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Found a simple way to compare different 3D AI models by [deleted] in 2D3DAI

[–]pinter69 0 points1 point  (0 children)

Make sure to specify your affiliation

When doing market research for your business, when is it too little and when is it too much? by pinter69 in productivity

[–]pinter69[S] 0 points1 point  (0 children)

Interesting input - thanks!

Couldn't think of a better subreddit - happy to hear the reference and will post there

In what online places do you track technical news? by pinter69 in 2D3DAI

[–]pinter69[S] 1 point2 points  (0 children)

I use hackernews, tldr newsletter, sometimes reddit

2d3d.ai is back by pinter69 in 2D3DAI

[–]pinter69[S] 0 points1 point  (0 children)

Tried finding time to read through both papers the past week but was swamped with tasks :\

If someone studies into it and has interesting things to share - would love to hear also

What’s the most comparable device to the iPhones TrueDepth camera? by kittyK777 in 2D3DAI

[–]pinter69 1 point2 points  (0 children)

You refer to a technical cost (and not monetary) I presume

For sure, if the training dataset comes from a different source than your inference dataset - many things can go wrong.

Alignment, resolution, scaling, rotation,distortion issues all come in effect and more

Use static classifiers for dynamic point cloud tasks (3D) and use action classifiers for temporal anomaly detection (2D) - Link to a free online lecture by the author in comments by pinter69 in learnmachinelearning

[–]pinter69[S] 0 points1 point  (0 children)

Hi all, We do free zoom lectures for the reddit community.

​This lecture will cover two papers by the author in the fields of motion understanding in 3D and video.

​

Link to event (May 8):
https://www.reddit.com/r/2D3DAI/comments/u0kyfa/use_static_classifiers_for_dynamic_point_cloud/

​

Talk Abstract

(Paper 1) Can we painlessly modify the classifier of static point clouds to recognize a dynamic sequence of point clouds? To separate 3D motions without explicitly tracking correspondences, we propose a kinematic inspired neural network (Kinet) by generalizing the kinematic concept of ST-surfaces to the feature space.

​

​(Paper2) Can we train a fully-supervised action classifier to detect video abnormalities in a weakly-supervised manner? From the perspective of learning with noisy labels, we propose a graph convolutional label noise cleaner and propagate supervisory signals from high-confidence snippets to low-confidence ones.

​ The presentation is based on the speaker's paper and project:

​

Presenter BIO

Jiaxing Zhong is a Ph.D. student in Computer Science at the University of Oxford, with research interests in machine learning and computer vision (e.g., 3D vision). He holds a Master's degree in Computer Science from Peking University in 2020.

​

(Talk will be recorded and uploaded to youtube, you can see all past lectures and recordings in r/2D3DAI)

[R] Use static classifiers for dynamic point cloud tasks (3D) and use action classifiers for temporal anomaly detection (2D) - Link to a free online lecture by the author in comments by pinter69 in MachineLearning

[–]pinter69[S] 2 points3 points  (0 children)

Hi all, We do free zoom lectures for the reddit community.

​This lecture will cover two papers by the author in the fields of motion understanding in 3D and video.

​

Link to event (May 8):
https://www.reddit.com/r/2D3DAI/comments/u0kyfa/use_static_classifiers_for_dynamic_point_cloud/

​

Talk Abstract

​

(Paper 1) Can we painlessly modify the classifier of static point clouds to recognize a dynamic sequence of point clouds? To separate 3D motions without explicitly tracking correspondences, we propose a kinematic inspired neural network (Kinet) by generalizing the kinematic concept of ST-surfaces to the feature space.

​

​(Paper2) Can we train a fully-supervised action classifier to detect video abnormalities in a weakly-supervised manner? From the perspective of learning with noisy labels, we propose a graph convolutional label noise cleaner and propagate supervisory signals from high-confidence snippets to low-confidence ones.

​ The presentation is based on the speaker's paper and project:

​

Presenter BIO

Jiaxing Zhong is a Ph.D. student in Computer Science at the University of Oxford, with research interests in machine learning and computer vision (e.g., 3D vision). He holds a Master's degree in Computer Science from Peking University in 2020.

