Deep learning identifies plant stresses accurately and quantitatively.
problem Manual inspection of plant stresses is subjective and time-consuming.
method Developed an explainable deep learning model using gradient-weighted class activation mapping.
result Model accurately identifies and quantifies diverse foliar stresses in soybeans and other species.
We propose a technique for making Convolutional Neural Network (CNN)-based models more transparent by visualizing input regions that are 'important' for predictions -- or visual explanations. Our approach, called Gradient-weighted Class Activation Mapping (Grad-CAM), uses class-specific gradient information to localize…
CNNs help diagnose diabetic retinopathy by localizing lesions.
problem Diabetic retinopathy diagnosis requires identifying lesions in fundus images.
method Post-attention technique (Grad-CAM) on deep learning models' penultimate layer.
result InceptionV3 model achieves best performance and localizes lesions better.
3D convolutional neural networks (3D-CNN) have been used for object recognition based on the voxelized shape of an object. In this paper, we present a 3D-CNN based method to learn distinct local geometric features of interest within an object. In this context, the voxelized representation may not be sufficient to captu…
Study evaluates deep learning models for solar flare prediction with interpretability analysis.
problem Lack of interpretability in deep learning models for solar flare prediction.
method Proximity-based metric for analyzing attribution maps generated by Guided Grad-CAM.
result Models' predictions align with active region characteristics, offering insights into their behavior.
3D-CNN method visualizes localized geometric features for manufacturability analysis.
problem Interpreting 3D-CNN decisions for complex geometries.
method 3D-CNN with surface normals, 3D-GradCAM for feature visualization.
result Identifies critical local features for manufacturability.
Graph Convolutional Neural Networks identify molecular functional groups.
problem Automatic discovery of molecular functional groups to reduce lab experiments.
method Graph Convolutional Neural Networks (GCNNs) trained on relational graphs of molecules.
result Grad-CAM method identified the most specific and relevant molecular substructures.
MDF represents time series motifs as images for improved classification.
problem Classifying time series data with high-order patterns.
method Motif Difference Field (MDF) using Fully Convolutional Networks (FCN).
result MDF outperforms other methods on UCR time series datasets.
Deep learning ensemble improves Alzheimer vs. Mild Cognitive Impairment diagnosis.
problem Differentiating Alzheimer Disease from Mild Cognitive Impairment.
method Hybrid deep learning ensemble framework using MRI slices, pretrained models, and stacked ensemble learning.
result State-of-the-art accuracy (99.21%) for Alzheimer vs. Mild Cognitive Impairment classification.
Gradient Weighted Superpixels improve CNN interpretability without sacrificing speed.
problem Efficiency vs. interpretability trade-off in CNNs, especially for large input volumes.
method Gradient-based pixel scoring techniques applied to superpixels.
result Superpixels approximate LIME in a fraction of the time, improving interpretability.
XCM improves MTS classification with explainable deep learning.
problem Lack of explainable deep learning models for MTS classification.
method XCM is a compact CNN that extracts variable and timestamp information directly from input data.
result XCM outperforms state-of-the-art MTS classifiers on large and small datasets.
MAPS and MAPS-SE learn from multiple suboptimal experts to improve policies efficiently.
problem Learning from multiple suboptimal experts in reinforcement learning.
method Active policy improvement from multiple black-box oracles.
result MAPS and MAPS-SE achieve sample efficiency and state-wise imitation learning.
The paper proves deep neural networks with analytic activation can approximate any function.
problem Approximating functions with neural networks using analytic activation functions.
method Elementary proofs for real and complex networks, Stone-Weierstrass theorem, Mergelyan's theorem.
result Closure of neural network classes equals space of polynomials for analytic activation.
Self-attention model improves HAR from wearable sensors.
problem Capturing spatio-temporal context from sensor data.
method Proposes a self-attention based neural network model.
result Significant performance improvement over state-of-the-art models.
Improved generalization bounds for CNNs using Rademacher complexity.
problem Establishing non-vacuous generalization bounds for deep learning models.
method Rademacher complexity framework with novel contraction lemmas for high-dimensional mappings.
result Enhanced generalization bounds for a broader class of activation functions.
Visualizes deep neural networks for speech recognition using learned topographic filter maps.
problem Unintuitive internal structure of deep neural networks complicates activation visualization.
method Trains a convolutional speech recognition model with filters arranged in a 2D grid, highlighting similar filters.
result Topographic filter maps visualize artificial neuron activations more intuitively.
