DEEN learns energy and score functions from complex data.
problem Challenges in density estimation for high-dimensional data.
method Inference-free hierarchical framework using score matching and multilayer perceptrons.
result DEEN successfully learns energy and score functions from synthetic and high-dimensional data.
Deep learning improves gamma-ray energy estimation and event selection.
problem Improving gamma-ray event selection and energy estimation.
method Adapted convolutional neural networks (CNN) for gamma-ray astronomy.
result Significant improvement in gamma-ray energy estimation and event selection.
Paper proposes energy-efficient DNN training methods.
problem Energy-constrained deployment of deep neural networks.
method Weighted sparse projection and layer input masking integrated into DNN training.
result Framework provides higher accuracy with same or lower energy budgets.
Bayesian free energy remains bounded for deep ReLU networks in overparametrized cases.
problem Understanding the generalization performance of deep ReLU neural networks.
method Analyzes Bayesian free energy in overparametrized deep ReLU neural networks.
result Bayesian free energy is bounded even in overparametrized deep ReLU networks.
Training energy-based probabilistic models is confronted with apparently intractable sums, whose Monte Carlo estimation requires sampling from the estimated probability distribution in the inner loop of training. This can be approximately achieved by Markov chain Monte Carlo methods, but may still face a formidable obs…
Efficient neural network improves disaggregation of home energy usage.
problem Estimating power consumption of individual appliances from total home power.
method Fully convolutional neural network architecture with improved computational efficiency.
result Achieves state-of-the-art disaggregation performance with reduced training and prediction times.
Deep neural networks improve free energy calculations for peptide conformations.
problem Challenges in developing suitable mappings for free energy perturbation.
method Adapted machine learning approach to train deep neural networks for mapping between Boltzmann distributions.
result Accurate free energy differences calculated between thermodynamic states with spring centers separated by 1 Å and sometimes 2 Å.
This paper examines how energy in feature maps decays in deep neural networks.
problem Understanding energy decay in deep convolutional neural networks.
method Analyzes energy conservation and decay rates in various deep neural network architectures.
result Energy in feature maps decays polynomially or exponentially across layers.
A neural network model minimizes region-based free energy for faster inference in MRFs.
problem Efficient inference in complex Markov random fields (MRFs).
method Region-based Energy Neural Network (RENN) that directly minimizes region-based free energy.
result RENN outperforms other methods in marginal distribution estimation, partition function estimation, and MRF learning.
Deep learning models accurately recognize and estimate physical activity types and energy expenditure from wrist accelerometer data.
problem Rigorous evaluation of wrist-worn accelerometers for assessing physical activity across the lifespan.
method Built deep learning networks to extract spatial and temporal representations from time-series data, recognizing physical activity types and estimating energy expenditure.
result Deep learning models achieved high performance: F1 scores of 0.82, 0.81, and 95 for sedentary, locomotor, and lifestyle activities, respectively; root mean square error of 1.1 for EE estimation.
Paper proposes a neural network for disaggregating appliance-level energy consumption.
problem Estimating appliance-level electricity consumption from a single meter.
method Adapts a neural network to classify operational state changes of appliances.
result Competitive performance compared to existing methods in simulated experiments.
This paper tackles risk-aware energy scheduling for MEC networks with microgrids.
problem Risk in energy demand and supply for MEC networks powered by microgrids.
method Formulated an optimization problem with CVaR for energy consumption and generation, analyzed using a multi-agent stochastic game, derived solution with MADRL-based A3C algorithm.
result Significant performance gain by considering CVaR for high accuracy energy scheduling.
Deep learning depends on tuning layers near critical points.
problem Understanding how deep learning architectures depend on tuning parameters.
method Random energy approach to analyze statistical dependence in deep belief networks.
result Statistical dependence can propagate only if layers are tuned near critical points.
New deep learning model optimizes energy use in buildings.
problem Optimizing energy use and comfort in large buildings.
method Transformer-based metamodel trained with simulation and sensor data, calibrated with CMA-ES, optimized with multi-objective algorithms.
result Optimal settings reduce energy loads while maintaining thermal comfort and air quality.
ECC compresses DNNs for energy-constrained devices like UAVs and smartphones.
problem Energy-constrained deep neural networks in vision applications.
method ECC uses a bilinear regression model to estimate DNN energy consumption and optimizes compression to meet energy constraints.
result ECC achieves higher accuracy under the same or lower energy budget compared to state-of-the-art techniques.
Deep-Energy trains DNNs without labels using energy functions.
problem Training DNNs without manually annotated labels.
method Uses task-specific energy functions to train DNNs unsupervised.
result Trained DNNs provide better quality labels than direct minimization.
