Paper introduces ABC-Net, a binary CNN that maintains high accuracy with reduced memory and power.
problem Accuracy loss in binary CNNs during inference.
method Approximating full-precision weights with binary bases and using multiple binary activations.
result ABC-Net achieves comparable prediction accuracy to full-precision CNNs, even on challenging datasets.
BIL allows binary input data in CNNs, improving performance on multimodal datasets.
problem Efficient execution of CNNs on edge devices with reduced bit width.
method BIL concept that learns bit-specific binary weights for binary input data.
result BIL outperforms full precision weights by 1.92% on multimodal datasets.
Paper proposes a robust deep graph-based classifier for noisy labels.
problem Difficulty in feature learning with noisy training labels.
method Convolutional neural networks with graph Laplacian regularization (GLR).
result Proposed method outperforms state-of-the-art classifiers on noisy datasets.
TentacleNet improves binarized CNNs, reducing accuracy loss and memory usage.
problem Excessive accuracy loss in binarized CNNs.
method Parallelization inspired by ensemble learning theory, end-to-end trainable compact topology.
result Significant memory savings compared to state-of-the-art binary ensemble methods.
Improves two-stage hashing methods for better image retrieval.
problem Developing efficient binary codes for image retrieval.
method Theoretical analysis and empirical improvements of two-stage hashing methods using high-capacity hash functions.
result Proposes a novel two-stage hashing method significantly outperforming previous studies.
Study on CNNs' learning rates and approximation capacities.
problem Learning rates and approximation capacities of CNNs.
method New approximation bound and covering number analysis for CNNs.
result Derives minimax optimal convergence rates for CNNs in various learning problems.
CNNs improve supernovae classification.
problem Classifying supernovae light-curves efficiently.
method Adapted CNNs for time series, Siamese CNN for sparsity.
result CNNs outperform current methods.
AOFP prunes CNN filters faster and more accurately.
problem Finding optimal CNN width and pruning filters efficiently.
method Binary search, random masking, multi-path framework.
result AOFP achieves faster pruning with minimal accuracy loss.
Improved action detection for multi-person videos using attention filtering.
problem Difficulty in distinguishing relevant parts of multi-person videos for action detection.
method Fovea attention filtering and generalized binary loss function.
result 20% relative improvement in mAP over baseline in AVA dataset.
A method extracts binary features directly from CS measurements for compressive image classification.
problem Efficiently classify images using compressive sensing without reconstruction.
method DCT-based approach for binary feature extraction from CS measurements, feature fusion with CNN features.
result Fused features outperform state-of-the-art methods in image classification.
Alternative to convolutions using decision trees for neural networks.
problem Replacing complex convolutions with simpler decision-based layers.
method Binary decisions as indices to conditional distributions, trained using backpropagation.
result Performance similar to conventional neural networks, with runtime improvements.
A CNN-based approach tackles malware image classification imbalance.
problem Imbalance in malware families during image classification.
method Proposes a weighted softmax loss to address imbalance.
result Improves classification performance on malware images.
Evolutionary method reduces CNN complexity for mobile devices.
problem Efficiently compressing CNNs for mobile devices.
method Evolutionary algorithm to identify and remove redundant convolution filters.
result Generates an extremely compact CNN with improved compression and speed-up ratios.
CNN predicts epileptic seizures from iEEG signals.
problem Accurately forecasting epileptic seizures to reduce patient uncertainty.
method Used a CNN for seizure prediction without hand-crafted features.
result CNN models outperformed previous methods on public datasets.
Enhances CNN feature extractors' separation capacity analysis.
problem Understanding the separation capacity of CNNs.
method Extending Cover's function-counting theory, analyzing scattering networks.
result Identifies factors affecting scattering networks' separation capacity.
Deep learning improves GW signal detection efficiency and robustness.
problem Traditional matched-filtering techniques are limited in detecting new GW signals.
method Optimized CNN models with techniques like batch normalization and dropout.
result CNN models are robust to the variation of GW waveform parameters.
SAPSAM trains CNNs on lung CTs with binary labels, improving CPA detection and localization.
problem Chronic Pulmonary Aspergillosis (CPA) detection and localization on CT scans using binary labels.
method Binary labels, average intensity projections, 2D RGB-like images, hierarchical CNN architectures.
result High classification accuracy, precise localization, predictive power of 2-year survival.
Modified AUC improves CNN training by considering model confidence.
problem Improving binary classifier performance metrics.
method Proposes a modified AUC metric that incorporates model confidence into BCE loss for CNN training.
result Demonstrates improved performance on three datasets: MNIST, prostate MRI, and brain MRI.
