New algorithm trains living neural networks for machine learning tasks.
problem Training living neural networks for machine learning applications.
method Supervised STDP-based learning algorithm considering neuron engineering constraints.
result 74.7% accuracy on MNIST handwritten digit recognition benchmark.
New neural networks for non-commutative data.
problem No existing neural networks suitable for non-commutative data.
method Developed compact matrix quantum group equivariant neural networks.
result Characterized weight matrices for easy compact matrix quantum groups.
Deep learning model classifies human activities without prior knowledge.
problem Human activity recognition using smart home sensors.
method Long Short Term Memory (LSTM) Recurrent Neural Network applied to three real-world datasets.
result The proposed approach outperforms existing methods in terms of accuracy and performance.
Simplicial neural networks extend graph neural networks to handle higher-order interactions.
problem Handling higher-order interactions in complex data structures.
method Define a convolution operation for simplicial complexes and use it to construct convolutional neural networks.
result SNNs effectively impute missing data in coauthorship complexes.
A new metric measures saturation of neural network layers.
problem Analyzing the quality of latent representations in neural networks.
method Layer Saturation metric based on spectral analysis.
result Saturation is related to generalization and predictive performance.
Deep learning and genetic algorithms speed up cosmological Bayesian inference.
problem Substantial computational demands in Bayesian inference for cosmological parameter estimation.
method Deep learning using feedforward neural networks to approximate likelihood functions dynamically, optimized with genetic algorithms.
result Significant speed-up in Bayesian inference process for cosmological models and datasets.
Detects unusual inputs to neural networks to prevent flawed predictions.
problem Erratic predictions from neural networks on unexpected inputs.
method Evaluates input unusualness by comparing its content to learned parameters.
result Simple, effective method for comparing input metrics across different scales.
Study predicts future hospitalizations to manage COVID-19 patient surge.
problem Managing hospitalization needs during COVID-19 pandemic.
method Used 4 recurrent neural networks to predict hospitalization changes.
result Sequence to sequence model with attention achieved high accuracy and AUC.
Study uses 1D-CNNs to forecast mortality in ELSA survey.
problem Forecasting mortality in the middle-aged and older population.
method 1D-CNNs applied to longitudinal data with various over/undersampling and activation functions.
result Swish nonlinearity outperforms other functions in forecasting mortality.
Trojans can be inserted into deep neural networks without altering training data.
problem Inserting malicious code into deep learning models without changing training data.
method Patches model parameters in memory to achieve desired behavior on specific inputs.
result Efficient patches can induce trojan behavior with minimal risk of detection.
DBMTL optimizes multiple e-commerce targets using Bayesian networks.
problem Multi-target optimization in e-commerce platforms.
method Bayesian modeling of target events in a Bayesian network, with hidden layers parameterized by neural networks.
result Significant improvement in recommendation performance over other methods.
New method compresses LSTM networks using MPS tensor trains.
problem Challenges in maintaining performance of compressed RNNs.
method Use of MPS tensor trains for LSTM network compression.
result MPS tensor trains outperform MPOs in storage and inference time.
New neural networks learn graph symmetries.
problem Learning from graph data without considering vertex relations.
method Constructs equivariant neural networks to Aut(G) group.
result Characterizes learnable, linear, Aut(G)-equivariant functions.
Deep neural network assesses quality of super-resolution images.
problem Evaluating the quality of super-resolution images from experts' scores.
method Deep neural network trained on expert scores to quantify image quality.
result Deep neural network's quality predictions correlate with human expert evaluations.
Multi-StyleGAN simulates live cell microscopy imagery.
problem Costly and complex live cell experiments.
method Generative adversarial network (GAN) synthesizing multi-domain time-lapse images.
result Captures biophysical factors and time dependencies in cell imagery.
Improved neural network predicts tropical storm trajectories and Bayesian intervals.
problem Accurately predicting the trajectories of tropical storms to prevent damage.
method Developed an improved RNN model with dropout to predict Bayesian intervals.
result Neural network dropout values significantly affect prediction accuracy and intervals.
System recommends workouts and predicts success rates using RNNs.
problem Promoting healthy lifestyles through personalized exercise recommendations.
method Two interconnected recurrent neural networks (RNNs) using historical workout data.
result Interconnected-RNN model predicts exercise success rates with improved accuracy.