A new HAR algorithm uses U-Net for pixel-level gesture recognition.
problem Multi-class window problem in traditional HAR methods.
method U-Net network for activity labeling and prediction at each sampling point.
result Highest accuracy and F1-score compared to other methods.
Visualizes DNNs using topographic maps for better understanding.
problem Difficulty in understanding how DNNs solve tasks.
method Adapting neuroscience methods to visualize DNN activations.
result Improved transparency and interpretability of DNN-based systems.
Enhances sound texture in CNN for better acoustic scene classification.
problem Limited understanding of how CNNs perceive audio scenes.
method Used Class Activation Mapping (CAM) to analyze log-Mel features and proposed edge enhancement using DoG and Sobel operators.
result Edge-enhanced log-Mel features improve CNN performance in acoustic scene classification.
This paper classifies typhoon damage features using aerial photography.
problem Typhoon damages and slow recovery times.
method Aerial photography for classification of eight classes including land covers and disaster areas.
result Visualize and explain typhoon disaster features using convolutional activation maps.
Study embeddings between Barron spaces with various activation functions, focusing on RePU.
problem Understanding the influence of activation functions on infinitely wide neural networks.
method Prove embeddings by constructing push-forward maps on measures representing functions.
result Barron spaces with RePU activation have a hierarchical structure similar to Sobolev spaces.
Risk-based active learning improves SHM decision-making.
problem Lack of prior labels for structural health monitoring.
method Risk-based active learning approach to guide data labeling.
result Improves decision-maker's performance in SHM.
Active learning method balances bias and variance under class imbalance.
problem Active learning under label shift when class proportions differ.
method Mediated Active Learning under Label Shift (MALLS) using a 'medial distribution'.
result MALLS reduces asymptotic sample complexity under arbitrary label shift.
AReLU uses attention-based rectification to improve neural network performance.
problem Improving neural network performance through better activation functions.
method Integrates attention mechanism with rectified linear unit (ReLU) to learn and scale feature maps.
result AReLU significantly boosts performance of most network architectures with minimal changes.
URNNs are as expressive as general RNNs with ReLU activations.
problem Expressiveness of URNNs compared to general RNNs.
method Input-output equivalence between URNNs and contractive RNNs with ReLU activations.
result URNNs are as expressive as general RNNs with ReLU activations.
Building models, or maps, of robot environments is a highly active research area; however, most existing techniques construct unstructured maps and assume static environments. In this paper, we present an algorithm for learning object models of non-stationary objects found in office-type environments. Our algorithm exp…
A new active learning method for one-class classification using two classifiers.
problem Reducing manual labeling efforts in one-class classification.
method Uses two one-class classifiers for active learning, proposing new query strategies.
result Improved results compared to existing methods on various datasets.
Balanced Activation improves object detection performance on long-tailed datasets.
problem Mismatch between training and testing label distributions in object detection.
method Introduces Balanced Activation (Balanced Softmax and Balanced Sigmoid) to address label distribution shift.
result Balanced Activation provides ~3% gain in mAP on LVIS-1.0 compared to state-of-the-art methods.
Improved EXACT strategy reduces GNN memory consumption and runtime.
problem Efficiently training large-scale GNNs with reduced memory usage.
method Block-wise quantization of intermediate activation maps with improved variance minimization.
result Further reduction in memory consumption (>15%) and runtime speedup (5%) with similar performance trade-offs.
First the title could be also understood as ``3-manifolds related by non-zero degree maps" or "Degrees of maps between 3-manifolds" for some aspects in this survey talk. The topology of surfaces was completely understood at the end of 19th century, but maps between surfaces kept to be an active topic in the 20th centur…
Framework tackles class imbalance and noisy labels in active learning.
problem Class imbalance and noisy labels in real-world datasets.
method Uses foundation model priors to select informative samples for active learning.
result Substantial annotation savings (over 50%) with preserved performance and robustness.
Random feature maps improve forecasting of chaotic dynamical systems.
problem Forecasting chaotic dynamical systems with high accuracy.
method Data-driven random feature maps with tanh activation, skip connections, and localization.
result Effective forecasting skill for dynamical systems with dimensions up to 512.
