modality (i. e. meta-learning) and (2) doing so while being trained on a different source modality.

Meta-Learning

Automatically Identifying Language Family from Acoustic Examples in Low Resource Scenarios

1 code implementation1 Dec 2020 Peter Wu, Yifan Zhong, Alan W Black

Existing multilingual speech NLP works focus on a relatively small subset of languages, and thus current linguistic understanding of languages predominantly stems from classical approaches.

Data Augmentation

LEAF: A Benchmark for Federated Settings

4 code implementations3 Dec 2018 Sebastian Caldas, Sai Meher Karthik Duddu, Peter Wu, Tian Li, Jakub Konečný, H. Brendan McMahan, Virginia Smith, Ameet Talwalkar

Modern federated networks, such as those comprised of wearable devices, mobile phones, or autonomous vehicles, generate massive amounts of data each day.

Autonomous Vehicles Federated Learning +2

Machine Learning for Exam Triage

1 code implementation30 Apr 2018 Xinyu Guan, Jessica Lee, Peter Wu, Yue Wu

In this project, we extend the state-of-the-art CheXNet (Rajpurkar et al. [2017]) by making use of the additional non-image features in the dataset.

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