Bayesian deep learning improves building energy simulation accuracy.
problem Uncertainty in surrogate models for building energy performance.
method Training dropout neural networks and stochastic variational Gaussian Processes.
result Surrogate models reduce errors by up to 30% with uncertainty-aware sampling.
Unified detector calibration and simulation using MLE from generative models.
problem Combining detector calibration and simulation using traditional methods.
method Maximum likelihood estimation from conditional generative models.
result Prior-independent and non-Gaussian resolutions possible.
CERN improves activity recognition in videos with novel energy layer and p-values.
problem Recognizing group activities in videos at semantic levels.
method Two-level LSTM network with Confidence-Energy Recurrent Network (CERN).
result Superior performance compared to state-of-the-art approaches.
Paper optimizes neural network layers to reduce energy usage without sacrificing accuracy.
problem Energy consumption in deep neural networks during inference.
method Layerwise noise maximization to optimize reliability of memory elements.
result Reduces memory energy consumption by 3.3 times at equal accuracy.
New method uses neural networks for unbiased physical observable estimation.
problem Estimating physical observables with neural samplers.
method Asymptotically unbiased estimators for observables, including partition function-dependent ones.
result Superiority over existing methods in numerical experiments for the 2d Ising model.
New algorithms improve learning deep energy models.
problem Learning deep energy models efficiently and accurately.
method Proposed new algorithms combining GAN-style methods with traditional energy-based learning.
result SteinCD performs well in test likelihood, SteinGAN in generating realistic images.
Gamification and deep learning improve energy efficiency in smart buildings.
problem Lack of human engagement and motivation in smart building control.
method Modeling user interaction as a game, integrating IoT sensors and cyber-physical systems, using Deep Learning for forecasting.
result Improved energy efficiency through gamified smart building control.
HIP-NN models molecular energies using a deep neural network with hierarchical terms.
problem Accurately predicting molecular energies from quantum calculations.
method HIP-NN decomposes molecular properties into a sum of hierarchical terms generated by a neural network.
result Achieves state-of-the-art performance with 0.26 kcal/mol mean absolute error.
Deep learning boosts building energy load forecasting.
problem Short-term load forecasting in buildings.
method Stacked Boosters Network architecture with sparse interactions, parameter sharing, and equivariant representations.
result Outperforms state-of-the-art models in short-term load forecasting tasks.
A new method uses deep learning to predict full conditional distributions.
problem Lack of uncertainty information in conditional distribution predictions.
method Transformed distribution estimation into multi-class classification, using deep neural networks and a joint binary cross-entropy loss function.
result Improved accuracy in probabilistic solar energy forecasting.
Estimates uncertainty in bounding box regression for object detection.
problem Reliable deployment of deep object detectors in safety-critical tasks.
method Training variance networks with energy score as a proper scoring rule.
result Energy score leads to better calibrated and lower entropy predictive distributions.
FEPS models agents to learn and act in complex environments without deep neural networks.
problem Modeling complex adaptive systems and understanding self-organizing behavior.
method Introducing Free Energy Projective Simulation (FEPS) within the constraints of the free energy principle and active inference.
result FEPS agents resolve ambiguity and infer optimal policies in partially observable environments.
Energy dissipating networks control neural network behavior during inference.
problem Lack of provable guarantees for neural networks during inference.
method Iteratively compute descent directions with respect to a given energy function, ensuring convergence to the global minimum.
result Proven convergence of descent directions to the global minimum of the energy function.
A deep neural network improves document binarization accuracy.
problem Binarizing digital documents with historical degradations.
method Combines FCN with primal-dual network for end-to-end training.
result Achieves state-of-the-art binarization on four out of seven datasets.
Deep k-Means compresses CNNs by clustering weights and re-training, reducing energy consumption.
problem High energy consumption and large parameter count in deep convolutions.
method Applying k-means clustering on convolutional layer weights, sharing K cluster centers, and re-training with hard assignments. result Significant reduction in energy consumption and compression ratio without accuracy loss.
A DRL-based strategy improves vehicle tracking accuracy while saving energy.
problem Enhancing vehicle tracking accuracy in WSNs without increasing energy consumption.
method Decentralized strategy with dynamic reinforcement learning to adjust sensing areas.
result Simulation results demonstrate superior performance of DRL-aided design.
Enhanced X-ray polarimetry with deep learning for better exposure times.
problem Improving sensitivity of X-ray telescopic observations with imaging polarimeters.
method A weighted maximum likelihood combination of predictions from a deep ensemble of ResNet convolutional neural networks trained on Monte Carlo event simulations.
result Improves effective exposure times by ~45% for power-law source spectra.
