#optimaltransport — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #optimaltransport, aggregated by home.social.
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Following up on this, I also explored a more direct use of #WassersteinDistance in #WGANs: Instead of training a discriminator, the generator is optimized by explicitly computing the #OptimalTransport distance between real and generated samples. This turns the loss into the actual metric of interest and removes the adversarial setup, leading to a more direct and stable training signal. And we can generate cool animations, too ^_^
🌍 https://www.fabriziomusacchio.com/blog/2023-07-30-wgan_with_direct_wasserstein_distance/
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Following up on this, I also explored a more direct use of #WassersteinDistance in #WGANs: Instead of training a discriminator, the generator is optimized by explicitly computing the #OptimalTransport distance between real and generated samples. This turns the loss into the actual metric of interest and removes the adversarial setup, leading to a more direct and stable training signal. And we can generate cool animations, too ^_^
🌍 https://www.fabriziomusacchio.com/blog/2023-07-30-wgan_with_direct_wasserstein_distance/
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Following up on this, I also explored a more direct use of #WassersteinDistance in #WGANs: Instead of training a discriminator, the generator is optimized by explicitly computing the #OptimalTransport distance between real and generated samples. This turns the loss into the actual metric of interest and removes the adversarial setup, leading to a more direct and stable training signal. And we can generate cool animations, too ^_^
🌍 https://www.fabriziomusacchio.com/blog/2023-07-30-wgan_with_direct_wasserstein_distance/
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Following up on this, I also explored a more direct use of #WassersteinDistance in #WGANs: Instead of training a discriminator, the generator is optimized by explicitly computing the #OptimalTransport distance between real and generated samples. This turns the loss into the actual metric of interest and removes the adversarial setup, leading to a more direct and stable training signal. And we can generate cool animations, too ^_^
🌍 https://www.fabriziomusacchio.com/blog/2023-07-30-wgan_with_direct_wasserstein_distance/
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Following up on this, I also explored a more direct use of #WassersteinDistance in #WGANs: Instead of training a discriminator, the generator is optimized by explicitly computing the #OptimalTransport distance between real and generated samples. This turns the loss into the actual metric of interest and removes the adversarial setup, leading to a more direct and stable training signal. And we can generate cool animations, too ^_^
🌍 https://www.fabriziomusacchio.com/blog/2023-07-30-wgan_with_direct_wasserstein_distance/
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I actually wrote a short introduction to #WassersteinDistance and #OptimalTransport some time ago, if you’re looking for a more intuitive entry point:
🌍 https://www.fabriziomusacchio.com/blog/2023-07-23-wasserstein_distance/
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I actually wrote a short introduction to #WassersteinDistance and #OptimalTransport some time ago, if you’re looking for a more intuitive entry point:
🌍 https://www.fabriziomusacchio.com/blog/2023-07-23-wasserstein_distance/
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I actually wrote a short introduction to #WassersteinDistance and #OptimalTransport some time ago, if you’re looking for a more intuitive entry point:
🌍 https://www.fabriziomusacchio.com/blog/2023-07-23-wasserstein_distance/
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I actually wrote a short introduction to #WassersteinDistance and #OptimalTransport some time ago, if you’re looking for a more intuitive entry point:
🌍 https://www.fabriziomusacchio.com/blog/2023-07-23-wasserstein_distance/
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I actually wrote a short introduction to #WassersteinDistance and #OptimalTransport some time ago, if you’re looking for a more intuitive entry point:
🌍 https://www.fabriziomusacchio.com/blog/2023-07-23-wasserstein_distance/
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📐📚New study on #WassersteinDistance: Bonet et al. study #geodesic rays in #Wasserstein space and derive conditions for their existence. They show that #Busemann functions can be computed via #OT, with closed-form solutions for 1D and Gaussian cases. This enables efficient sliced distances for labeled datasets, closely matching classical metrics at lower cost and supporting dataset “flows” for #TransferLearning.
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📐📚New study on #WassersteinDistance: Bonet et al. study #geodesic rays in #Wasserstein space and derive conditions for their existence. They show that #Busemann functions can be computed via #OT, with closed-form solutions for 1D and Gaussian cases. This enables efficient sliced distances for labeled datasets, closely matching classical metrics at lower cost and supporting dataset “flows” for #TransferLearning.
