Mathias Niepert
@mniepert.bsky.social
Professor @ University of Stuttgart, Scientific Advisor @ NEC Labs, GraphML, geometric deep learning, ML for Science and Simulations. Formerly @IUBloomington and @uwcse
Reposted by Mathias Niepert
🚨 New preprint: How well do universal ML potentials perform in biomolecular simulations under realistic conditions?
There's growing excitement around ML potentials trained on large datasets.
But do they deliver in simulations of biomolecular systems?
It’s not so clear. 🧵
1/
There's growing excitement around ML potentials trained on large datasets.
But do they deliver in simulations of biomolecular systems?
It’s not so clear. 🧵
1/
August 15, 2025 at 8:30 AM
🚨 New preprint: How well do universal ML potentials perform in biomolecular simulations under realistic conditions?
There's growing excitement around ML potentials trained on large datasets.
But do they deliver in simulations of biomolecular systems?
It’s not so clear. 🧵
1/
There's growing excitement around ML potentials trained on large datasets.
But do they deliver in simulations of biomolecular systems?
It’s not so clear. 🧵
1/
Anji is an amazing mentor and colleague. If I could go for another PhD in CS I would apply!
🎓 Looking for PhD students, postdocs & interns!
I’m recruiting for my new lab at NUS School of Computing, focusing on generative modeling, reasoning, and tractable inference.
💡 Interested? Learn more here: liuanji.github.io
🗓️ PhD application deadline: June 15, 2025
I’m recruiting for my new lab at NUS School of Computing, focusing on generative modeling, reasoning, and tractable inference.
💡 Interested? Learn more here: liuanji.github.io
🗓️ PhD application deadline: June 15, 2025
Anji Liu
Incoming Assistant Professor at NUS working on tractable deep generative models.
liuanji.github.io
May 17, 2025 at 5:31 PM
Anji is an amazing mentor and colleague. If I could go for another PhD in CS I would apply!
Reposted by Mathias Niepert
🚨ICLR poster in 1.5 hours, presented by @danielmusekamp.bsky.social :
Can active learning help to generate better datasets for neural PDE solvers?
We introduce a new benchmark to find out!
Featuring 6 PDEs, 6 AL methods, 3 architectures and many ablations - transferability, speed, etc.!
Can active learning help to generate better datasets for neural PDE solvers?
We introduce a new benchmark to find out!
Featuring 6 PDEs, 6 AL methods, 3 architectures and many ablations - transferability, speed, etc.!
April 24, 2025 at 12:38 AM
🚨ICLR poster in 1.5 hours, presented by @danielmusekamp.bsky.social :
Can active learning help to generate better datasets for neural PDE solvers?
We introduce a new benchmark to find out!
Featuring 6 PDEs, 6 AL methods, 3 architectures and many ablations - transferability, speed, etc.!
Can active learning help to generate better datasets for neural PDE solvers?
We introduce a new benchmark to find out!
Featuring 6 PDEs, 6 AL methods, 3 architectures and many ablations - transferability, speed, etc.!
Reposted by Mathias Niepert
Authors: Marimuthu Kalimuthu, @dholzmueller.bsky.social, @mniepert.bsky.social
Full text: openreview.net/forum?id=OCM...
Full text: openreview.net/forum?id=OCM...
LOGLO-FNO: Efficient Learning of Local and Global Features in...
Learning local features and high frequencies is an important problem in Scientific Machine Learning. For instance, effectively modeling turbulence (e.g., $Re=3500$ and above) depends on accurately...
openreview.net
March 18, 2025 at 8:13 AM
Authors: Marimuthu Kalimuthu, @dholzmueller.bsky.social, @mniepert.bsky.social
Full text: openreview.net/forum?id=OCM...
Full text: openreview.net/forum?id=OCM...
Reposted by Mathias Niepert
The slides for my lectures on (Bayesian) Active Learning, Information Theory, and Uncertainty are online now 🥳 They cover quite a bit from basic information theory to some recent papers:
blackhc.github.io/balitu/
and I'll try to add proper course notes over time 🤗
blackhc.github.io/balitu/
and I'll try to add proper course notes over time 🤗
December 17, 2024 at 6:50 AM
The slides for my lectures on (Bayesian) Active Learning, Information Theory, and Uncertainty are online now 🥳 They cover quite a bit from basic information theory to some recent papers:
blackhc.github.io/balitu/
and I'll try to add proper course notes over time 🤗
blackhc.github.io/balitu/
and I'll try to add proper course notes over time 🤗
Reposted by Mathias Niepert
[9/n] Beyond Image Generation
LD3 can be applied to diffusion models in other domains, such as molecular docking.
