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Found 43 papers, 14 papers with code
Studying the Impact of Latent Representations in Implicit Neural Networks for Scientific Continuous Field Reconstruction
Learning a continuous and reliable representation of physical fields from sparse sampling is challenging and it affects diverse scientific disciplines.
Extracting Protein-Protein Interactions (PPIs) from Biomedical Literature using Attention-based Relational Context Information
On the other hand, machine learning methods to automate PPI knowledge extraction from the scientific literature have been limited by a shortage of appropriate annotated data.
An Evaluation of Real-time Adaptive Sampling Change Point Detection Algorithm using KCUSUM
Detecting abrupt changes in real-time data streams from scientific simulations presents a challenging task, demanding the deployment of accurate and efficient algorithms.
Continuous Field Reconstruction from Sparse Observations with Implicit Neural Networks
Reliably reconstructing physical fields from sparse sensor data is a challenge that frequently arises in many scientific domains.
Quantum Privacy Aggregation of Teacher Ensembles (QPATE) for Privacy-preserving Quantum Machine Learning
The utility of machine learning has rapidly expanded in the last two decades and presents an ethical challenge.
Federated Quantum Long Short-term Memory (FedQLSTM)
The proposed federated QLSTM (FedQLSTM) framework is exploited for performing the task of function approximation.
AesFA: An Aesthetic Feature-Aware Arbitrary Neural Style Transfer
The primary idea is to decompose the image via its frequencies to better disentangle aesthetic styles from the reference image while training the entire model in an end-to-end manner to exclude pre-trained models at inference completely.
Fast 2D Bicephalous Convolutional Autoencoder for Compressing 3D Time Projection Chamber Data
Developing real-time data compression algorithms to reduce such data at high throughput to fit permanent storage has drawn increasing attention.
Quantum Federated Learning With Quantum Networks
A major concern of deep learning models is the large amount of data that is required to build and train them, much of which is reliant on sensitive and personally identifiable information that is vulnerable to access by third parties.
Federated Quantum Machine Learning with Differential Privacy
The preservation of privacy is a critical concern in the implementation of artificial intelligence on sensitive training data.
DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies
In the upcoming decade, deep learning may revolutionize the natural sciences, enhancing our capacity to model and predict natural occurrences.
Exploring Robust Features for Improving Adversarial Robustness
In this paper, we strive to explore the robust features which are not affected by the adversarial perturbations, i. e., invariant to the clean image and its adversarial examples, to improve the model's adversarial robustness.
INSURE: An Information Theory Inspired Disentanglement and Purification Model for Domain Generalization
Specifically, we first propose an information theory inspired loss function to ensure the disentangled class-relevant features contain sufficient class label information and the other disentangled auxiliary feature has sufficient domain information.
An extensible point-based method for data chart value detection
We present an extensible method for identifying semantic points to reverse engineer (i. e. extract the values of) data charts, particularly those in scientific articles.
Transferable Graph Neural Fingerprint Models for Quick Response to Future Bio-Threats
With this dataset, we trained graph neural fingerprint docking models for high-throughput virtual COVID-19 drug screening.
Comparative Performance Evaluation of Large Language Models for Extracting Molecular Interactions and Pathway Knowledge
Understanding protein interactions and pathway knowledge is crucial for unraveling the complexities of living systems and investigating the underlying mechanisms of biological functions and complex diseases.
SwiFT: Swin 4D fMRI Transformer
To address this challenge, we present SwiFT (Swin 4D fMRI Transformer), a Swin Transformer architecture that can learn brain dynamics directly from fMRI volumes in a memory and computation-efficient manner.
Optimal sensor placement for reconstructing wind pressure field around buildings using compressed sensing
Deciding how to optimally deploy sensors in a large, complex, and spatially extended structure is critical to ensure that the surface pressure field is accurately captured for subsequent analysis and design.
UVCGAN v2: An Improved Cycle-Consistent GAN for Unpaired Image-to-Image Translation
An unpaired image-to-image (I2I) translation technique seeks to find a mapping between two domains of data in a fully unsupervised manner.
A Neural PDE Solver with Temporal Stencil Modeling
Numerical simulation of non-linear partial differential equations plays a crucial role in modeling physical science and engineering phenomena, such as weather, climate, and aerodynamics.
Comprehensive analysis of gene expression profiles to radiation exposure reveals molecular signatures of low-dose radiation response
There are various sources of ionizing radiation exposure, where medical exposure for radiation therapy or diagnosis is the most common human-made source.
