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Found 27 papers, 9 papers with code
Adversarial-Robust Transfer Learning for Medical Imaging via Domain Assimilation
In the field of Medical Imaging, extensive research has been dedicated to leveraging its potential in uncovering critical diagnostic features in patients.
Physics-informed Deep Learning to Solve Three-dimensional Terzaghi Consolidation Equation: Forward and Inverse Problems
Meanwhile, the loss functions for different cases are introduced, and their differences in three-dimensional consolidation problems are highlighted.
A dynamic interactive learning framework for automated 3D medical image segmentation
Many deep learning based automated medical image segmentation systems, in reality, face difficulties in deployment due to the cost of massive data annotation and high latency in model iteration.
Reason out Your Layout: Evoking the Layout Master from Large Language Models for Text-to-Image Synthesis
Recent advancements in text-to-image (T2I) generative models have shown remarkable capabilities in producing diverse and imaginative visuals based on text prompts.
EDGE++: Improved Training and Sampling of EDGE
Recently developed deep neural models like NetGAN, CELL, and Variational Graph Autoencoders have made progress but face limitations in replicating key graph statistics on generating large graphs.
Generative quantum machine learning via denoising diffusion probabilistic models
Inspired by the classical counterpart, we propose the quantum denoising diffusion probabilistic model (QuDDPM) to enable efficiently trainable generative learning of quantum data.
On Separate Normalization in Self-supervised Transformers
Self-supervised training methods for transformers have demonstrated remarkable performance across various domains.
Catching Elusive Depression via Facial Micro-Expression Recognition
Depression is a common mental health disorder that can cause consequential symptoms with continuously depressed mood that leads to emotional distress.
Micro Expression Recognition
Micro-Expression Recognition
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Understanding the Efficacy of U-Net & Vision Transformer for Groundwater Numerical Modelling
This paper presents a comprehensive comparison of various machine learning models, namely U-Net, U-Net integrated with Vision Transformers (ViT), and Fourier Neural Operator (FNO), for time-dependent forward modelling in groundwater systems.
Statistically Optimal K-means Clustering via Nonnegative Low-rank Semidefinite Programming
In contrast, nonnegative matrix factorization (NMF) is a simple clustering algorithm widely used by machine learning practitioners, but it lacks a solid statistical underpinning and theoretical guarantees.
Graph-Based Model-Agnostic Data Subsampling for Recommendation Systems
Most subsampling methods are model-based and often require a pre-trained pilot model to measure data importance via e. g. sample hardness.
Efficient and Degree-Guided Graph Generation via Discrete Diffusion Modeling
The empirical study shows that EDGE is much more efficient than competing methods and can generate large graphs with thousands of nodes.
Interpretable Node Representation with Attribute Decoding
We further propose a new learning model, interpretable NOde Representation with Attribute Decoding (NORAD).
NVDiff: Graph Generation through the Diffusion of Node Vectors
Learning to generate graphs is challenging as a graph is a set of pairwise connected, unordered nodes encoding complex combinatorial structures.
Likelihood Adjusted Semidefinite Programs for Clustering Heterogeneous Data
Clustering is a widely deployed unsupervised learning tool.
GeONet: a neural operator for learning the Wasserstein geodesic
Optimal transport (OT) offers a versatile framework to compare complex data distributions in a geometrically meaningful way.
Wasserstein $K$-means for clustering probability distributions
Clustering is an important exploratory data analysis technique to group objects based on their similarity.
Attention U-Net as a surrogate model for groundwater prediction
This manuscript presents an Attention U-Net model that attempts to capture the fundamental input-output relations of the groundwater system and generates solutions of hydraulic head in the whole domain given a set of physical parameters and boundary conditions.
Sketch-and-Lift: Scalable Subsampled Semidefinite Program for $K$-means Clustering
Semidefinite programming (SDP) is a powerful tool for tackling a wide range of computationally hard problems such as clustering.
A Framework for Multisensory Foresight for Embodied Agents
The framework was tested and validated with a dataset containing 4 sensory modalities (vision, haptic, audio, and tactile) on a humanoid robot performing 9 behaviors multiple times on a large set of objects.
Order Matters: Probabilistic Modeling of Node Sequence for Graph Generation
A graph generative model defines a distribution over graphs.
GAN Ensemble for Anomaly Detection
Motivated by the observation that GAN ensembles often outperform single GANs in generation tasks, we propose to construct GAN ensembles for anomaly detection.
Scenario analysis of non-pharmaceutical interventions on global COVID-19 transmissions
This paper introduces a dynamic panel SIR (DP-SIR) model to investigate the impact of non-pharmaceutical interventions (NPIs) on the COVID-19 transmission dynamics with panel data from 9 countries across the globe.
Cutoff for exact recovery of Gaussian mixture models
We determine the information-theoretic cutoff value on separation of cluster centers for exact recovery of cluster labels in a $K$-component Gaussian mixture model with equal cluster sizes.
An End-to-End Block Autoencoder For Physical Layer Based On Neural Networks
Deep Learning has been widely applied in the area of image processing and natural language processing.
Information Theory Signal Processing Information Theory
Diffusion $K$-means clustering on manifolds: provable exact recovery via semidefinite relaxations
We show that exact recovery of the localized diffusion $K$-means is fully adaptive to the local probability density and geometric structures of the underlying submanifolds.
Sparse transition matrix estimation for high-dimensional and locally stationary vector autoregressive models
Under the sparsity assumption on the transition matrix, we establish the rate of convergence of the proposed estimator and show that the convergence rate depends on the smoothness of the locally stationary VAR processes only through the smoothness of the transition matrix function.
Statistics Theory Applications Statistics Theory