LD3 can be applied to diffusion models in other domains, such as molecular docking.
February 13, 2025 at 8:31 AM
[9/n] Beyond Image Generation
LD3 can be applied to diffusion models in other domains, such as molecular docking.
LD3 can be applied to diffusion models in other domains, such as molecular docking.
Reposted by Mathias Niepert
Want to turn your state-of-the-art diffusion models into ultra-fast few-step generators? 🚀
Learn how to optimize your time discretization strategy—in just ~10 minutes! ⏳✨
Check out how it's done in our Oral paper at ICLR 2025 👇
Learn how to optimize your time discretization strategy—in just ~10 minutes! ⏳✨
Check out how it's done in our Oral paper at ICLR 2025 👇
🚀 Exciting news! Our paper "Learning to Discretize Diffusion ODEs" has been accepted as an Oral at #ICLR2025! 🎉
[1/n]
We propose LD3, a lightweight framework that learns the optimal time discretization for sampling from pre-trained Diffusion Probabilistic Models (DPMs).
[1/n]
We propose LD3, a lightweight framework that learns the optimal time discretization for sampling from pre-trained Diffusion Probabilistic Models (DPMs).
February 13, 2025 at 8:44 AM
Want to turn your state-of-the-art diffusion models into ultra-fast few-step generators? 🚀
Learn how to optimize your time discretization strategy—in just ~10 minutes! ⏳✨
Check out how it's done in our Oral paper at ICLR 2025 👇
Learn how to optimize your time discretization strategy—in just ~10 minutes! ⏳✨
Check out how it's done in our Oral paper at ICLR 2025 👇
Reposted by Mathias Niepert
Welcome to our Bluesky account! 🦋
We're excited to announce ComBayNS workshop: Combining Bayesian & Neural Approaches for Structured Data 🌐
Submit your paper and join us in Rome for #IJCNN2025! 🇮🇹
📅 Papers Due: March 20th, 2025 📜
Webpage: combayns2025.github.io
We're excited to announce ComBayNS workshop: Combining Bayesian & Neural Approaches for Structured Data 🌐
Submit your paper and join us in Rome for #IJCNN2025! 🇮🇹
📅 Papers Due: March 20th, 2025 📜
Webpage: combayns2025.github.io
Home - ComBayNS 2025 Workshop @ IJCNN 2025
combayns2025.github.io
February 13, 2025 at 9:18 AM
Welcome to our Bluesky account! 🦋
We're excited to announce ComBayNS workshop: Combining Bayesian & Neural Approaches for Structured Data 🌐
Submit your paper and join us in Rome for #IJCNN2025! 🇮🇹
📅 Papers Due: March 20th, 2025 📜
Webpage: combayns2025.github.io
We're excited to announce ComBayNS workshop: Combining Bayesian & Neural Approaches for Structured Data 🌐
Submit your paper and join us in Rome for #IJCNN2025! 🇮🇹
📅 Papers Due: March 20th, 2025 📜
Webpage: combayns2025.github.io
Reposted by Mathias Niepert
🚀 Exciting news! Our paper "Learning to Discretize Diffusion ODEs" has been accepted as an Oral at #ICLR2025! 🎉
[1/n]
We propose LD3, a lightweight framework that learns the optimal time discretization for sampling from pre-trained Diffusion Probabilistic Models (DPMs).
[1/n]
We propose LD3, a lightweight framework that learns the optimal time discretization for sampling from pre-trained Diffusion Probabilistic Models (DPMs).
February 13, 2025 at 8:31 AM
🚀 Exciting news! Our paper "Learning to Discretize Diffusion ODEs" has been accepted as an Oral at #ICLR2025! 🎉
[1/n]
We propose LD3, a lightweight framework that learns the optimal time discretization for sampling from pre-trained Diffusion Probabilistic Models (DPMs).
[1/n]
We propose LD3, a lightweight framework that learns the optimal time discretization for sampling from pre-trained Diffusion Probabilistic Models (DPMs).
Reposted by Mathias Niepert
Very excited to announce the Neurosymbolic Generative Models special track at NeSy 2025! Looking forward to all your submissions!
