56 citations · 308 across the 70 of their papers we have counts for
27 papers · 1 filter
RNADyn: A Benchmark for Generating and Understanding RNA Dynamics
Yiming Huang, Lennart Bastian, Hanqun Cao +2
Ribonucleic acid (RNA) functions through conformational changes that are not fully captured by static structures. However, large-scale standardized RNA dynamics data remain limited…
Higher-Order Molecular Grammars for Generative and Foundation Models in Chemistry
Yiming Huang, Yujie Zeng, Vijay Prakash Dwivedi +4
Molecular learning models are strongly shaped by their underlying representations. Yet standard sequential and graph formalisms struggle to explicitly encode higher-order topology,…
Collapsed Effective Operators for Higher-order Structures
Maximilian Krahn, Lennart Bastian, Vikas Garg +2
Higher-order structures are powerful relational modeling tools, yet existing spectral operators decompose the topology into separate ranks, leaving practitioners to fuse the inform…
Topological Neural Operators
Lennart Bastian, Samuel Leventhal, Mustafa Hajij +1
We introduce Topological Neural Operators (TNOs), a principled framework for operator learning on cell complexes that lifts neural operators (NOs) from functions on points and/or e…
Variational Inference for Lévy Process-Driven SDEs via Neural Tilting
Yaman Kindap, Manfred Opper, Benjamin Dupuis +2
Modelling extreme events and heavy-tailed phenomena is central to building reliable predictive systems in domains such as finance, climate science, and safety-critical AI. While Lé…
Generalization at the Edge of Stability
Mario Tuci, Caner Korkmaz, Umut Şimşekli +1
Training modern neural networks often relies on large learning rates, operating at the edge of stability, where the optimization dynamics exhibit oscillatory and chaotic behavior.…
From Data Statistics to Feature Geometry: How Correlations Shape Superposition
Lucas Prieto, Edward Stevinson, Melih Barsbey +2
A central idea in mechanistic interpretability is that neural networks represent more features than they have dimensions, arranging them in superposition to form an over-complete b…
Fractional Diffusion Bridge Models
Gabriel Nobis, Maximilian Springenberg, Arina Belova +5
We present Fractional Diffusion Bridge Models (FDBM), a novel generative diffusion bridge framework driven by an approximation of the rich and non-Markovian fractional Brownian mot…
Adversarial Attacks Leverage Interference Between Features in Superposition
Edward Stevinson, Lucas Prieto, Melih Barsbey +1
Why do adversarial examples exist, and why do they transfer between models? Existing explanations appeal to high-dimensional geometry, non-robust patterns in the input, and decisio…
Large Learning Rates Simultaneously Achieve Robustness to Spurious Correlations and Compressibility
Melih Barsbey, Lucas Prieto, Stefanos Zafeiriou +1
Robustness and resource-efficiency are two highly desirable properties for modern machine learning models. However, achieving them jointly remains a challenge. In this paper, we id…
On the Interaction of Compressibility and Adversarial Robustness
Melih Barsbey, Antônio H. Ribeiro, Umut Şimşekli +1
As demands for resource efficiency and safety in modern neural networks intensify, substantial research effort has gone into model compression and adversarial robustness. Yet despi…
Stability, Complexity and Data-Dependent Worst-Case Generalization Bounds
Mario Tuci, Lennart Bastian, Benjamin Dupuis +3
Providing generalization guarantees for stochastic optimization algorithms remains a key challenge in learning theory. Recently, numerous works demonstrated the impact of the geome…
Efficient Training of Neural SDEs Using Stochastic Optimal Control
Rembert Daems, Manfred Opper, Guillaume Crevecoeur +1
We present a hierarchical, control theory inspired method for variational inference (VI) for neural stochastic differential equations (SDEs). While VI for neural SDEs is a promisin…
Copresheaf Topological Neural Networks: A Generalized Deep Learning Framework
Mustafa Hajij, Lennart Bastian, Sarah Osentoski +9
We introduce copresheaf topological neural networks (CTNNs), a powerful unifying framework that encapsulates a wide spectrum of deep learning architectures, designed to operate on…
HOG-Diff: Higher-Order Guided Diffusion for Graph Generation
Yiming Huang, Tolga Birdal
Graph generation is a critical yet challenging task, as empirical analyses require a deep understanding of complex, non-Euclidean structures. Diffusion models have recently made si…
Grokking at the Edge of Numerical Stability
Lucas Prieto, Melih Barsbey, Pedro A. M. Mediano +1
Grokking, the sudden generalization that occurs after prolonged overfitting, is a surprising phenomenon challenging our understanding of deep learning. Although significant progres…
Convex Formulations for Training Two-Layer ReLU Neural Networks
Karthik Prakhya, Tolga Birdal, Alp Yurtsever
Solving non-convex, NP-hard optimization problems is crucial for training machine learning models, including neural networks. However, non-convexity often leads to black-box machin…
Topological Generalization Bounds for Discrete-Time Stochastic Optimization Algorithms
Rayna Andreeva, Benjamin Dupuis, Rik Sarkar +2
We present a novel set of rigorous and computationally efficient topology-based complexity notions that exhibit a strong correlation with the generalization gap in modern deep neur…
Attending to Topological Spaces: The Cellular Transformer
Rubén Ballester, Pablo Hernández-García, Mathilde Papillon +6
Topological Deep Learning seeks to enhance the predictive performance of neural network models by harnessing topological structures in input data. Topological neural networks opera…
Position: Topological Deep Learning is the New Frontier for Relational Learning
Theodore Papamarkou, Tolga Birdal, Michael Bronstein +19
Topological deep learning (TDL) is a rapidly evolving field that uses topological features to understand and design deep learning models. This paper posits that TDL is the new fron…
TopoX: A Suite of Python Packages for Machine Learning on Topological Domains
Mustafa Hajij, Mathilde Papillon, Florian Frantzen +40
We introduce TopoX, a Python software suite that provides reliable and user-friendly building blocks for computing and machine learning on topological domains that extend graphs: h…
Variational Inference for SDEs Driven by Fractional Noise
Rembert Daems, Manfred Opper, Guillaume Crevecoeur +1
We present a novel variational framework for performing inference in (neural) stochastic differential equations (SDEs) driven by Markov-approximate fractional Brownian motion (fBM)…
Probabilistic Sampling of Balanced K-Means using Adiabatic Quantum Computing
Jan-Nico Zaech, Martin Danelljan, Tolga Birdal +1
Adiabatic quantum computing (AQC) is a promising approach for discrete and often NP-hard optimization problems. Current AQCs allow to implement problems of research interest, which…
ICML 2023 Topological Deep Learning Challenge : Design and Results
Mathilde Papillon, Mustafa Hajij, Helen Jenne +53
This paper presents the computational challenge on topological deep learning that was hosted within the ICML 2023 Workshop on Topology and Geometry in Machine Learning. The competi…