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Found 22 papers, 7 papers with code
In-Context Example Ordering Guided by Label Distributions
Inspired by the idea of learning from label proportions, we propose two principles for in-context example ordering guided by model's probability predictions.
Position Paper: Challenges and Opportunities in Topological Deep Learning
Topological deep learning (TDL) is a rapidly evolving field that uses topological features to understand and design deep learning models.
TROPHY: A Topologically Robust Physics-Informed Tracking Framework for Tropical Cyclones
In comparison with the observed tracks, we demonstrate that TROPHY can capture TC characteristics that are comparable to and sometimes even better than a well-validated TC tracking algorithm that requires multiple dynamic and thermodynamic scalar fields.
Interpreting and generalizing deep learning in physics-based problems with functional linear models
In this work, motivated by the field of functional data analysis (FDA), we propose generalized functional linear models as an interpretable surrogate for a trained deep learning model.
Contrastive Learning for Sleep Staging based on Inter Subject Correlation
In recent years, multitudes of researches have applied deep learning to automatic sleep stage classification.
Automatic Sleep Stage Classification
Contrastive Learning
+1
Experimental Observations of the Topology of Convolutional Neural Network Activations
Topological data analysis (TDA) is a branch of computational mathematics, bridging algebraic topology and data science, that provides compact, noise-robust representations of complex structures.
Multilevel Robustness for 2D Vector Field Feature Tracking, Selection, and Comparison
However, in practice, the computation of classic robustness may produce artifacts when a critical point is close to the boundary of the domain; thus, we do not have a complete picture of the vector field behavior within its local neighborhood.
The SVD of Convolutional Weights: A CNN Interpretability Framework
Fully connected layers can be studied by decomposing their weight matrices using a singular value decomposition, in effect studying the correlations between the rows in each matrix to discover the dynamics of the map.
Multi-Spatio-temporal Fusion Graph Recurrent Network for Traffic forecasting
The network proposes a data-driven weighted adjacency matrix generation method to compensate for real-time spatial dependencies not reflected by the predefined adjacency matrix.
STCGAT: A Spatio-temporal Causal Graph Attention Network for traffic flow prediction in Intelligent Transportation Systems
Existing methods are usually based on graph neural networks using predefined spatial adjacency graphs of traffic networks to model spatial dependencies, ignoring the dynamic correlation of relationships between road nodes.
VERB: Visualizing and Interpreting Bias Mitigation Techniques for Word Representations
To aid this, we present Visualization of Embedding Representations for deBiasing system ("VERB"), an open-source web-based visualization tool that helps the users gain a technical understanding and visual intuition of the inner workings of debiasing techniques, with a focus on their geometric properties.
Plot2API: Recommending Graphic API from Plot via Semantic Parsing Guided Neural Network
Considering the lack of technologies in Plot2API, we present a novel deep multi-task learning approach named Semantic Parsing Guided Neural Network (SPGNN) which translates the Plot2API issue as a multi-label image classification and an image semantic parsing tasks for the solution.
Two Problems about Monomial Bent Functions
They also proposed an open problem regarding monomial function with maximal number of bent components.
Information Theory Information Theory
Parallelizing the Unpacking and Clustering of Detector Data for Reconstruction of Charged Particle Tracks on Multi-core CPUs and Many-core GPUs
We present results from parallelizing the unpacking and clustering steps of the raw data from the silicon strip modules for reconstruction of charged particle tracks.
High Energy Physics - Experiment Distributed, Parallel, and Cluster Computing
Sketching Merge Trees for Scientific Data Visualization
That is, given a large set T of merge trees, we would like to find a much smaller basis set S such that each tree in T can be approximately reconstructed from a linear combination of merge trees in S. A set of high-dimensional vectors can be sketched via matrix sketching techniques such as principal component analysis and column subset selection.
Interpreting Graph Drawing with Multi-Agent Reinforcement Learning
Applying machine learning techniques to graph drawing has become an emergent area of research in visualization.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
FIS-Nets: Full-image Supervised Networks for Monocular Depth Estimation
This paper addresses the importance of full-image supervision for monocular depth estimation.
TopoAct: Visually Exploring the Shape of Activations in Deep Learning
To understand how such performance is achieved, we probe a trained deep neural network by studying neuron activations, i. e., combinations of neuron firings, at various layers of the network in response to a particular input.
Voxel-FPN: multi-scale voxel feature aggregation in 3D object detection from point clouds
Object detection in point cloud data is one of the key components in computer vision systems, especially for autonomous driving applications.
A Kernel for Multi-Parameter Persistent Homology
Topological data analysis and its main method, persistent homology, provide a toolkit for computing topological information of high-dimensional and noisy data sets.
Homology-Preserving Multi-Scale Graph Skeletonization Using Mapper on Graphs
Node-link diagrams are a popular method for representing graphs that capture relationships between individuals, businesses, proteins, and telecommunication endpoints.
Discrete Stratified Morse Theory: Algorithms and A User's Guide
Inspired by the works of Forman on discrete Morse theory, which is a combinatorial adaptation to cell complexes of classical Morse theory on manifolds, we introduce a discrete analogue of the stratified Morse theory of Goresky and MacPherson.
Computational Geometry Algebraic Topology
