Soroush Mehri

When is Transfer Learning Possible?

My Phan

Kianté Brantley

Stephanie Milani

Soroush Mehri

Gokul Swamy

Geoff Gordon

2024-07-08

Proceedings of the 41st International Conference on Machine Learning (published)

proceedings.mlr.press

Deep Complex Networks

At present, the vast majority of building blocks, techniques, and architectures for deep learning are based on real-valued operations and re… (see more)presentations. However, recent work on recurrent neural networks and older fundamental theoretical analysis suggests that complex numbers could have a richer representational capacity and could also facilitate noise-robust memory retrieval mechanisms. Despite their attractive properties and potential for opening up entirely new neural architectures, complex-valued deep neural networks have been marginalized due to the absence of the building blocks required to design such models. In this work, we provide the key atomic components for complex-valued deep neural networks and apply them to convolutional feed-forward networks. More precisely, we rely on complex convolutions and present algorithms for complex batch-normalization, complex weight initialization strategies for complex-valued neural nets and we use them in experiments with end-to-end training schemes. We demonstrate that such complex-valued models are competitive with their real-valued counterparts. We test deep complex models on several computer vision tasks, on music transcription using the MusicNet dataset and on Speech spectrum prediction using TIMIT. We achieve state-of-the-art performance on these audio-related tasks.

2018-01-01

ICLR.cc/2018/Conference (poster)

openreview.net

Deep Complex Networks

2017-05-27

ArXiv (preprint)

arxiv.org

Deep Complex Networks

At present, the vast majority of building blocks, techniques, and architectures for deep learning are based on real-valued operations and re… (see more)presentations. However, recent work on recurrent neural networks and older fundamental theoretical analysis suggests that complex numbers could have a richer representational capacity and could also facilitate noise-robust memory retrieval mechanisms. Despite their attractive properties and potential for opening up entirely new neural architectures, complex-valued deep neural networks have been marginalized due to the absence of the building blocks required to design such models. In this work, we provide the key atomic components for complex-valued deep neural networks and apply them to convolutional feed-forward networks and convolutional LSTMs. More precisely, we rely on complex convolutions and present algorithms for complex batch-normalization, complex weight initialization strategies for complex-valued neural nets and we use them in experiments with end-to-end training schemes. We demonstrate that such complex-valued models are competitive with their real-valued counterparts. We test deep complex models on several computer vision tasks, on music transcription using the MusicNet dataset and on Speech Spectrum Prediction using the TIMIT dataset. We achieve state-of-the-art performance on these audio-related tasks.

2017-05-27

ArXiv (preprint)

arxiv.org

Deep Complex Networks

2017-05-27

ArXiv (preprint)

arxiv.org

Deep Complex Networks

2017-05-27

ArXiv (preprint)

arxiv.org

Deep Complex Networks

2017-05-27

ArXiv (preprint)

arxiv.org

Deep Complex Networks

2017-05-27

ArXiv (preprint)

arxiv.org

Deep Complex Networks

2017-05-27

ArXiv (preprint)

arxiv.org

Deep Complex Networks

2017-05-27

ArXiv (preprint)

arxiv.org

Deep Complex Networks

2017-05-27

ArXiv (preprint)

arxiv.org

Char2Wav: End-to-End Speech Synthesis

Jose Sotelo

We present Char2Wav, an end-to-end model for speech synthesis. Char2Wav has two components: a reader and a neural vocoder . The reader is an… (see more) encoder-decoder model with attention. The encoder is a bidirectional recurrent neural network that accepts text or phonemes as inputs, while the decoder is a recurrent neural network (RNN) with attention that produces vocoder acoustic features. Neural vocoder refers to a conditional extension of SampleRNN which generates raw waveform samples from intermediate representations. Unlike traditional models for speech synthesis, Char2Wav learns to produce audio directly from text

2017-02-17

International Conference on Learning Representations (published)

openreview.net

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Soroush Mehri

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Custom AI Learning Programs

Mil'Haq Fest 2025

Mila Community of Practice

Supervision Requests

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Soroush Mehri

Publications