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Found 17 papers, 4 papers with code
STARS: Sensor-agnostic Transformer Architecture for Remote Sensing
We present a sensor-agnostic spectral transformer as the basis for spectral foundation models.
ICML Topological Deep Learning Challenge 2024: Beyond the Graph Domain
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 Learning and Generative Modeling (GRaM).
Generalist Multimodal AI: A Review of Architectures, Challenges and Opportunities
Multimodal models are expected to be a critical component to future advances in artificial intelligence.
Data-Driven Invertible Neural Surrogates of Atmospheric Transmission
We present a framework for inferring an atmospheric transmission profile from a spectral scene.
Haldane Bundles: A Dataset for Learning to Predict the Chern Number of Line Bundles on the Torus
Characteristic classes, which are abstract topological invariants associated with vector bundles, have become an important notion in modern physics with surprising real-world consequences.
ICML 2023 Topological Deep Learning Challenge : Design and Results
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.
Parameters, Properties, and Process: Conditional Neural Generation of Realistic SEM Imagery Towards ML-assisted Advanced Manufacturing
By characterizing microstructure from a topological perspective we are able to evaluate our models' ability to capture the breadth and diversity of experimental scanning electron microscope (SEM) samples.
Internal Representations of Vision Models Through the Lens of Frames on Data Manifolds
While the last five years have seen considerable progress in understanding the internal representations of deep learning models, many questions remain.
Do Neural Networks Trained with Topological Features Learn Different Internal Representations?
While this field, sometimes known as topological machine learning (TML), has seen some notable successes, an understanding of how the process of learning from topological features differs from the process of learning from raw data is still limited.
On the Symmetries of Deep Learning Models and their Internal Representations
In this paper we seek to connect the symmetries arising from the architecture of a family of models with the symmetries of that family's internal representation of data.
TopTemp: Parsing Precipitate Structure from Temper Topology
Technological advances are in part enabled by the development of novel manufacturing processes that give rise to new materials or material property improvements.
Fiber Bundle Morphisms as a Framework for Modeling Many-to-Many Maps
That is, a single input can potentially yield many different outputs (whether due to noise, imperfect measurement, or intrinsic stochasticity in the process) and many different inputs can yield the same output (that is, the map is not injective).
Differential Property Prediction: A Machine Learning Approach to Experimental Design in Advanced Manufacturing
We show that by focusing on the experimenter's need to choose between multiple candidate experimental parameters, we can reframe the challenging regression task of predicting material properties from processing parameters, into a classification task on which machine learning models can achieve good performance.
A Topological Approach for Motion Track Discrimination
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 to differentiate it from dynamic confusers present in the scene.
Argumentative Topology: Finding Loop(holes) in Logic
Advances in natural language processing have resulted in increased capabilities with respect to multiple tasks.
Topological Data Analysis of Task-Based fMRI Data from Experiments on Schizophrenia
With these tools, which allow one to characterize topological invariants such as loops in high-dimensional data, we are able to gain understanding into low-dimensional structures in networks in a way that complements traditional approaches that are based on pairwise interactions.
Persistence Images: A Stable Vector Representation of Persistent Homology
We convert a PD to a finite-dimensional vector representation which we call a persistence image (PI), and prove the stability of this transformation with respect to small perturbations in the inputs.
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