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📐📚New study on #WassersteinDistance: Bonet et al. study #geodesic rays in #Wasserstein space and derive conditions for their existence. They show that #Busemann functions can be computed via #OT, with closed-form solutions for 1D and Gaussian cases. This enables efficient sliced distances for labeled datasets, closely matching classical metrics at lower cost and supporting dataset “flows” for #TransferLearning.
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📐📚New study on #WassersteinDistance: Bonet et al. study #geodesic rays in #Wasserstein space and derive conditions for their existence. They show that #Busemann functions can be computed via #OT, with closed-form solutions for 1D and Gaussian cases. This enables efficient sliced distances for labeled datasets, closely matching classical metrics at lower cost and supporting dataset “flows” for #TransferLearning.
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📐📚New study on #WassersteinDistance: Bonet et al. study #geodesic rays in #Wasserstein space and derive conditions for their existence. They show that #Busemann functions can be computed via #OT, with closed-form solutions for 1D and Gaussian cases. This enables efficient sliced distances for labeled datasets, closely matching classical metrics at lower cost and supporting dataset “flows” for #TransferLearning.
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📐 New preprint by Gabriel Peyré: The paper introduces a new class of spectral #Wasserstein distances, linking #OptimalTransport with normalized #gradient methods. It shows that spectrally normalized #GradientDescent can be interpreted as a gradient flow in this spectral-W geometry, providing a principled bridge between #optimization dynamics and transport metrics:
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📐 New preprint by Gabriel Peyré: The paper introduces a new class of spectral #Wasserstein distances, linking #OptimalTransport with normalized #gradient methods. It shows that spectrally normalized #GradientDescent can be interpreted as a gradient flow in this spectral-W geometry, providing a principled bridge between #optimization dynamics and transport metrics:
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📐 New preprint by Gabriel Peyré: The paper introduces a new class of spectral #Wasserstein distances, linking #OptimalTransport with normalized #gradient methods. It shows that spectrally normalized #GradientDescent can be interpreted as a gradient flow in this spectral-W geometry, providing a principled bridge between #optimization dynamics and transport metrics:
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📐 New preprint by Gabriel Peyré: The paper introduces a new class of spectral #Wasserstein distances, linking #OptimalTransport with normalized #gradient methods. It shows that spectrally normalized #GradientDescent can be interpreted as a gradient flow in this spectral-W geometry, providing a principled bridge between #optimization dynamics and transport metrics:
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📐 New preprint by Gabriel Peyré: The paper introduces a new class of spectral #Wasserstein distances, linking #OptimalTransport with normalized #gradient methods. It shows that spectrally normalized #GradientDescent can be interpreted as a gradient flow in this spectral-W geometry, providing a principled bridge between #optimization dynamics and transport metrics:
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🌟 Sinkhorn-Knopp Algorithm: Giải thuật này như Softmax nhưng专注 về transport tối ưu trong toán học. Tài liệu liệt kê #SinkhornKnoppAlgorithm #OptimalTransport #AI #ToánHọc #MachineLearning
https://www.reddit.com/r/programming/comments/1oc3ond/sinkhornknopp_algorithm_like_softmax_but_for/
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📝💤 "Behold, the 'brief' intro to optimal transport where intuition triumphs over 'maths' because who needs rigor? 🙄 It's basically a #YouTube rabbit hole disguised as a blog, because nothing says 'understandable' like suggesting you watch a four-year-old lecture series. 📚📺"
https://alexhwilliams.info/itsneuronalblog/2020/10/09/optimal-transport/ #optimaltransport #rabbitHole #blogpost #mathintuition #lectureSeries #HackerNews #ngated -
📝💤 "Behold, the 'brief' intro to optimal transport where intuition triumphs over 'maths' because who needs rigor? 🙄 It's basically a #YouTube rabbit hole disguised as a blog, because nothing says 'understandable' like suggesting you watch a four-year-old lecture series. 📚📺"