DRL-DPT improves energy efficiency in wireless networks with deterministic power control.
problem Severe performance degradation in traditional ICIC schemes with complex interference patterns.
method Deep Reinforcement Learning with Deterministic Policy and Target (DRL-DPT) framework.
result Consistently outperforms existing schemes in terms of energy efficiency and throughput.
Deep neural networks are optimizable due to their multilayered structure.
problem Understanding why deep neural networks are easily optimizable despite their non-convex loss functions.
method Analysis of a spin glass model of deep neural networks using random matrix theory and algebraic geometry.
result The multilayered structure of deep neural networks leads to fewer stationary points, more clustered minima, and less severe tradeoffs between depth and width of minima.
This review compares various deep generative models.
problem Training deep neural networks to model data distributions.
method Comprehensive comparison of VAEs, GANs, flows, energy models, and autoregressives.
result Trade-offs and interrelationships among different models.
Novel framework finds globally optimal energy-efficient power control in wireless networks.
problem Energy-efficient power control in wireless networks.
method Branch-and-bound procedure with problem-specific bounds for faster convergence.
result Global solution for common energy-efficient power control problems with reduced complexity.
Study on the optimization landscape of half-rectified networks without simplifying assumptions.
problem Understanding the optimization landscape of deep neural networks, focusing on half-rectified networks.
method Theoretical analysis and empirical study of gradient descent on half-rectified networks.
result Proves that half-rectified single layer networks are asymptotically connected and provides bounds on the interplay between data distribution and model over-parametrization.
Proposes linking energy and force uncertainty in deep learning potentials.
problem Uncertainty in predicted energies and forces in machine learning models.
method Introduces a spatially correlated noise process to link energy and force uncertainty.
result Demonstrates the approach on molecular datasets, linking energy and force uncertainties.
A hybrid neural network optimizes AI deployment on edge and cloud for energy efficiency.
problem Energy and resource constraints in edge devices for deep learning models.
method Conditionally deep hybrid neural network with quantized layers at edge and full-precision layers at cloud.
result Early classification at the edge reduces energy consumption by 5.5x on CIFAR-10 dataset.
Paper proposes a new method for estimating treatment effects using interpretable deep learning models.
problem Estimating treatment effects from observational data with interpretability.
method Proposes a novel objective function using energy distance balancing score and neural additive models for improved interpretability.
result Demonstrates superior performance over state-of-the-art methods in semi-synthetic experiments.
Geometric Occam's Razor shapes deep learning solutions.
problem Understanding the regularization in over-parameterized neural networks.
method Analyzing the geometric model complexity and Dirichlet energy in neural networks.
result Over-parameterized neural networks are implicitly regularized by geometric model complexity.
Deep neural networks improve real-time power system state estimation and forecasting.
problem Real-time monitoring of power grids with large-scale renewable generation and electric vehicles.
method Developed a novel model-specific DNN for real-time PSSE and used deep RNNs for forecasting.
result Improved performance compared to existing alternatives, including Gauss-Newton PSSE solver.
This paper reviews methods to create compact neural networks for IoT applications.
problem Complex deep neural networks are costly and slow, hindering real-world deployment.
method Automatic synthesis of compact, accurate DNN/LSTM models.
result Compact neural networks reduce energy consumption, memory, and inference time.
The paper investigates how reducing memory supply voltage improves DNN accuracy under bit-cell faults.
problem Reducing energy consumption in deep neural networks by lowering memory supply voltage introduces bit-cell faults.
method The authors explore the robustness of DNN architectures to bit-cell faults and propose a regularizer to mitigate their effects.
result Operating the system in a faulty regime can save energy without significantly reducing accuracy.
A new deep learning method for energy disaggregation.
problem Energy disaggregation or non-intrusive load monitoring (NILM) to identify individual appliance power usage.
method Sequence to Point Learning based on Bidirectional Dilated Residual Network (BRDN).
result Our method outperforms state-of-the-art approaches in all appliances on REDD and UK-DALE datasets.
Paper introduces CRP-O framework for uncertainty quantification in deep operators.
problem Uncertainty quantification in energy-efficient deep learning algorithms, especially in SNNs.
method CRP-O framework using RP networks and SCP, with Gaussian Process Regression for super-resolution.
result Enhanced uncertainty bounds improve UQ estimates compared to existing methods.
Modeling wind dynamics in Saudi Arabia using deep learning and stochastic PDEs.
problem Accurately modeling spatio-temporal wind patterns in a large, diverse, and understudied region.
method Energy distance-based spatial reduction, sparse stochastic Echo State Network, non-stationary stochastic PDE reconstruction.
result Produces more accurate wind speed and energy forecasts, saving $1 million annually.