UVCGAN: UNet Vision Transformer cycle-consistent GAN for unpaired image-to-image translation
Unpaired image-to-image translation has broad applications in art, design, and scientific simulations.
A Bayesian Deep Learning Approach to Near-Term Climate Prediction
Since model bias and associated initialization shock are serious shortcomings that reduce prediction skills in state-of-the-art decadal climate prediction efforts, we pursue a complementary machine-learning-based approach to climate prediction.
A Machine Learning-based Characterization Framework for Parametric Representation of Nonlinear Sloshing
The growing interest in creating a parametric representation of liquid sloshing inside a container stems from its practical applications in modern engineering systems.
Efficient Data Compression for 3D Sparse TPC via Bicephalous Convolutional Autoencoder
This method shows advantages both in compression fidelity and ratio compared to traditional data compression methods, such as MGARD, SZ, and ZFP.
Sparse Attention with Learning to Hash
To overcome these issues, this paper proposes a new strategy for sparse attention, namely LHA (Learning-to-Hash Attention), which directly learns separate parameterized hash functions for queries and keys, respectively.
Feature Importance in a Deep Learning Climate Emulator
From the perspective of climate dynamics, these findings suggest a dominant role for local processes and a negligible role for remote teleconnections at the spatial and temporal scales we consider.
AGKD-BML: Defense Against Adversarial Attack by Attention Guided Knowledge Distillation and Bi-directional Metric Learning
The attention knowledge is obtained from a weight-fixed model trained on a clean dataset, referred to as a teacher model, and transferred to a model that is under training on adversarial examples (AEs), referred to as a student model.
Stochastic Projective Splitting: Solving Saddle-Point Problems with Multiple Regularizers
We present a new, stochastic variant of the projective splitting (PS) family of algorithms for monotone inclusion problems.
Federated Quantum Machine Learning
Distributed training across several quantum computers could significantly improve the training time and if we could share the learned model, not the data, it could potentially improve the data privacy as the training would happen where the data is located.
Quantum machine learning with differential privacy
In this study, we develop a hybrid quantum-classical model that is trained to preserve privacy using differentially private optimization algorithm.
Three-dimensional Coherent X-ray Diffraction Imaging via Deep Convolutional Neural Networks
The trained ML model can rapidly provide an immediate result with high accuracy which could benefit real-time experiments, and the predicted result can be further refined with transfer learning.
Hybrid Quantum-Classical Graph Convolutional Network
This research provides a hybrid quantum-classical graph convolutional network (QGCNN) for learning HEP data.
Quantum Convolutional Neural Networks for High Energy Physics Data Analysis
This work presents a quantum convolutional neural network (QCNN) for the classification of high energy physics events.
Application of Quantum Machine Learning using the Quantum Variational Classifier Method to High Energy Physics Analysis at the LHC on IBM Quantum Computer Simulator and Hardware with 10 qubits
On the quantum hardware, the quantum variational classifier method has shown promising discrimination power, comparable to that on the quantum simulator.
Quantum Physics High Energy Physics - Experiment
Quantum Long Short-Term Memory
Long short-term memory (LSTM) is a kind of recurrent neural networks (RNN) for sequence and temporal dependency data modeling and its effectiveness has been extensively established.
Bounding the expected run-time of nonconvex optimization with early stopping
This work examines the convergence of stochastic gradient-based optimization algorithms that use early stopping based on a validation function.
Visual Detection with Context for Document Layout Analysis
Due to the limited availability of high-quality region-labels for scientific articles, we also contribute a novel dataset of region annotations, the first version of which covers 9 region classes and 822 article pages.
On the expected running time of nonconvex optimization with early stopping
This work examines the convergence of stochastic gradient algorithms that use early stopping based on a validation function, wherein optimization ends when the magnitude of a validation function gradient drops below a threshold.
Failure Analysis on Multivariate Time-series Data given Uncertain Labels
Machine failure analysis and detection is critical to today’s industrial society.
Layered SGD: A Decentralized and Synchronous SGD Algorithm for Scalable Deep Neural Network Training
The efficiency of the algorithm is tested by training a deep network on the ImageNet classification task.
Performance Analysis of Deep Learning Workloads on Leading-edge Systems
This work examines the performance of leading-edge systems designed for machine learning computing, including the NVIDIA DGX-2, Amazon Web Services (AWS) P3, IBM Power System Accelerated Compute Server AC922, and a consumer-grade Exxact TensorEX TS4 GPU server.
Re-examination of the Role of Latent Variables in Sequence Modeling
With latent variables, stochastic recurrent models have achieved state-of-the-art performance in modeling sound-wave sequence.