https://alexhwilliams.info/itsneuronalblog/2020/10/09/optimal-transport/ #optimaltransport #rabbitHole #blogpost #mathintuition #lectureSeries #HackerNews #ngated -
📝💤 "Behold, the 'brief' intro to optimal transport where intuition triumphs over 'maths' because who needs rigor? 🙄 It's basically a #YouTube rabbit hole disguised as a blog, because nothing says 'understandable' like suggesting you watch a four-year-old lecture series. 📚📺"
https://alexhwilliams.info/itsneuronalblog/2020/10/09/optimal-transport/ #optimaltransport #rabbitHole #blogpost #mathintuition #lectureSeries #HackerNews #ngated -
📝💤 "Behold, the 'brief' intro to optimal transport where intuition triumphs over 'maths' because who needs rigor? 🙄 It's basically a #YouTube rabbit hole disguised as a blog, because nothing says 'understandable' like suggesting you watch a four-year-old lecture series. 📚📺"
https://alexhwilliams.info/itsneuronalblog/2020/10/09/optimal-transport/ #optimaltransport #rabbitHole #blogpost #mathintuition #lectureSeries #HackerNews #ngated -
📝💤 "Behold, the 'brief' intro to optimal transport where intuition triumphs over 'maths' because who needs rigor? 🙄 It's basically a #YouTube rabbit hole disguised as a blog, because nothing says 'understandable' like suggesting you watch a four-year-old lecture series. 📚📺"
https://alexhwilliams.info/itsneuronalblog/2020/10/09/optimal-transport/ #optimaltransport #rabbitHole #blogpost #mathintuition #lectureSeries #HackerNews #ngated -
A short introduction to optimal transport and Wasserstein distance
https://alexhwilliams.info/itsneuronalblog/2020/10/09/optimal-transport/
#HackerNews #optimaltransport #Wasserstein #distance #machinelearning #math #statistics #dataanalysis
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A short introduction to optimal transport and Wasserstein distance
https://alexhwilliams.info/itsneuronalblog/2020/10/09/optimal-transport/
#HackerNews #optimaltransport #Wasserstein #distance #machinelearning #math #statistics #dataanalysis
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A short introduction to optimal transport and Wasserstein distance
https://alexhwilliams.info/itsneuronalblog/2020/10/09/optimal-transport/
#HackerNews #optimaltransport #Wasserstein #distance #machinelearning #math #statistics #dataanalysis
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A short introduction to optimal transport and Wasserstein distance
https://alexhwilliams.info/itsneuronalblog/2020/10/09/optimal-transport/
#HackerNews #optimaltransport #Wasserstein #distance #machinelearning #math #statistics #dataanalysis
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A short introduction to optimal transport and Wasserstein distance
https://alexhwilliams.info/itsneuronalblog/2020/10/09/optimal-transport/
#HackerNews #optimaltransport #Wasserstein #distance #machinelearning #math #statistics #dataanalysis
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The #Wasserstein distance (#EMD), sliced Wasserstein distance (#SWD), and the #L2norm are common #metrics used to quantify the ‘distance’ between two distributions. This tutorial compares these three metrics and discusses their advantages and disadvantages.
🌎 https://www.fabriziomusacchio.com/blog/2023-07-26-wasserstein_vs_l2_norm/
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The #Wasserstein distance (#EMD), sliced Wasserstein distance (#SWD), and the #L2norm are common #metrics used to quantify the ‘distance’ between two distributions. This tutorial compares these three metrics and discusses their advantages and disadvantages.
🌎 https://www.fabriziomusacchio.com/blog/2023-07-26-wasserstein_vs_l2_norm/
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The #Wasserstein distance (#EMD), sliced Wasserstein distance (#SWD), and the #L2norm are common #metrics used to quantify the ‘distance’ between two distributions. This tutorial compares these three metrics and discusses their advantages and disadvantages.
🌎 https://www.fabriziomusacchio.com/blog/2023-07-26-wasserstein_vs_l2_norm/
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The #Wasserstein distance (#EMD), sliced Wasserstein distance (#SWD), and the #L2norm are common #metrics used to quantify the ‘distance’ between two distributions. This tutorial compares these three metrics and discusses their advantages and disadvantages.
🌎 https://www.fabriziomusacchio.com/blog/2023-07-26-wasserstein_vs_l2_norm/
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This tutorial takes a different approach to explain the #Wasserstein distance (#EMD) by approximating the #EMD with cumulative distribution functions (#CDF), providing a more intuitive understanding of the metric.
