Ahmed Elhag
@Ahmed_AI035
Followers
615
Following
225
Media
16
Statuses
128
PhD Student in Computer Science @UniofOxford / Fellow @hilda_beastoxf /
Joined July 2020
Don't wanna be here?
Send us removal request.
Explore trending content on Musk Viewer
No Riley
• 550825 Tweets
Chile
• 194726 Tweets
Perú
• 125829 Tweets
#NuNew1stConcert
• 90923 Tweets
Oilers
• 84631 Tweets
#holoJustice
• 54553 Tweets
Game 7
• 52524 Tweets
Edmonton
• 49827 Tweets
Panthers
• 44888 Tweets
Gigi
• 33787 Tweets
Jacob Fatu
• 32204 Tweets
#กดบัตรDREAMCATCHER
• 21118 Tweets
EVANGELISTAS FURIOSOS
• 17356 Tweets
エンゼルス
• 16639 Tweets
ラグビー
• 14076 Tweets
MINA IN MALAYSIA
• 13393 Tweets
Pinned Tweet
Best Student Paper Award-:)
Congrats to all the team and thanks to the workshop organizers!
I'll present Manifold Diffusion Fields as an oral at the Diffusion Models workshop
@NeurIPSConf
, today 2:30 pm New Orleans time!!
work with
@YuyangW95
@jsusskin
and
@itsbautistam
Don't forget to join and let's know your questions and feedback!
2
16
158
10
22
287
I'll present Manifold Diffusion Fields as an oral at the Diffusion Models workshop
@NeurIPSConf
, today 2:30 pm New Orleans time!!
work with
@YuyangW95
@jsusskin
and
@itsbautistam
Don't forget to join and let's know your questions and feedback!
2
16
158
📢Very excited to share our work Graph Anisotropic Diffusion, which I will present today at two
#ICLR2022
workshops! (GTRL and MLDD) 🎉🥳🥳🥳
Extremely grateful to my advisor
@mmbronstein
and my collaborators
@GabriCorso
and
@HannesStaerk
🤗
7
33
136
Personal news: Happy to announce that I’ve joined
@Apple
MLR as a research intern!🎉🥳
I’ll be in
#Cupertino
office joining the team of
@jsusskin
. So excited about this, and looking forward to working with an awesome team at Apple!
8
5
97
Starting a series of blog posts on our GDL course, taught by
@mmbronstein
,
@joanbruna
,
@TacoCohen
, and
@PetarV_93
.
The Erlangen Programme of ML, co-authored with
@Mohamed87290109
1
7
73
Very excited to present Manifold Diffusion Fields (MDF): A generalization of diffusion generative models over continuous functions defined on manifolds!! Work done through my internship with a wonderful team
@jsusskin
and
@itsbautistam
at
@Apple
MLR!🥳
Introducing Manifold Diffusion Fields (MDF), our new work on learning generative models over fields defined on curved geometries. This is joint work with our intern
@Ahmed_AI035
(who hasn’t even started his PhD yet!) and
@jsusskin
at
@Apple
MLR 🧵
1
28
117
2
2
36
Thrilled to be a Fellow at the Summer Geometry Initiative (SGI) 2022 at
@MIT
organised by
@JustinMSolomon
! So excited to work on
#geometryprocessing
research!
1
4
36
A new post in our series on the GDL course taught by
@mmbronstein
,
@joanbruna
,
@TacoCohen
, and
@PetarV_93
High-Dimensional Learning, co-authored with
@Mohamed87290109
1
4
31
Following our
#GDL
series, In a new post at
@TDataScience
coauthored with
@Mohamed87290109
, we show that symmetry alone is not sufficient to break the curse of dimensionality.
1
7
24
A new post
@TDataScience
discusses the concept of symmetry in ML and GDL, as well as several mathematical ideas such as abstract groups, group actions, & group representations, and ends up with invariant & equivariant networks in deep learning.
0
8
19
Happy to present Manifold Diffusion Fields at
@HannesStaerk
reading group today at 4 pm UK!
If you are interested in generative models for 3D meshes and graphs, join us in ~2h!
Tomorrow (Mon) in le reading group: "Manifold Diffusion Fields"
With
@Ahmed_AI035
.
Join us on Zoom at 11am ET / 5pm CET:
3
8
60
0
3
22
I’ll not say it is the end but rather is a new start! Great time with a wonderful people at
@Apple
MLR, I really enjoyed it! Special thanks to
@jsusskin
and
@itsbautistam
!
Exciting news coming soon!