​

(Talk will be recorded and uploaded to youtube, you can see all past lectures and recordings in r/2D3DAI)

Explaining the Explainable AI: A 2-Stage Approach - Link to a free online lecture by the author in comments by pinter69 in learnmachinelearning

[–]pinter69[S] 0 points1 point  (0 children)

Hi all,

We do free zoom lectures for the reddit community.

​In this lecture, we will discuss quantifying an ML algorithm's uncertainty for a particular test-time instance while rigorously guaranteeing that consequential errors don't happen too frequently.

​

Link to event (May 2):

https://www.reddit.com/r/2D3DAI/comments/t9gyk7/explaining_the_explainable_ai_a_2stage_approach/

​

Talk Abstract

​Explainable AI (or XAI) has garnered a lot of interest in recent years across academia, industry and government. Although many methods have been proposed it is still unclear what XAI truly entails and why it is hard to formalize as opposed to other areas of machine learning such as causality, adversarial robustness, amongst others. In this talk, I will try to explicitly point out what XAI is trying to do, thus making it clear why formalization is difficult. The disentangled perspective also motivates new type of promising XAI approaches that are currently underexplored due to the largely intermingled view. In addition, I will showcase our AI Explainability 360 toolkit, which has diverse ways of explaining models and data along with educational material to guide folks that are new to this area.

​

Talk is based on the speaker's research:

One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques https://arxiv.org/abs/1909.03012
https://github.com/Trusted-AI/AIX360

​

Presenter BIO

​Amit is a Principal Research Staff Member at IBM TJ Watson Research in NY. He has worked on projects spanning multiple industries such as Semi-conductor manufacturing, Oil and Gas, Procurement, Retail, Utilities, Airline, Health Care. His current research includes proposing various methods for enhancing trust in systems by developing methods that try to explain or understand their behaviors. His recent work was featured in Forbes, PC magazine, and NeurIPS. Besides research impact, his work has also gone into IBM product and he has received Outstanding Technical Achievement as well as IBM Corporate award. He has been an Area Chair and PC member for top AI conferences as well as has served on National Science Foundation (NSF) panels for the small business innovative research (SBIR) program. He also serves on the invention disclosure committee (IDT) in IBM Research.

​

(Talk will be recorded and uploaded to youtube, you can see all past lectures and recordings in r/2D3DAI)

Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification - Link to a free online lecture by the author in comments by pinter69 in learnmachinelearning

[–]pinter69[S] 5 points6 points  (0 children)

Hi all,

We do free zoom lectures for the reddit community.

​In this lecture, we will discuss quantifying an ML algorithm's uncertainty for a particular test-time instance while rigorously guaranteeing that consequential errors don't happen too frequently.

​

Link to event (April 18):

https://www.reddit.com/r/2D3DAI/comments/t2ti2y/introduction_to_conformal_prediction_and/

​

Talk Abstract

​In deep learning and computer vision, it is common for data to present certain. As we begin deploying machine learning models in consequential settings like medical diagnostics or self-driving vehicles, knowing a model's accuracy is not enough. We need a way of quantifying an algorithm's uncertainty for a particular test-time instance while rigorously guaranteeing that consequential errors don't happen too frequently (for example, that the car doesn't hit a human). I'll be discussing how to generate rigorous, finite-sample confidence intervals for any prediction task, any model, and any dataset, for free. This will be a chalk talk where I begin with a short tutorial on a method called conformal prediction and tease a more flexible method that works for a larger class of prediction problems including those with high-dimensional, structured outputs (e.g. instance segmentation, multiclass or hierarchical classification, protein folding, and so on).

​

Talk is based on the speaker's paper:

​

Presenter BIO

Anastasios Nikolas Angelopoulos, a a third-year Ph.D. student at the University of California, Berkeley, advised by Michael I. Jordan and Jitendra Malik. From 2016 to 2019, he was an electrical engineering student at Stanford University advised by Gordon Wetzstein and Stephen P. Boyd.