Together with @kareemyousrii.bsky.social, we announce the Neurosymbolic Generative Models special track at the NeSy 2025 Conference 🎉
Call for Papers is live!
2025.nesyconf.org/nesy-generat...
See you in Santa Cruz in Sep 2025!
@nesyconf.org
Call for Papers is live!
2025.nesyconf.org/nesy-generat...
See you in Santa Cruz in Sep 2025!
@nesyconf.org
Neurosymbolic Generative Models
19th International Conference on Neurosymbolic Learning and Reasoning (NeSy 2025, 8-10 September 2025, Santa Cruz, CA, USA)
2025.nesyconf.org
December 20, 2024 at 8:43 PM
Very excited to announce the Neurosymbolic Generative Models special track at NeSy 2025! Looking forward to all your submissions!
Reposted by Mathias Niepert
arxiv.org/abs/2412.11569, a very relevant effort!
The dark side of the forces: assessing non-conservative force models for atomistic machine learning
The use of machine learning to estimate the energy of a group of atoms, and the forces that drive them to more stable configurations, have revolutionized the fields of computational chemistry and mate...
arxiv.org
December 17, 2024 at 7:07 AM
arxiv.org/abs/2412.11569, a very relevant effort!
Reposted by Mathias Niepert
Catch my poster tomorrow at the NeurIPS MLSB Workshop! We present a simple (yet effective 😁) multimodal Transformer for molecules, supporting multiple 3D conformations & showing promise for transfer learning.
Interested in molecular representation learning? Let’s chat 👋!
Interested in molecular representation learning? Let’s chat 👋!
December 15, 2024 at 12:32 AM
Catch my poster tomorrow at the NeurIPS MLSB Workshop! We present a simple (yet effective 😁) multimodal Transformer for molecules, supporting multiple 3D conformations & showing promise for transfer learning.
Interested in molecular representation learning? Let’s chat 👋!
Interested in molecular representation learning? Let’s chat 👋!
We will run out of data for pretraining and see diminishing returns. In many application domains such as in the sciences we also have to be very careful on what data we pretrain to be effective. It is important to adaptively generate new data from physical simulators. Excited about the work below
Neural surrogates can accelerate PDE solving but need expensive ground-truth training data. Can we reduce the training data size with active learning (AL)? In our NeurIPS D3S3 poster, we introduce AL4PDE, an extensible AL benchmark for autoregressive neural PDE solvers. 🧵
December 14, 2024 at 1:02 PM
We will run out of data for pretraining and see diminishing returns. In many application domains such as in the sciences we also have to be very careful on what data we pretrain to be effective. It is important to adaptively generate new data from physical simulators. Excited about the work below
Reposted by Mathias Niepert
I'll present our paper in the afternoon poster session at 4:30pm - 7:30 pm in East Exhibit Hall A-C, poster 3304!
Can deep learning finally compete with boosted trees on tabular data? 🌲
In our NeurIPS 2024 paper, we introduce RealMLP, a NN with improvements in all areas and meta-learned default parameters.
Some insights about RealMLP and other models on large benchmarks (>200 datasets): 🧵
In our NeurIPS 2024 paper, we introduce RealMLP, a NN with improvements in all areas and meta-learned default parameters.
Some insights about RealMLP and other models on large benchmarks (>200 datasets): 🧵
December 12, 2024 at 6:54 PM
I'll present our paper in the afternoon poster session at 4:30pm - 7:30 pm in East Exhibit Hall A-C, poster 3304!
Reposted by Mathias Niepert
Neural surrogates can accelerate PDE solving but need expensive ground-truth training data. Can we reduce the training data size with active learning (AL)? In our NeurIPS D3S3 poster, we introduce AL4PDE, an extensible AL benchmark for autoregressive neural PDE solvers. 🧵
December 11, 2024 at 6:22 PM
Neural surrogates can accelerate PDE solving but need expensive ground-truth training data. Can we reduce the training data size with active learning (AL)? In our NeurIPS D3S3 poster, we introduce AL4PDE, an extensible AL benchmark for autoregressive neural PDE solvers. 🧵
Reposted by Mathias Niepert
Join us today at #NeurIPS2024 for our poster presentation:
Higher-Rank Irreducible Cartesian Tensors for Equivariant Message Passing
🗓️ When: Wed, Dec 11, 11 a.m. – 2 p.m. PST
📍 Where: East Exhibit Hall A-C, Poster #4107
#MachineLearning #InteratomicPotentials #Equivariance #GraphNeuralNetworks
Higher-Rank Irreducible Cartesian Tensors for Equivariant Message Passing
🗓️ When: Wed, Dec 11, 11 a.m. – 2 p.m. PST
📍 Where: East Exhibit Hall A-C, Poster #4107
#MachineLearning #InteratomicPotentials #Equivariance #GraphNeuralNetworks
📣 Can we go beyond state-of-the-art message-passing models based on spherical tensors such as #MACE and #NequIP?
