Andy Radler

PhD student of Artificial Intelligence at JKU Linz under the supervision of Johannes Brandstetter and Sepp Hochreiter.

[Andy Radler @AndyRadler]

Proud of our Geometry-Informed Neural Net (GINN🍸) which generates diverse shapes without training data.

Paper: https://t.co/GNsDL64lDn

Great work and thanks to my amazing team mates and supervisors @artuursberzins @e_volkmann @SebSanokowski @HochreiterSepp @jo_brandstetter

[Arturs Berzins @artuursberzins]

Geometry-Informed Neural Networks are evolving! Beyond faster training and improved shapes, GINNs surprised us with an emergent property – a structured latent space. 🧵

[Andy Radler @AndyRadler]

👉 GINNs: A new method to train shape-generative models using design constraints, producing diverse geometries with a unique diversity constraint.

👉GenTO: Applies GINNs to topology optimization, using a solver-in-the-loop to create diverse and structurally compliant shapes.

[Andy Radler @AndyRadler]

I had the privilege of presenting my latest work at the renowned CRUNCH seminar at Brown University.
👉 Geometry-informed Neural Networks (GINNs)
👉 Generative Topology Optimization (GenTO)

Video: https://t.co/EoO0QC2r5j

With
@e_volkmann @artuursberzins @jo_brandstetter

[Andy Radler @AndyRadler]

Geometry-informed Neural Networks: https://t.co/GNsDL64lDn
Generative Topology Optimization: https://t.co/0OQklACUHP

[Johannes Brandstetter @jo_brandstetter]

Super hyped to share NeuralDEM -- the first real-time simulation of industrial particulate flows. NeuralDEM replaces Discrete Element Method (DEM) routines and coupled (CFD-DEM) multiphysics simulations. 🧵

📜: https://t.co/JH4PDpth5g
🖥️: https://t.co/VEsawzd9IV

[Andy Radler @AndyRadler]

Thanks to all the folks visiting our poster on Geometry-informed Neural Networks (GINNs) at the workshop on Geometry-grounded Representation Learning and Generative Modelling.

With @artuursberzins @SebSanokowski @jo_brandstetter @HochreiterSepp

#ICML2024 #GenerativeAI

[Andy Radler @AndyRadler]

Thanks for the fantastic collaboration @artuursberzins @SebSanokowski @jo_brandstetter @HochreiterSepp

[Johannes Brandstetter @jo_brandstetter]

We introduce Geometry-Informed Neural Networks to train shape generative models
without any data (!!), combining learning under constraints, neural fields as a suitable representation, and generating diverse solutions to under-determined problems:
🖥️: https://t.co/qRbJ9SXuc0

[Johannes Brandstetter @jo_brandstetter]

We introduce Geometry-Informed Neural Networks to train shape generative models
without any data (!!), combining learning under constraints, neural fields as a suitable representation, and generating diverse solutions to under-determined problems:
🖥️: https://t.co/qRbJ9SXuc0

[AK @_akhaliq]

SymbolicAI

A framework for logic-based approaches combining generative models and solvers

paper page: https://t.co/76vBvlnmhi

introduce SymbolicAI, a versatile and modular framework employing a logic-based approach to concept learning and flow management in generative processes. SymbolicAI enables the seamless integration of generative models with a diverse range of solvers by treating large language models (LLMs) as semantic parsers that execute tasks based on both natural and formal language instructions, thus bridging the gap between symbolic reasoning and generative AI. We leverage probabilistic programming principles to tackle complex tasks, and utilize differentiable and classical programming paradigms with their respective strengths. The framework introduces a set of polymorphic, compositional, and self-referential operations for data stream manipulation, aligning LLM outputs with user objectives. As a result, we can transition between the capabilities of various foundation models endowed with zero- and few-shot learning capabilities and specialized, fine-tuned models or solvers proficient in addressing specific problems. In turn, the framework facilitates the creation and evaluation of explainable computational graphs. We conclude by introducing a quality measure and its empirical score for evaluating these computational graphs, and propose a benchmark that compares various state-of-the-art LLMs across a set of complex workflows. We refer to the empirical score as the "Vector Embedding for Relational Trajectory Evaluation through Cross-similarity", or VERTEX score for short. The framework codebase and benchmark are linked below.

[Johannes Brandstetter @jo_brandstetter]

Personal update: last month, I re-joined the group of my mentor @HochreiterSepp and my amazing colleague @gklambauer in Linz, opening my own group "AI for data-driven simulations". We all share the vision to create a large-scale AI ecosystem in Linz. Big news to come soon 🚀

[Adam Grant @AdamMGrant]

One of the clearest signs of learning is rethinking your assumptions and revising your opinions.

21 things I rethought in 2021: a thread...

[Sepp Hochreiter @HochreiterSepp]

In our recent work, we address the problem of parameter choice in unsupervised domain adaptation by aggregation. Paper (selected for oral presentation at #ICLR2023): https://t.co/A0x6A1X2mU [1/2]

[AK @_akhaliq]

A Dataset Perspective on Offline Reinforcement Learning
abs: https://t.co/qQZ4SwAA1I
github: https://t.co/UZtgYMCSld

[Sepp Hochreiter @HochreiterSepp]

ArXiv https://t.co/Zmh1ywCUHD: New simple data-augmentation for Vision Transformers (ViTs): Grayscale, Solarization, Gaussian Blur. Suggests simple random crops. Outperforms by a large margin previous fully supervised training recipes for ViTs.