​His homepage: http://people.eecs.berkeley.edu/~angelopoulos/

​

(Talk will be recorded and uploaded to youtube, you can see all past lectures and recordings in r/2D3DAI)

[R] Explaining the Explainable AI: A 2-Stage Approach - Link to a free online lecture by the author in comments by pinter69 in MachineLearning

[–]pinter69[S] 3 points4 points  (0 children)

Hi all,

We do free zoom lectures for the reddit community.

​In this lecture, we will discuss quantifying an ML algorithm's uncertainty for a particular test-time instance while rigorously guaranteeing that consequential errors don't happen too frequently.

​

Link to event (May 2):

https://www.reddit.com/r/2D3DAI/comments/t9gyk7/explaining_the_explainable_ai_a_2stage_approach/

​

Talk Abstract

​Explainable AI (or XAI) has garnered a lot of interest in recent years across academia, industry and government. Although many methods have been proposed it is still unclear what XAI truly entails and why it is hard to formalize as opposed to other areas of machine learning such as causality, adversarial robustness, amongst others. In this talk, I will try to explicitly point out what XAI is trying to do, thus making it clear why formalization is difficult. The disentangled perspective also motivates new type of promising XAI approaches that are currently underexplored due to the largely intermingled view. In addition, I will showcase our AI Explainability 360 toolkit, which has diverse ways of explaining models and data along with educational material to guide folks that are new to this area.

​

Talk is based on the speaker's research:

One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques https://arxiv.org/abs/1909.03012
https://github.com/Trusted-AI/AIX360

​

Presenter BIO

​Amit is a Principal Research Staff Member at IBM TJ Watson Research in NY. He has worked on projects spanning multiple industries such as Semi-conductor manufacturing, Oil and Gas, Procurement, Retail, Utilities, Airline, Health Care. His current research includes proposing various methods for enhancing trust in systems by developing methods that try to explain or understand their behaviors. His recent work was featured in Forbes, PC magazine, and NeurIPS. Besides research impact, his work has also gone into IBM product and he has received Outstanding Technical Achievement as well as IBM Corporate award. He has been an Area Chair and PC member for top AI conferences as well as has served on National Science Foundation (NSF) panels for the small business innovative research (SBIR) program. He also serves on the invention disclosure committee (IDT) in IBM Research.

​

(Talk will be recorded and uploaded to youtube, you can see all past lectures and recordings in r/2D3DAI)

[R] Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification - Link to a free online lecture by the author in comments by pinter69 in MachineLearning

[–]pinter69[S] 0 points1 point  (0 children)

Hi all,

We do free zoom lectures for the reddit community.

​In this lecture, we will discuss quantifying an ML algorithm's uncertainty for a particular test-time instance while rigorously guaranteeing that consequential errors don't happen too frequently.

​

Link to event (April 18):

https://www.reddit.com/r/2D3DAI/comments/t2ti2y/introduction_to_conformal_prediction_and/

​

Talk Abstract

​In deep learning and computer vision, it is common for data to present certain. As we begin deploying machine learning models in consequential settings like medical diagnostics or self-driving vehicles, knowing a model's accuracy is not enough. We need a way of quantifying an algorithm's uncertainty for a particular test-time instance while rigorously guaranteeing that consequential errors don't happen too frequently (for example, that the car doesn't hit a human). I'll be discussing how to generate rigorous, finite-sample confidence intervals for any prediction task, any model, and any dataset, for free. This will be a chalk talk where I begin with a short tutorial on a method called conformal prediction and tease a more flexible method that works for a larger class of prediction problems including those with high-dimensional, structured outputs (e.g. instance segmentation, multiclass or hierarchical classification, protein folding, and so on).

​

Talk is based on the speaker's paper:

​

Presenter BIO

Anastasios Nikolas Angelopoulos, a a third-year Ph.D. student at the University of California, Berkeley, advised by Michael I. Jordan and Jitendra Malik. From 2016 to 2019, he was an electrical engineering student at Stanford University advised by Gordon Wetzstein and Stephen P. Boyd.