Our #NeurIPS2024 paper explores higher-rank irreducible Cartesian tensors to design equivariant #MLIPs.
Paper: arxiv.org/abs/2405.14253
Code: github.com/nec-research...
Our #NeurIPS2024 paper explores higher-rank irreducible Cartesian tensors to design equivariant #MLIPs.
Paper: arxiv.org/abs/2405.14253
Code: github.com/nec-research...
December 11, 2024 at 3:38 PM
Join us today at #NeurIPS2024 for our poster presentation:
Higher-Rank Irreducible Cartesian Tensors for Equivariant Message Passing
🗓️ When: Wed, Dec 11, 11 a.m. – 2 p.m. PST
📍 Where: East Exhibit Hall A-C, Poster #4107
#MachineLearning #InteratomicPotentials #Equivariance #GraphNeuralNetworks
Higher-Rank Irreducible Cartesian Tensors for Equivariant Message Passing
🗓️ When: Wed, Dec 11, 11 a.m. – 2 p.m. PST
📍 Where: East Exhibit Hall A-C, Poster #4107
#MachineLearning #InteratomicPotentials #Equivariance #GraphNeuralNetworks
Reposted by Mathias Niepert
"Transferability of atom-based neural networks" authored by @januseriksen.bsky.social (thanks for publishing with us, amazing work!) is now out as part of the #QuantumChemistry and #ArtificialIntelligence focus collection #MachineLearningScienceandTechnology. Link: iopscience.iop.org/article/10.1...
Transferability of atom-based neural networks - IOPscienceSearch
Transferability of atom-based neural networks, Frederik Ø Kjeldal, Janus J Eriksen
iopscience.iop.org
December 10, 2024 at 8:06 PM
"Transferability of atom-based neural networks" authored by @januseriksen.bsky.social (thanks for publishing with us, amazing work!) is now out as part of the #QuantumChemistry and #ArtificialIntelligence focus collection #MachineLearningScienceandTechnology. Link: iopscience.iop.org/article/10.1...
Reposted by Mathias Niepert
1/6 We're excited to share our #NeurIPS2024 paper: Probabilistic Graph Rewiring via Virtual Nodes! It addresses key challenges in GNNs, such as over-squashing and under-reaching, while reducing reliance on heuristic rewiring. w/ Chendi Qian, @christophermorris.bsky.social @mniepert.bsky.social 🧵
December 7, 2024 at 5:50 PM
1/6 We're excited to share our #NeurIPS2024 paper: Probabilistic Graph Rewiring via Virtual Nodes! It addresses key challenges in GNNs, such as over-squashing and under-reaching, while reducing reliance on heuristic rewiring. w/ Chendi Qian, @christophermorris.bsky.social @mniepert.bsky.social 🧵
Reposted by Mathias Niepert
New #compchem paper out in MLST. We study the transferability of both invariant and equivariant neural networks when training these either exclusively on total molecular energies or in combination with data from different atomic partitioning schemes:
iopscience.iop.org/article/10.1...
iopscience.iop.org/article/10.1...
Transferability of atom-based neural networks - IOPscienceSearch
Transferability of atom-based neural networks, Frederik Ø Kjeldal, Janus J Eriksen
iopscience.iop.org
December 7, 2024 at 8:49 AM
New #compchem paper out in MLST. We study the transferability of both invariant and equivariant neural networks when training these either exclusively on total molecular energies or in combination with data from different atomic partitioning schemes:
iopscience.iop.org/article/10.1...
iopscience.iop.org/article/10.1...
You should take a look at this if you want to know how to use Cartesian (instead of spherical) tensors for building equivariant MLIPs.
📣 Can we go beyond state-of-the-art message-passing models based on spherical tensors such as #MACE and #NequIP?
Our #NeurIPS2024 paper explores higher-rank irreducible Cartesian tensors to design equivariant #MLIPs.
Paper: arxiv.org/abs/2405.14253
Code: github.com/nec-research...
