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07/03/2023
SCITUNE: Aligning Large Language Models with Scientific Multimodal Instructions
Instruction finetuning is a popular paradigm to align large language mod...
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05/22/2023
ColMix – A Simple Data Augmentation Framework to Improve Object Detector Performance and Robustness in Aerial Images
In the last decade, Convolutional Neural Network (CNN) and transformer b...
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03/24/2023
How many dimensions are required to find an adversarial example?
Past work exploring adversarial vulnerability have focused on situations...
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03/10/2023
Fast computation of permutation equivariant layers with the partition algebra
Linear neural network layers that are either equivariant or invariant to...
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02/28/2023
Robustness of edited neural networks
Successful deployment in uncertain, real-world environments requires tha...
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02/16/2023
Exploring the Representation Manifolds of Stable Diffusion Through the Lens of Intrinsic Dimension
Prompting has become an important mechanism by which users can more effe...
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11/19/2022
Neural frames: A Tool for Studying the Tangent Bundles Underlying Image Datasets and How Deep Learning Models Process Them
The assumption that many forms of high-dimensional data, such as images,...
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11/14/2022
Do Neural Networks Trained with Topological Features Learn Different Internal Representations?
There is a growing body of work that leverages features extracted via to...
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10/07/2022
In What Ways Are Deep Neural Networks Invariant and How Should We Measure This?
It is often said that a deep learning model is "invariant" to some speci...
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10/03/2022
Convolutional networks inherit frequency sensitivity from image statistics
It is widely acknowledged that trained convolutional neural networks (CN...
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05/27/2022
On the Symmetries of Deep Learning Models and their Internal Representations
Symmetry has been a fundamental tool in the exploration of a broad range...
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04/01/2022
TopTemp: Parsing Precipitate Structure from Temper Topology
Technological advances are in part enabled by the development of novel m...
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03/15/2022
Fiber Bundle Morphisms as a Framework for Modeling Many-to-Many Maps
While it is not generally reflected in the `nice' datasets used for benc...
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12/17/2021
DNA: Dynamic Network Augmentation
In many classification problems, we want a classifier that is robust to ...
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12/03/2021
Differential Property Prediction: A Machine Learning Approach to Experimental Design in Advanced Manufacturing
Advanced manufacturing techniques have enabled the production of materia...
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10/14/2021
Brittle interpretations: The Vulnerability of TCAV and Other Concept-based Explainability Tools to Adversarial Attack
Methods for model explainability have become increasingly critical for t...
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10/13/2021
Bundle Networks: Fiber Bundles, Local Trivializations, and a Generative Approach to Exploring Many-to-one Maps
Many-to-one maps are ubiquitous in machine learning, from the image reco...
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07/09/2021
A Topological-Framework to Improve Analysis of Machine Learning Model Performance
As both machine learning models and the datasets on which they are evalu...
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06/07/2021
Rotating spiders and reflecting dogs: a class conditional approach to learning data augmentation distributions
Building invariance to non-meaningful transformations is essential to bu...
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06/02/2021
One Representation to Rule Them All: Identifying Out-of-Support Examples in Few-shot Learning with Generic Representations
The field of few-shot learning has made remarkable strides in developing...
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05/21/2021
Sheaves as a Framework for Understanding and Interpreting Model Fit
As data grows in size and complexity, finding frameworks which aid in in...
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04/08/2021
Prototypical Region Proposal Networks for Few-Shot Localization and Classification
Recently proposed few-shot image classification methods have generally f...
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09/23/2020
Fuzzy Simplicial Networks: A Topology-Inspired Model to Improve Task Generalization in Few-shot Learning
Deep learning has shown great success in settings with massive amounts o...
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06/27/2019
More chemical detection through less sampling: amplifying chemical signals in hyperspectral data cubes through compressive sensing
Compressive sensing (CS) is a method of sampling which permits some clas...
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06/20/2019
A data-driven approach to sampling matrix selection for compressive sensing
Sampling is a fundamental aspect of any implementation of compressive se...
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01/29/2019
Rare geometries: revealing rare categories via dimension-driven statistics
In many situations, the classes of data points of primary interest also ...
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12/08/2018
Letting symmetry guide visualization: multidimensional scaling on groups
Multidimensional scaling (MDS) is a fundamental tool for both data visua...
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10/27/2018
Monitoring the shape of weather, soundscapes, and dynamical systems: a new statistic for dimension-driven data analysis on large data sets
Dimensionality-reduction methods are a fundamental tool in the analysis ...
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08/05/2018
Too many secants: a hierarchical approach to secant-based dimensionality reduction on large data sets
A fundamental question in many data analysis settings is the problem of ...
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07/10/2018
A GPU-Oriented Algorithm Design for Secant-Based Dimensionality Reduction
Dimensionality-reduction techniques are a fundamental tool for extractin...
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07/03/2018