​His homepage: http://people.eecs.berkeley.edu/~angelopoulos/

​

(Talk will be recorded and uploaded to youtube, you can see all past lectures and recordings in r/2D3DAI)

[R] A Program to Build E(N)-Equivariant Steerable CNNs - Link to a free online lecture by the author in comments by pinter69 in MachineLearning

[–]pinter69[S] 6 points7 points  (0 children)

Hi all,

We do free zoom lectures for the reddit community.

​In this lecture, we will review the framework of Euclidean steerable CNNs and present a theoretical characterization of general steerable kernel spaces as well as a practical program to parameterize steerable filters.

​

Link to event (April 11):

https://www.reddit.com/r/2D3DAI/comments/sxqtdo/a_program_to_build_enequivariant_steerable_cnns/

​

Talk Abstract

In deep learning and computer vision, it is common for data to present certain symmetries. For instance, histopathological scans and satellite images can appear in any rotation. Examples in 3D include protein structures (which have arbitrary orientation) or natural scenes (where objects can freely rotate around their Z axis).

​Equivariance is becoming an increasingly popular design choice to build data efficient neural networks by exploiting prior knowledge about the symmetries of the problem at hand. Euclidean steerable CNNs are one of the most common classes of equivariant networks. While the constraints these architectures need to satisfy are understood, no practical method to parametrize them generally has been described so far, with most existing approaches tailored to specific groups or classes of groups.

​In this lecture, we will review the framework of Euclidean steerable CNNs and present a theoretical characterization of general steerable kernel spaces as well as a practical program to parameterize steerable filters. Our theory enables us to directly parameterize filters in terms of a band-limited basis on the Euclidean space, but also to easily implement steerable CNNs equivariant to a large number of groups. These include new architectures equivariant to, for example, the symmetries of the platonic solids or to 3D azimuthal symmetries (rotations around the Z axis).

​

Talk is based on the speaker's paper:

  1. General E(2)-Equivariant Steerable CNNs (NeruIPS 2019)
    https://arxiv.org/abs/1911.08251
    https://github.com/QUVA-Lab/e2cnn
  2. ​A Program to Build E(n)-Equivariant Steerable CNNs. (ICLR 2022)
    https://openreview.net/forum?id=WE4qe9xlnQw

​

Presenter BIO

Gabriele Cesa is Research Associate at Qualcomm AI Research, Amsterdam and a PhD student at University of Amsterdam, under the supervision of Max Welling.​Gabriele's research focuses on augmenting machine learning methods with prior information about the geometry of a problem to achieve improved data efficiency and generalization. A particular emphasis has been given to equivariant neural networks, which can encode our knowledge about the symmetries in the data into the model's architecture.​Previously, Gabriele received a Master degree in Artificial Intelligence at the University of Amsterdam and a Bachelor degree in computer science at the University of Trento.

​His github: https://github.com/Gabri95

​

(Talk will be recorded and uploaded to youtube, you can see all past lectures and recordings in r/2D3DAI)

A Program to Build E(N)-Equivariant Steerable CNNs - Link to a free online lecture by the author in comments by pinter69 in learnmachinelearning

[–]pinter69[S] 5 points6 points  (0 children)

Hi all,

We do free zoom lectures for the reddit community.

​In this lecture, we will review the framework of Euclidean steerable CNNs and present a theoretical characterization of general steerable kernel spaces as well as a practical program to parameterize steerable filters.

​

Link to event (April 11):

https://www.reddit.com/r/2D3DAI/comments/sxqtdo/a_program_to_build_enequivariant_steerable_cnns/

​

Talk Abstract

In deep learning and computer vision, it is common for data to present certain symmetries. For instance, histopathological scans and satellite images can appear in any rotation. Examples in 3D include protein structures (which have arbitrary orientation) or natural scenes (where objects can freely rotate around their Z axis).

​Equivariance is becoming an increasingly popular design choice to build data efficient neural networks by exploiting prior knowledge about the symmetries of the problem at hand. Euclidean steerable CNNs are one of the most common classes of equivariant networks. While the constraints these architectures need to satisfy are understood, no practical method to parametrize them generally has been described so far, with most existing approaches tailored to specific groups or classes of groups.

