Search Results for author:
Found 36 papers, 6 papers with code
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.
Understanding the Inner Workings of Language Models Through Representation Dissimilarity
As language models are applied to an increasing number of real-world applications, understanding their inner workings has become an important issue in model trust, interpretability, and transparency.
Attributing Learned Concepts in Neural Networks to Training Data
By now there is substantial evidence that deep learning models learn certain human-interpretable features as part of their internal representations of data.
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.
SCITUNE: Aligning Large Language Models with Scientific Multimodal Instructions
Instruction finetuning is a popular paradigm to align large language models (LLM) with human intent.
ColMix -- A Simple Data Augmentation Framework to Improve Object Detector Performance and Robustness in Aerial Images
In this work, we present a novel augmentation method, called collage pasting, for increasing the object density without a need for segmentation masks, thereby improving the detector performance.
How many dimensions are required to find an adversarial example?
Past work exploring adversarial vulnerability have focused on situations where an adversary can perturb all dimensions of model input.
Fast computation of permutation equivariant layers with the partition algebra
The space of permutation equivariant linear layers is a generalization of the partition algebra, an object first discovered in statistical physics with deep connections to the representation theory of the symmetric group, and the basis described above generalizes the so-called orbit basis of the partition algebra.
Edit at your own risk: evaluating the robustness of edited models to distribution shifts
The current trend toward ever-larger models makes standard retraining procedures an ever-more expensive burden.
Exploring the Representation Manifolds of Stable Diffusion Through the Lens of Intrinsic Dimension
We find that choice of prompt has a substantial impact on the intrinsic dimension of representations at both layers of the model which we explored, but that the nature of this impact depends on the layer being considered.
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.
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 specific type of transformation.
Testing predictions of representation cost theory with CNNs
It is widely acknowledged that trained convolutional neural networks (CNNs) have different levels of sensitivity to signals of different frequency.
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.
Making Corgis Important for Honeycomb Classification: Adversarial Attacks on Concept-based Explainability Tools
Methods for model explainability have become increasingly critical for testing the fairness and soundness of deep learning.
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 recognition model that assigns a multitude of distinct images to the concept of "cat" to the time series forecasting model which assigns a range of distinct time-series to a single scalar regression value.
A Topological-Framework to Improve Analysis of Machine Learning Model Performance
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 performance has become increasingly problematic.
Rotating spiders and reflecting dogs: a class conditional approach to learning data augmentation distributions
Building invariance to non-meaningful transformations is essential to building efficient and generalizable machine learning models.
One Representation to Rule Them All: Identifying Out-of-Support Examples in Few-shot Learning with Generic Representations
We describe how this problem is subtly different from out-of-distribution detection and describe a new method of identifying OOS examples within the Prototypical Networks framework using a fixed point which we call the generic representation.
Sheaves as a Framework for Understanding and Interpreting Model Fit
As data grows in size and complexity, finding frameworks which aid in interpretation and analysis has become critical.
Prototypical Region Proposal Networks for Few-Shot Localization and Classification
Recently proposed few-shot image classification methods have generally focused on use cases where the objects to be classified are the central subject of images.
Hypergraph Models of Biological Networks to Identify Genes Critical to Pathogenic Viral Response
Results: We compiled a novel data set of transcriptional host response to pathogenic viral infections and formulated relationships between genes as a hypergraph where hyperedges represent significantly perturbed genes, and vertices represent individual biological samples with specific experimental conditions.
Fuzzy Simplicial Networks: A Topology-Inspired Model to Improve Task Generalization in Few-shot Learning
Few-shot learning algorithms, which seek to address this limitation, are designed to generalize well to new tasks with limited data.
More chemical detection through less sampling: amplifying chemical signals in hyperspectral data cubes through compressive sensing
In this paper we explore a phenomenon in which bandwise CS sampling of a hyperspectral data cube followed by reconstruction can actually result in amplification of chemical signals contained in the cube.
A data-driven approach to sampling matrix selection for compressive sensing
We propose a method for defining an order for a sampling basis that is optimal with respect to capturing variance in data, thus allowing for meaningful sensing at any desired level of compression.
Rare geometries: revealing rare categories via dimension-driven statistics
The first is a general lack of labeled examples of the rare class and the second is the potential non-separability of the rare class from the majority (in terms of available features).
Multi-Dimensional Scaling on Groups
Leveraging the intrinsic symmetries in data for clear and efficient analysis is an important theme in signal processing and other data-driven sciences.
Monitoring the shape of weather, soundscapes, and dynamical systems: a new statistic for dimension-driven data analysis on large data sets
In this paper, we propose a new statistic that we call the $\kappa$-profile for analysis of large data sets.
Too many secants: a hierarchical approach to secant-based dimensionality reduction on large data sets
Intuitively, the SAP algorithm seeks to determine a projection which best preserves the lengths of all secants between points in a data set; by applying the algorithm to find the best projections to vector spaces of various dimensions, one may infer the dimension of the manifold of origination.
A GPU-Oriented Algorithm Design for Secant-Based Dimensionality Reduction
Dimensionality-reduction techniques are a fundamental tool for extracting useful information from high-dimensional data sets.
Endmember Extraction on the Grassmannian
Endmember extraction plays a prominent role in a variety of data analysis problems as endmembers often correspond to data representing the purest or best representative of some feature.