2
1
16
In this paper, we propose a linear diffusion layer with a learnable kernel size combined with an anisotropic filter, in a novel GNN architecture that we call Graph Anisotropic Diffusion (GAD).
1
0
10
We obtain an efficient multi-hop anisotropic kernels, and show a competitive performance on molecular property prediction benchmarks.
0
0
9
Check out our two posts discussing *SIREN and DIGS* methods for 3D object representation, coauthored with Alisia Lupidi and Krishnendu Kar.
1
1
9
Nice to see so much interests here! Formulating molecules conformers as functions on graphs! Simple idea and great performance
1/n New preprint alert! Introducing Generative Molecular Conformer Fields (MCF) a generative model for molecular conformer generation that obtains state-of-the-art results without using any domain specific inductive biases!
4
33
247
0
0
6
@y0b1byte
@mmbronstein
@GabriCorso
@HannesStaerk
Thanks! I made many images using LaTeX TikZ, and then animated them using
0
0
5
0
0
4
Very sad to see war breaking out in my country Sudan between the Rapid Support Forces and the Sudanese Army Forces. Even worse, this is happening within cities and states starting from Khartoum.
1
2
3
0
0
3
Furthermore, MDF can be test on out-of-distribution data (4x mesh resolution!!)
1
0
3
@BlackHC
@NeurIPSConf
@YuyangW95
@jsusskin
@itsbautistam
I think you need to login to openreview, here an arxiv version
0
0
1
This gives MDF a strong robustness property at inference time and making it invariant to rigid and isometric transformations in 3D space.
1
0
2
MDF achieved superior results and has been able to learn different distributions of functions over diverse geometries and with high-quality samples. Example: MDF can generate a moving Gaussian Mixture at different locations on the paw and tail of the cat geometry.
1
0
2
Its victims are innocent, defenseless residents who have nothing to do with power or politics. May Allah stops the bloodshed and we fully support the Sudanese Armed Forces in maintaining security and stability in the country.
0
0
3
@NtinosBarmpas
@mmbronstein
@GabriCorso
@HannesStaerk
we tested here on molecules graphs, but yes it is not specific to molecules
0
0
2
1
0
2
These posts are based on the project “Implicit Neural Representation (INR) based on the Geometric Information of Shapes” during SGI 2022, under the mentor of Dena Bazazian and Shaimaa Abdelhafez.
1
0
2
In this post we review some of the basics in statistical learning tasks, the curse of dimensionality, and lastly we introduce the geometric domains and their assumptions on the input data.
0
0
2
In MDF, we represent training samples as functions that go from a manifold M to signal space Y. Then we use the eigenfunctions of the Laplacian as a local coordinate system for points of M which consider an intrinsic representation.
2
0
1
0
0
1
Based on INR concept, we then discuss and compare the Sinusoidal Representation Network (SIREN) and its enhanced version Divergence Guided Shape Implicit Neural Representation (DiGS) from the papers and
2
0
1
0
0
1
0
0
1
0
0
1
@linminhtoo
@mmbronstein
@GabriCorso
@HannesStaerk
It helps to obtain long-range interactions between nodes as shown in animation
1
0
1
I'd like to see the long-term impact of AI and neural networks in our lives, what they can do and what they can't.. is this predictable?
0
0
1
In later posts we will explain in detail how by the two properties Symmetry and Scale Separation we can develop a GDL Blueprint that can serve as a framework for current state-of-the-art architectures.
1
0
1
@itsbautistam
@jsusskin
@Apple
Thanks Miguel for the kind words! It has been an amazing experience for me! Really grateful for both of you!!
0
0
1
We will discuss the so-called Geometric Domains or the 5 Gs which include Grids, Groups, Graphs, Geodesics, and Gauges, and their appropriate structure, in the pipeline of the GDL Blueprint.
1
0
1
0
0
1
We also show that MDF opens an interesting application in solving forward and reverse PDEs problems such as the heat diffusion!
0
0
1
Then we discuss the scale separation prior, how it rises from the multiscale structure, and how it is crucial to break the curse. And finally, we conclude with the GDL Blueprint as a general framework that can be applied to various geometric domains.
1
0
1
In the first post, we provide a theoretical walkthrough introducing the concept of implicit neural representation (INR) for representing a 3D object.
1
0
1
In this post, we review the definition of the word symmetry among various mathematicians touching on some historical context, the appearance of the Erlangen Programme, and how it came to deep learning in the name Geometric Deep Learning
Happy for any comments!
1
0
1