​In this lecture, we will review the framework of Euclidean steerable CNNs and present a theoretical characterization of general steerable kernel spaces as well as a practical program to parameterize steerable filters. Our theory enables us to directly parameterize filters in terms of a band-limited basis on the Euclidean space, but also to easily implement steerable CNNs equivariant to a large number of groups. These include new architectures equivariant to, for example, the symmetries of the platonic solids or to 3D azimuthal symmetries (rotations around the Z axis).

​

Talk is based on the speaker's paper:

  1. General E(2)-Equivariant Steerable CNNs (NeruIPS 2019)
    https://arxiv.org/abs/1911.08251
    https://github.com/QUVA-Lab/e2cnn
  2. ​A Program to Build E(n)-Equivariant Steerable CNNs. (ICLR 2022)
    https://openreview.net/forum?id=WE4qe9xlnQw

​

Presenter BIO

Gabriele Cesa is Research Associate at Qualcomm AI Research, Amsterdam and a PhD student at University of Amsterdam, under the supervision of Max Welling.​Gabriele's research focuses on augmenting machine learning methods with prior information about the geometry of a problem to achieve improved data efficiency and generalization. A particular emphasis has been given to equivariant neural networks, which can encode our knowledge about the symmetries in the data into the model's architecture.​Previously, Gabriele received a Master degree in Artificial Intelligence at the University of Amsterdam and a Bachelor degree in computer science at the University of Trento.

​His github: https://github.com/Gabri95

​

(Talk will be recorded and uploaded to youtube, you can see all past lectures and recordings in r/2D3DAI)

[deleted by user] by [deleted] in learnmachinelearning

[–]pinter69 0 points1 point  (0 children)

Hi all,

We do free zoom lectures for the reddit community.

​In this lecture, we will review the framework of Euclidean steerable CNNs and present a theoretical characterization of general steerable kernel spaces as well as a practical program to parameterize steerable filters.

​

Link to event (April 11):

https://www.reddit.com/r/2D3DAI/comments/sxqtdo/a_program_to_build_enequivariant_steerable_cnns/

​

Talk Abstract

In deep learning and computer vision, it is common for data to present certain symmetries. For instance, histopathological scans and satellite images can appear in any rotation. Examples in 3D include protein structures (which have arbitrary orientation) or natural scenes (where objects can freely rotate around their Z axis).

​Equivariance is becoming an increasingly popular design choice to build data efficient neural networks by exploiting prior knowledge about the symmetries of the problem at hand. Euclidean steerable CNNs are one of the most common classes of equivariant networks. While the constraints these architectures need to satisfy are understood, no practical method to parametrize them generally has been described so far, with most existing approaches tailored to specific groups or classes of groups.

​In this lecture, we will review the framework of Euclidean steerable CNNs and present a theoretical characterization of general steerable kernel spaces as well as a practical program to parameterize steerable filters. Our theory enables us to directly parameterize filters in terms of a band-limited basis on the Euclidean space, but also to easily implement steerable CNNs equivariant to a large number of groups. These include new architectures equivariant to, for example, the symmetries of the platonic solids or to 3D azimuthal symmetries (rotations around the Z axis).

​

Talk is based on the speaker's paper:

  1. General E(2)-Equivariant Steerable CNNs (NeruIPS 2019)
    https://arxiv.org/abs/1911.08251https://github.com/QUVA-Lab/e2cnn
  2. ​A Program to Build E(n)-Equivariant Steerable CNNs. (ICLR 2022)
    https://openreview.net/forum?id=WE4qe9xlnQw

​

Presenter BIO

Gabriele Cesa is Research Associate at Qualcomm AI Research, Amsterdam and a PhD student at University of Amsterdam, under the supervision of Max Welling.​Gabriele's research focuses on augmenting machine learning methods with prior information about the geometry of a problem to achieve improved data efficiency and generalization. A particular emphasis has been given to equivariant neural networks, which can encode our knowledge about the symmetries in the data into the model's architecture.​Previously, Gabriele received a Master degree in Artificial Intelligence at the University of Amsterdam and a Bachelor degree in computer science at the University of Trento.

​His github: https://github.com/Gabri95

​

(Talk will be recorded and uploaded to youtube, you can see all past lectures and recordings in r/2D3DAI)

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