New approach uses satellite imagery to estimate economic growth.
problem Lack of reliable economic data in developing countries.
method Dynamic network and representation learning.
result Accurately predicts spatial gross economic expenditures.
Paper optimizes urban navigation with deep learning models.
problem Real-time urban pathfinding challenges.
method Enhanced A* algorithm and neural network model.
result Neural network model outperforms traditional methods, reducing travel times by up to 40%.
Method analyzes hyperparameters using HSIC for better neural network performance.
problem Complex hyperparameter spaces in deep learning.
method Goal-oriented sensitivity analysis using HSIC.
result Robust analysis index quantifies hyperparameters' impact.
RNN predicts hurricane paths with high accuracy.
problem Accurate prediction of hurricane trajectories to mitigate disasters.
method Fully connected RNN trained on NHC data.
result RNN predicts hurricane paths up to 120 hours with competitive accuracy.
AI-assisted heart disease diagnosis reduces misdiagnosis and saves lives.
problem Misdiagnosis of heart disease leads to unnecessary deaths.
method Developed an AI application using ML and DNN algorithms on a dataset from the Cleveland Clinic Foundation.
result DNN model achieved a 92% accuracy rate, reducing misdiagnosis.
Improved action recognition in live videos with hybrid FR-DL method.
problem High computational costs and lack of temporal information in conventional action recognition.
method Automated selection of representative frames, feature extraction, background subtraction, HOG, deep neural network, LSTM, Softmax-KNN classifier.
result Significant improvement in accuracy and speed compared to state-of-the-art methods.
Persistent homology reveals a topological signature of grokking in neural networks.
problem Understanding how neural networks learn and generalize from modular arithmetic tasks.
method Persistent homology on point clouds derived from embedding matrices of models trained on modular arithmetic.
result A sharp increase in first homology persistence indicates grokking, with a dominant long-lived topological feature and structured secondary features.
Statistical model for analyzing longitudinal data on Riemannian manifolds.
problem Analyzing longitudinal data on non-Euclidean spaces.
method Developed a recurrent statistical model for ordered data on Riemannian manifolds.
result Efficient algorithm with competitive performance and fewer parameters.
New test uses neural networks to compare distributions, outperforming traditional methods.
problem Comparing distributions in high dimensions and higher orders of smoothness.
method Integral probability metrics with Radon bounded variation functions and neural networks.
result The Radon-Kolmogorov-Smirnov (RKS) test outperforms traditional methods in distinguishing distributions.
Deep neural network predicts product returns before purchase.
problem High costs of handling returned fashion products.
method Bayesian Personalized Ranking (BPR) embeddings and skip-gram model for user and product features.
result Reduced overall returns through real-time return probability prediction.
AquaSight detects water impurity using deep learning.
problem Water pollution and lack of affordable water quality assessment.
method Convolutional Neural Networks for automated water impurity detection.
result Deep learning model achieved 96% accuracy in detecting water contamination.
System suggests clinical concepts in real-time for faster note creation.
problem Efficiently creating structured clinical notes with minimal keystrokes.
method Contextual autocompletion using shallow neural networks.
result Reduces keystrokes by 67% in real hospital environments.
Deep learning shows neural networks can be trained with limited data, revealing a low-dimensional manifold of optimal configurations.
problem How neural networks can be trained with limited data despite having billions of potential configurations.
method Using mutual information between layers of a deep neural network to speed up training and find optimal configurations.
result Adding structure to neural networks that enforces higher mutual information between layers speeds training and leads to more accurate results.
Study improves forecasting in betting markets using novel neural networks.
problem Improving short-term price movement predictions in betting exchanges.
method Innovative convolutional attention mechanisms applied to recurrent neural networks and bi-dimensional layers.
result All proposed innovations positively impact classification task performance.
Study creates a global living index to assess quality of life.
problem Long-term impacts of global economic changes on living conditions.
method Machine learning framework combining socio-economic factors.
result Developed a practical tool for policymakers to identify areas needing improvement.
