Search Results for author: Cory Stephenson

Found 13 papers, 5 papers with code

CommonCanvas: Open Diffusion Models Trained on Creative-Commons Images

no code implementations CVPR 2024 Aaron Gokaslan, A. Feder Cooper, Jasmine Collins, Landan Seguin, Austin Jacobson, Mihir Patel, Jonathan Frankle, Cory Stephenson, Volodymyr Kuleshov

We then develop a data- and compute-efficient training recipe that requires as little as 3% of the LAION data (i. e. roughly 70 million examples) needed to train existing SD2 models but obtains the same quality.

Transfer Learning

CommonCanvas: An Open Diffusion Model Trained with Creative-Commons Images

1 code implementation25 Oct 2023 Aaron Gokaslan, A. Feder Cooper, Jasmine Collins, Landan Seguin, Austin Jacobson, Mihir Patel, Jonathan Frankle, Cory Stephenson, Volodymyr Kuleshov

This task presents two challenges: (1) high-resolution CC images lack the captions necessary to train text-to-image generative models; (2) CC images are relatively scarce.

Transfer Learning

Fast Benchmarking of Accuracy vs. Training Time with Cyclic Learning Rates

1 code implementation2 Jun 2022 Jacob Portes, Davis Blalock, Cory Stephenson, Jonathan Frankle

Benchmarking the tradeoff between neural network accuracy and training time is computationally expensive.


When and how epochwise double descent happens

no code implementations26 Aug 2021 Cory Stephenson, Tyler Lee

This model is based on the hypothesis that the training data contains features that are slow to learn but informative.

On the geometry of generalization and memorization in deep neural networks

no code implementations ICLR 2021 Cory Stephenson, Suchismita Padhy, Abhinav Ganesh, Yue Hui, Hanlin Tang, SueYeon Chung

Understanding how large neural networks avoid memorizing training data is key to explaining their high generalization performance.


Emergence of Separable Manifolds in Deep Language Representations

1 code implementation ICML 2020 Jonathan Mamou, Hang Le, Miguel Del Rio, Cory Stephenson, Hanlin Tang, Yoon Kim, SueYeon Chung

In addition, we find that the emergence of linear separability in these manifolds is driven by a combined reduction of manifolds' radius, dimensionality and inter-manifold correlations.

Semi-supervised voice conversion with amortized variational inference

no code implementations30 Sep 2019 Cory Stephenson, Gokce Keskin, Anil Thomas, Oguz H. Elibol

In this work we introduce a semi-supervised approach to the voice conversion problem, in which speech from a source speaker is converted into speech of a target speaker.

Variational Inference Voice Conversion

Probing emergent geometry in speech models via replica theory

no code implementations28 May 2019 Suchismita Padhy, Jenelle Feather, Cory Stephenson, Oguz Elibol, Hanlin Tang, Josh Mcdermott, SueYeon Chung

The success of deep neural networks in visual tasks have motivated recent theoretical and empirical work to understand how these networks operate.

speech-recognition Speech Recognition

Adversarially Trained Autoencoders for Parallel-Data-Free Voice Conversion

no code implementations9 May 2019 Orhan Ocal, Oguz H. Elibol, Gokce Keskin, Cory Stephenson, Anil Thomas, Kannan Ramchandran

Due to the use of a single encoder, our method can generalize to converting the voice of out-of-training speakers to speakers in the training dataset.

Voice Conversion

Many-to-Many Voice Conversion with Out-of-Dataset Speaker Support

no code implementations30 Apr 2019 Gokce Keskin, Tyler Lee, Cory Stephenson, Oguz H. Elibol

We present a Cycle-GAN based many-to-many voice conversion method that can convert between speakers that are not in the training set.

Speaker Identification Voice Conversion

Deep Speech Denoising with Vector Space Projections

no code implementations27 Apr 2018 Jeff Hetherly, Paul Gamble, Maria Barrios, Cory Stephenson, Karl Ni

We propose an algorithm to denoise speakers from a single microphone in the presence of non-stationary and dynamic noise.

Denoising Speech Denoising

Monaural Audio Speaker Separation with Source Contrastive Estimation

1 code implementation12 May 2017 Cory Stephenson, Patrick Callier, Abhinav Ganesh, Karl Ni

Although the matrix determined by the output weights is dependent on a set of known speakers, we only use the input vectors during inference.

Clustering Descriptive +1

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