🌎 https://www.fabriziomusacchio.com/blog/2023-07-24-wasserstein_distance_cdf_approximation/
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This tutorial takes a different approach to explain the #Wasserstein distance (#EMD) by approximating the #EMD with cumulative distribution functions (#CDF), providing a more intuitive understanding of the metric.
🌎 https://www.fabriziomusacchio.com/blog/2023-07-24-wasserstein_distance_cdf_approximation/
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This tutorial takes a different approach to explain the #Wasserstein distance (#EMD) by approximating the #EMD with cumulative distribution functions (#CDF), providing a more intuitive understanding of the metric.
🌎 https://www.fabriziomusacchio.com/blog/2023-07-24-wasserstein_distance_cdf_approximation/
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This tutorial takes a different approach to explain the #Wasserstein distance (#EMD) by approximating the #EMD with cumulative distribution functions (#CDF), providing a more intuitive understanding of the metric.
🌎 https://www.fabriziomusacchio.com/blog/2023-07-24-wasserstein_distance_cdf_approximation/
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Calculating the #Wasserstein distance (#EMD) 📈 can be computational costly when using #LinearProgramming. The #Sinkhorn algorithm provides a computationally efficient method for approximating the EMD, making it a practical choice for many applications, especially for large datasets 💫. Here is another tutorial, showing how to solve #OptimalTransport problem using the Sinkhorn algorithm in #Python 🐍
🌎 https://www.fabriziomusacchio.com/blog/2023-07-23-wasserstein_distance_sinkhorn/
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Calculating the #Wasserstein distance (#EMD) 📈 can be computational costly when using #LinearProgramming. The #Sinkhorn algorithm provides a computationally efficient method for approximating the EMD, making it a practical choice for many applications, especially for large datasets 💫. Here is another tutorial, showing how to solve #OptimalTransport problem using the Sinkhorn algorithm in #Python 🐍
🌎 https://www.fabriziomusacchio.com/blog/2023-07-23-wasserstein_distance_sinkhorn/
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Calculating the #Wasserstein distance (#EMD) 📈 can be computational costly when using #LinearProgramming. The #Sinkhorn algorithm provides a computationally efficient method for approximating the EMD, making it a practical choice for many applications, especially for large datasets 💫. Here is another tutorial, showing how to solve #OptimalTransport problem using the Sinkhorn algorithm in #Python 🐍
🌎 https://www.fabriziomusacchio.com/blog/2023-07-23-wasserstein_distance_sinkhorn/
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Calculating the #Wasserstein distance (#EMD) 📈 can be computational costly when using #LinearProgramming. The #Sinkhorn algorithm provides a computationally efficient method for approximating the EMD, making it a practical choice for many applications, especially for large datasets 💫. Here is another tutorial, showing how to solve #OptimalTransport problem using the Sinkhorn algorithm in #Python 🐍
🌎 https://www.fabriziomusacchio.com/blog/2023-07-23-wasserstein_distance_sinkhorn/
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The #Wasserstein distance 📐, aka Earth Mover’s Distance (#EMD), provides a robust and insightful approach for comparing #ProbabilityDistributions 📊. I’ve composed a #Python tutorial 🐍 that explains the #OptimalTransport problem required to calculate EMD. It also shows how to solve the OT problem and calculate the EMD using the Python Optimal Transport (POT) library. Feel free to use and share it 🤗
🌎 https://www.fabriziomusacchio.com/blog/2023-07-23-wasserstein_distance/
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The #Wasserstein distance 📐, aka Earth Mover’s Distance (#EMD), provides a robust and insightful approach for comparing #ProbabilityDistributions 📊. I’ve composed a #Python tutorial 🐍 that explains the #OptimalTransport problem required to calculate EMD. It also shows how to solve the OT problem and calculate the EMD using the Python Optimal Transport (POT) library. Feel free to use and share it 🤗
🌎 https://www.fabriziomusacchio.com/blog/2023-07-23-wasserstein_distance/
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The #Wasserstein distance 📐, aka Earth Mover’s Distance (#EMD), provides a robust and insightful approach for comparing #ProbabilityDistributions 📊. I’ve composed a #Python tutorial 🐍 that explains the #OptimalTransport problem required to calculate EMD. It also shows how to solve the OT problem and calculate the EMD using the Python Optimal Transport (POT) library. Feel free to use and share it 🤗