New method learns intrinsic manifold from observed data for accurate localization.
problem Learning latent intrinsic geometry from observed high-dimensional data.
method Intrinsic Isometric Manifold Learning (IIML) using neural networks for metric estimation.
result IIML achieves accurate localization in ad-hoc sensor networks from imaging data.
This paper reviews hyper-parameter optimization methods for deep learning.
problem Designing and training deep neural networks is challenging and unpredictable.
method Reviews major optimization algorithms and services for hyper-parameter tuning.
result Comprehensive comparison of optimization algorithms and services.
AFP-CKSAAP predicts antifreeze proteins using k-spaced amino acid pairs with deep neural networks.
problem Predicting antifreeze proteins due to their diverse sequence characteristics.
method Deep neural network with skipped connections and ReLU non-linearity to learn protein sequence descriptors.
result AFP-CKSAAP achieves excellent prediction scores and high Youden's index (0.82) on independent dataset.
Smart meters detect dementia patients' daily activities to prevent crises.
problem Monitoring dementia patients' daily activities without intruding.
method Machine learning and signal processing for smart meter load disaggregation.
result SVM and Decision Forest models accurately detect ADLs and routine changes.
Living review of ML for particle physics, updated frequently.
problem Keeping up with rapid advancements in ML for particle physics.
method Creating a living document to list and update citations of ML applications.
result Provides a comprehensive list of ML citations for particle physics.
We address the problem of defining a network graph on a large collection of classes. Each class is comprised of a collection of data points, sampled in a non i.i.d. way, from some unknown underlying distribution. The application we consider in this paper is a large scale high dimensional survey of people living in the …
Study predicts social relationships using triadic influence from social networks.
problem Difficulty in quantifying social relationships and their dynamics.
method Real social networks of 13 schools, neural networks, high-dimensional embedding.
result Triadic influence achieves highest accuracy in predicting student relationships.
SoftAdapt dynamically adjusts loss weights for multi-part functions.
problem Slow convergence and poor weight selection for multi-part loss functions.
method SoftAdapt dynamically changes weights based on live performance statistics.
result Improved convergence and better weight selection for multi-part loss functions.
LiveTradeBench evaluates LLMs in live trading environments.
problem Static benchmarks fail to assess real-world trading ability.
method Live data streaming, portfolio management abstraction, multi-market evaluation.
result LLMs show distinct portfolio styles and adapt to live signals.
This paper analyzes CNNs for malware detection in cloud IaaS.
problem Malware vulnerability in cloud IaaS environments.
method Analysis of Convolutional Neural Networks (CNNs) for online malware detection using process-level performance metrics.
result State-of-the-art DenseNets and ResNets effectively detect malware in online cloud systems.
Backtests of structured strategies lose much of their predictive power in live trading.
problem Uncertainty in how marketed backtests predict live performance of structured strategies.
method Analysis of 1,726 structured strategies from ten global institutions.
result Raw backtests have limited portability into live trading and deteriorate sharply.
Real-time system detects accidents to save lives and reduce congestion.
problem Traffic congestion and accidents causing casualties and wasted time.
method Big data processing and computational intelligence techniques.
result The system can predict the possibility of accidents, even with false alarms.
This paper optimizes resource allocation for crowdsourced live streaming to improve viewer experience and reduce costs.
problem Improving viewer quality of experience (QoE) in crowdsourced live streaming.
method A prediction-driven resource allocation framework using machine learning to predict viewer numbers and proactively allocate resources.
result Maximizes viewer QoE and minimizes resource allocation costs through precise resource provisioning.
Self-Organizing Maps (SOM) are popular unsupervised artificial neural network used to reduce dimensions and visualize data. Visual interpretation from Self-Organizing Maps (SOM) has been limited due to grid approach of data representation, which makes inter-scenario analysis impossible. The paper proposes a new way to …
Optimizes retirement income with MBGs and neural networks for longevity risk.
problem Maximizing lifetime withdrawals while managing longevity risk.
method Neural-network optimization under stochastic mortality.
result International diversification and longevity pooling improve retirement outcomes.
ManiGen generates adversarial examples without classifier knowledge.
problem Vulnerability of neural network classifiers to adversarial examples.
method Generates adversarial examples by searching along the manifold.
result Adversarial examples generated by ManiGen are as successful as state-of-the-art generators.