172 citations · 195 across the 19 of their papers we have counts for
20 papers
TAGTorch: A PyTorch Library for Geometry, Topology, and Symmetry-Aware Machine Learning
Brendan Kennedy, Tegan Emerson, Gregory Roek +2
Over the last decade, neural networks have been applied to an increasingly diverse range of applications, including data with rich geometric, topological, or symmetry-related struc…
What do Geometric Hallucination Detection Metrics Actually Measure?
Eric Yeats, John Buckheit, Sarah Scullen +9
Hallucination remains a barrier to deploying generative models in high-consequence applications. This is especially true in cases where external ground truth is not readily availab…
ICML Topological Deep Learning Challenge 2024: Beyond the Graph Domain
Guillermo Bernárdez, Lev Telyatnikov, Marco Montagna +70
This paper describes the 2nd edition of the ICML Topological Deep Learning Challenge that was hosted within the ICML 2024 ELLIS Workshop on Geometry-grounded Representation Learnin…
Generalist Multimodal AI: A Review of Architectures, Challenges and Opportunities
Sai Munikoti, Ian Stewart, Sameera Horawalavithana +4
Multimodal models are expected to be a critical component to future advances in artificial intelligence. This field is starting to grow rapidly with a surge of new design elements…
Haldane Bundles: A Dataset for Learning to Predict the Chern Number of Line Bundles on the Torus
Cody Tipton, Elizabeth Coda, Davis Brown +5
Characteristic classes, which are abstract topological invariants associated with vector bundles, have become an important notion in modern physics with surprising real-world conse…
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…
Reproducing Kernel Hilbert Space Pruning for Sparse Hyperspectral Abundance Prediction
Michael G. Rawson, Timothy Doster
Hyperspectral measurements from long range sensors can give a detailed picture of the items, materials, and chemicals in a scene but analysis can be difficult, slow, and expensive…
Parameters, Properties, and Process: Conditional Neural Generation of Realistic SEM Imagery Towards ML-assisted Advanced Manufacturing
Scott Howland, Lara Kassab, Keerti Kappagantula +2
The research and development cycle of advanced manufacturing processes traditionally requires a large investment of time and resources. Experiments can be expensive and are hence c…
Do Neural Networks Trained with Topological Features Learn Different Internal Representations?
Sarah McGuire, Shane Jackson, Tegan Emerson +1
There is a growing body of work that leverages features extracted via topological data analysis to train machine learning models. While this field, sometimes known as topological m…
Internal Representations of Vision Models Through the Lens of Frames on Data Manifolds
Henry Kvinge, Grayson Jorgenson, Davis Brown +2
While the last five years have seen considerable progress in understanding the internal representations of deep learning models, many questions remain. This is especially true when…
In What Ways Are Deep Neural Networks Invariant and How Should We Measure This?
Henry Kvinge, Tegan H. Emerson, Grayson Jorgenson +3
It is often said that a deep learning model is "invariant" to some specific type of transformation. However, what is meant by this statement strongly depends on the context in whic…
TopTemp: Parsing Precipitate Structure from Temper Topology
Lara Kassab, Scott Howland, Henry Kvinge +2
Technological advances are in part enabled by the development of novel manufacturing processes that give rise to new materials or material property improvements. Development and ev…
On the Symmetries of Deep Learning Models and their Internal Representations
Charles Godfrey, Davis Brown, Tegan Emerson +1
Symmetry is a fundamental tool in the exploration of a broad range of complex systems. In machine learning symmetry has been explored in both models and data. In this paper we seek…
Fiber Bundle Morphisms as a Framework for Modeling Many-to-Many Maps
Elizabeth Coda, Nico Courts, Colby Wight +6
While it is not generally reflected in the `nice' datasets used for benchmarking machine learning algorithms, the real-world is full of processes that would be best described as ma…
Differential Property Prediction: A Machine Learning Approach to Experimental Design in Advanced Manufacturing
Loc Truong, WoongJo Choi, Colby Wight +4
Advanced manufacturing techniques have enabled the production of materials with state-of-the-art properties. In many cases however, the development of physics-based models of these…
A Topological-Framework to Improve Analysis of Machine Learning Model Performance
Henry Kvinge, Colby Wight, Sarah Akers +7
As both machine learning models and the datasets on which they are evaluated have grown in size and complexity, the practice of using a few summary statistics to understand model p…
A Topological Approach for Motion Track Discrimination
Tegan Emerson, Sarah Tymochko, George Stantchev +3
Detecting small targets at range is difficult because there is not enough spatial information present in an image sub-region containing the target to use correlation-based methods…
Argumentative Topology: Finding Loop(holes) in Logic
Sarah Tymochko, Zachary New, Lucius Bynum +4
Advances in natural language processing have resulted in increased capabilities with respect to multiple tasks. One of the possible causes of the observed performance gains is the…
Topological Data Analysis of Task-Based fMRI Data from Experiments on Schizophrenia
Bernadette J. Stolz, Tegan Emerson, Satu Nahkuri +2
We use methods from computational algebraic topology to study functional brain networks, in which nodes represent brain regions and weighted edges encode the similarity of fMRI tim…
Persistence Images: A Stable Vector Representation of Persistent Homology
Henry Adams, Sofya Chepushtanova, Tegan Emerson +7
Many datasets can be viewed as a noisy sampling of an underlying space, and tools from topological data analysis can characterize this structure for the purpose of knowledge discov…