Our #NeurIPS2024 paper explores higher-rank irreducible Cartesian tensors to design equivariant #MLIPs.
Paper: arxiv.org/abs/2405.14253
Code: github.com/nec-research...
December 6, 2024 at 2:57 PM
You should take a look at this if you want to know how to use Cartesian (instead of spherical) tensors for building equivariant MLIPs.
Reposted by Mathias Niepert
📣 Can we go beyond state-of-the-art message-passing models based on spherical tensors such as #MACE and #NequIP?
Our #NeurIPS2024 paper explores higher-rank irreducible Cartesian tensors to design equivariant #MLIPs.
Paper: arxiv.org/abs/2405.14253
Code: github.com/nec-research...
Our #NeurIPS2024 paper explores higher-rank irreducible Cartesian tensors to design equivariant #MLIPs.
Paper: arxiv.org/abs/2405.14253
Code: github.com/nec-research...
December 6, 2024 at 2:45 PM
📣 Can we go beyond state-of-the-art message-passing models based on spherical tensors such as #MACE and #NequIP?
Our #NeurIPS2024 paper explores higher-rank irreducible Cartesian tensors to design equivariant #MLIPs.
Paper: arxiv.org/abs/2405.14253
Code: github.com/nec-research...
Our #NeurIPS2024 paper explores higher-rank irreducible Cartesian tensors to design equivariant #MLIPs.
Paper: arxiv.org/abs/2405.14253
Code: github.com/nec-research...
Reposted by Mathias Niepert
@ropeharz.bsky.social forced me to do this starter pack on #tractable #probabilistic modeling and #reasoning in #AI and #ML
please write below if you want to be added (and sorry if I did not find you from the beginning).
go.bsky.app/DhVNyz5
please write below if you want to be added (and sorry if I did not find you from the beginning).
go.bsky.app/DhVNyz5
November 29, 2024 at 1:11 PM
@ropeharz.bsky.social forced me to do this starter pack on #tractable #probabilistic modeling and #reasoning in #AI and #ML
please write below if you want to be added (and sorry if I did not find you from the beginning).
go.bsky.app/DhVNyz5
please write below if you want to be added (and sorry if I did not find you from the beginning).
go.bsky.app/DhVNyz5
Reposted by Mathias Niepert
Amazing opportunity for #Neurosymbolic folks! 🚨🚨🚨
We are looking for a Tenure Track Prof for the 🇦🇹 #FWF Cluster of Excellence Bilateral AI (think #NeSy ++) www.bilateral-ai.net A nice starting pack for fully funded PhDs is included.
jobs.tugraz.at/en/jobs/226f...
We are looking for a Tenure Track Prof for the 🇦🇹 #FWF Cluster of Excellence Bilateral AI (think #NeSy ++) www.bilateral-ai.net A nice starting pack for fully funded PhDs is included.
jobs.tugraz.at/en/jobs/226f...
BilateralAI – Bilateral AI – Cluster of Excellence
www.bilateral-ai.net
November 28, 2024 at 10:42 AM
Amazing opportunity for #Neurosymbolic folks! 🚨🚨🚨
We are looking for a Tenure Track Prof for the 🇦🇹 #FWF Cluster of Excellence Bilateral AI (think #NeSy ++) www.bilateral-ai.net A nice starting pack for fully funded PhDs is included.
jobs.tugraz.at/en/jobs/226f...
We are looking for a Tenure Track Prof for the 🇦🇹 #FWF Cluster of Excellence Bilateral AI (think #NeSy ++) www.bilateral-ai.net A nice starting pack for fully funded PhDs is included.
jobs.tugraz.at/en/jobs/226f...
Reposted by Mathias Niepert
I haven’t read it carefully, but +1 to works like the one below. It mentions learning artifacts from discreetness. We saw some things like that in this paper, where bad integration of the true Hamiltonian did worse than a learned model (that absorbed artifacts).
arxiv.org/abs/1909.12790
arxiv.org/abs/1909.12790
November 26, 2024 at 1:17 PM
I haven’t read it carefully, but +1 to works like the one below. It mentions learning artifacts from discreetness. We saw some things like that in this paper, where bad integration of the true Hamiltonian did worse than a learned model (that absorbed artifacts).
arxiv.org/abs/1909.12790
arxiv.org/abs/1909.12790
Reposted by Mathias Niepert
November 24, 2024 at 1:36 AM