🌎 https://www.fabriziomusacchio.com/blog/2023-07-23-wasserstein_distance/
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The #Wasserstein distance 📐, aka Earth Mover’s Distance (#EMD), provides a robust and insightful approach for comparing #ProbabilityDistributions 📊. I’ve composed a #Python tutorial 🐍 that explains the #OptimalTransport problem required to calculate EMD. It also shows how to solve the OT problem and calculate the EMD using the Python Optimal Transport (POT) library. Feel free to use and share it 🤗
🌎 https://www.fabriziomusacchio.com/blog/2023-07-23-wasserstein_distance/
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#OptimalTransport: Moving stuff through a #labyrinth
(Nicolas Papadakis: Optimal Transport for Image Processing, Signal and Image Processing. Université
de Bordeaux; Habilitation thesis, 2015. tel-01246096v8, 2007) -
#OptimalTransport: Moving stuff through a #labyrinth
(Nicolas Papadakis: Optimal Transport for Image Processing, Signal and Image Processing. Université
de Bordeaux; Habilitation thesis, 2015. tel-01246096v8, 2007) -
#OptimalTransport: Moving stuff through a #labyrinth
(Nicolas Papadakis: Optimal Transport for Image Processing, Signal and Image Processing. Université
de Bordeaux; Habilitation thesis, 2015. tel-01246096v8, 2007) -
#OptimalTransport: Moving stuff through a #labyrinth
(Nicolas Papadakis: Optimal Transport for Image Processing, Signal and Image Processing. Université
de Bordeaux; Habilitation thesis, 2015. tel-01246096v8, 2007) -
Our Pick of the week: Phuong-Hang Le et al., "Pre-training for Speech Translation: CTC Meets Optimal Transport"
by @mgaido91:arxiv: https://arxiv.org/abs/2301.11716
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Our Pick of the week: Phuong-Hang Le et al., "Pre-training for Speech Translation: CTC Meets Optimal Transport"
by @mgaido91:arxiv: https://arxiv.org/abs/2301.11716
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Our Pick of the week: Phuong-Hang Le et al., "Pre-training for Speech Translation: CTC Meets Optimal Transport"
by @mgaido91:arxiv: https://arxiv.org/abs/2301.11716
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Our Pick of the week: Phuong-Hang Le et al., "Pre-training for Speech Translation: CTC Meets Optimal Transport"
by @mgaido91:arxiv: https://arxiv.org/abs/2301.11716
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The first is Éric Daoud, who defended on Monday.
His dissertation is titled "Geographic and socio-demographic disparities in oncology care pathways" and was supervised by Fabien Reyal and Marc Lelarge. Éric was a member of a working group we had on #machineLearning and #electronicHealthRecords until I was sick. See his preprints here: https://edaoud.com/research/ Interesting applications of #optimalTransport inside!
2/3
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The first is Éric Daoud, who defended on Monday.
His dissertation is titled "Geographic and socio-demographic disparities in oncology care pathways" and was supervised by Fabien Reyal and Marc Lelarge. Éric was a member of a working group we had on #machineLearning and #electronicHealthRecords until I was sick. See his preprints here: https://edaoud.com/research/ Interesting applications of #optimalTransport inside!
2/3
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The first is Éric Daoud, who defended on Monday.
His dissertation is titled "Geographic and socio-demographic disparities in oncology care pathways" and was supervised by Fabien Reyal and Marc Lelarge. Éric was a member of a working group we had on #machineLearning and #electronicHealthRecords until I was sick. See his preprints here: https://edaoud.com/research/ Interesting applications of #optimalTransport inside!
2/3
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The first is Éric Daoud, who defended on Monday.
His dissertation is titled "Geographic and socio-demographic disparities in oncology care pathways" and was supervised by Fabien Reyal and Marc Lelarge. Éric was a member of a working group we had on #machineLearning and #electronicHealthRecords until I was sick. See his preprints here: https://edaoud.com/research/ Interesting applications of #optimalTransport inside!
2/3
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The first is Éric Daoud, who defended on Monday.
His dissertation is titled "Geographic and socio-demographic disparities in oncology care pathways" and was supervised by Fabien Reyal and Marc Lelarge. Éric was a member of a working group we had on #machineLearning and #electronicHealthRecords until I was sick. See his preprints here: https://edaoud.com/research/ Interesting applications of #optimalTransport inside!
2/3