ActiLabel learns activity patterns across diverse sensor devices.
problem Limited adoption of activity recognition models across different domains due to diverse sensor devices.
method Combination of graph model and optimal tiered mapping for learning activity labels.
result Superior performance compared to state-of-the-art methods on public datasets.
We consider active maximum a posteriori (MAP) inference problem for Hidden Markov Models (HMM), where, given an initial MAP estimate of the hidden sequence, we select to label certain states in the sequence to improve the estimation accuracy of the remaining states. We develop an analytical approach to this problem for…
A new multi-class active learning method combining informativeness and representativeness.
problem Efficiently labeling large datasets with limited resources.
method A hybrid informative and representative criterion approach for multi-class active learning.
result The proposed method outperforms state-of-the-art methods on multiple UCI datasets.
This article compares and evaluates active learning methods for outlier detection with one-class classifiers.
problem Selecting an appropriate active learning method for outlier detection with one-class classifiers is challenging due to diverse assumptions and objectives.
method The article categorizes various methods, proposes evaluation criteria, and conducts extensive experiments to compare them.
result Formulated guidelines for selecting active learning methods for outlier detection with one-class classifiers.
Nodes in real world networks often have class labels, or underlying attributes, that are related to the way in which they connect to other nodes. Sometimes this relationship is simple, for instance nodes of the same class are may be more likely to be connected. In other cases, however, this is not true, and the way tha…
New hypothesis and method reduce noise in saliency maps.
problem Noisy saliency maps from deep neural networks.
method Proposed a new hypothesis and Rectified Gradient method.
result Rectified Gradient effectively reduces saliency map noise.
Active learning reduces simulation needs for high-fidelity mobility maps.
problem Efficiently training machine learning classifiers for high-fidelity mobility maps.
method Active learning based on PAC learning theory to reduce simulation needs.
result Our sampling algorithm trains neural networks with higher accuracy using less than half the number of simulations.
CBDA improves active learning for semantic segmentation, especially with imbalanced classes.
problem Class imbalance degrades performance in domain adaptive active learning.
method Class Balanced Dynamic Acquisition (CBDA) selects more balanced labels for active learning.
result CBDA increases minority class performance and outperforms baselines by 0.6-2.4 mIoU.
SympNets identify Hamiltonian systems from data using linear, activation, and gradient modules.
problem Identifying Hamiltonian systems from data.
method Composition of linear, activation, and gradient modules; universal approximation theorems.
result SympNets can approximate arbitrary symplectic maps and generalize well to various Hamiltonian systems.
Deep neural networks with various activation functions can approximate Hölder smooth functions.
problem Expressivity of deep neural networks with general activation functions.
method Investigates approximation ability of deep neural networks with a broad class of activation functions, including Hölder smooth functions.
result Derives the required depth, width, and sparsity of deep neural networks to approximate Hölder smooth functions.
Method learns feature maps from deep CNN layers for weakly supervised chest pathology localization.
problem Localization of chest pathologies in X-ray images is challenging due to varying sizes and appearances.
method Class-aware deep multiscale feature learning using intermediate feature maps from CNN layers.
result Improves localization performance of small pathologies like nodules and masses.
Universal approximation for ODENet and ResNet with a single activation function.
problem Approximating complex dynamical systems with limited vector fields.
method Examined ODENet and ResNet with vector fields composed of a single activation function and affine mapping.
result ODENet and ResNet with restricted vector fields can uniformly approximate those with general vector fields.
NEU learns feature maps for any model class preserving UAP.
problem Effective feature representation for predictive performance.
method Meta-procedure NEU for UAP-invariant feature maps.
result NEU learns feature maps with UAP for most model classes.
The author constructs height functions and studies their first derivatives on closed manifolds.
problem Understanding the first derivatives of height functions on closed manifolds.
method Constructing explicit maps and height functions, studying their global behaviors.
result Detailed study of the first derivatives of height functions on closed manifolds.
Generalizes neural networks for infinite-dimensional mappings, including PDE solutions.
problem Learning mappings between infinite-dimensional spaces and finite-dimensional approximations.
method Graph kernel network architecture with message passing for kernel integration.
result Competitive performance compared to state-of-the-art solvers for PDEs.
Softmax cross-entropy optimizes mutual information in neural networks.
problem Understanding the relationship between mutual information and classification neural networks.
method Demonstrated that optimizing softmax cross-entropy maximizes mutual information between inputs and labels.
result Softmax cross-entropy can approximate mutual information and highlight relevant image regions.