NICE framework explains CNN predictions and compresses images.
problem Interpreting deep neural networks and efficient image compression.
method End-to-end Neural Image Compression and Explanation (NICE) framework.
result NICE achieves high compression rates while maintaining classification accuracy.
CNN-PCA method uses deep learning to parameterize complex geological models.
problem Representing complex geological models in a low-dimensional space.
method CNN-PCA method combines PCA and CNN to honor geological features.
result CNN-PCA provides high-quality realizations and history matching results.
Machine learning classifies gravitational wave signals to test General Relativity.
problem Testing General Relativity with gravitational wave signals from binary black hole mergers.
method Convolutional Neural Networks (CNNs) trained on whitened waveforms and response function type observables.
result CNNs improve classification sensitivity by a factor of approximately 33 compared to whitened waveforms.
Deep CNN model predicts neuronal cell health from images.
problem Predicting the biological activity of chemical compounds on neuronal cells.
method Deep convolutional neural network (CNN) with residual connections.
result Achieved 99.6% accuracy in distinguishing treated from untreated cells.
Improved CNN training with BDFA reduces computational cost and improves accuracy.
problem Low training performance of DFA in CNN.
method Combining DFA with BP, introducing feedback weight initialization, and proposing BDFA.
result BDFA shows better performance than conventional BP, especially in small datasets.
CNNs improve classification of small spectra datasets with one-shot learning.
problem Limited training data and computational cost for new substance classes.
method Reformulate multi-class problem to binary, use Siamese CNN with novel sampling strategy.
result Achieves one-shot learning with high accuracy for unseen substance classes.
Batch normalization biases linear models towards uniform margins, improving performance in binary classification.
problem Understanding the implicit bias of batch normalization in linear models and neural networks.
method Analyzing gradient descent convergence on linear models and two-layer CNNs with batch normalization.
result Gradient descent with batch normalization in linear models converges to a uniform margin classifier with an exponential convergence rate.
Convolutional LSTM detects emphysema in lung cancer screening images.
problem Learning disease signatures from weakly annotated volumetric medical images.
method 3D volumetric images analyzed as a sequence of 2D images using convolutional LSTM.
result Convolutional LSTM model outperformed other methods in detecting emphysema.
We introduce the hierarchical compositional network (HCN), a directed generative model able to discover and disentangle, without supervision, the building blocks of a set of binary images. The building blocks are binary features defined hierarchically as a composition of some of the features in the layer immediately be…
New neural network separates singing voices from music using cross entropy loss.
problem Separating singing voices from music accompaniment.
method Deep Convolutional Neural Network (CNN) trained with Ideal Binary Mask (IBM) and cross entropy loss.
result Proposed CNN outperforms existing systems in MIREX evaluations.
Pooling is not essential for image classification stability.
problem The necessity of pooling for image classification stability.
method Rigorous empirical testing of CNNs without pooling.
result Pooling is neither necessary nor sufficient for optimal deformation stability in CNNs.
CTM uses conjunctive clauses for image recognition, achieving high accuracy.
problem High computational complexity and lack of interpretability in CNNs.
method Introduces Convolutional Tsetlin Machine (CTM) using conjunctive clauses in propositional logic.
result CTM achieves competitive accuracy on various benchmarks, including MNIST and Fashion-MNIST.
A new method compresses point clouds efficiently, outperforming existing techniques.
problem Efficiently compressing large point cloud datasets for VR applications.
method Learned convolutional transforms and uniform quantization for joint rate and distortion optimization.
result Significant rate-distortion improvement (51.5% BDBR savings) on Microsoft Voxelized Upper Bodies dataset.
Novel BCI system classifies imagined speech with high accuracy.
problem Classifying imagined speech from brain signals.
method Hierarchical deep learning with CNN and autoencoder.
result Achieved 83.42% average accuracy across six phonological tasks.
Our goal is to design architectures that retain the groundbreaking performance of CNNs for landmark localization and at the same time are lightweight, compact and suitable for applications with limited computational resources. To this end, we make the following contributions: (a) we are the first to study the effect of…
Paper classifies movie genres using multimodal data.
problem Challenging task of multi-label movie genre classification.
method Created dataset from video clips, subtitles, synopses, and posters. Extracted features using various descriptors. Evaluated using different classifiers and late fusion strategy.
result Best F-Score result of 0.628 achieved by combining LSTM on synopses and CNN on movie trailer frames.
DeepIrisNet2 learns iris codes without iris segmentation or normalization.
problem Iris recognition under non-ideal conditions.
method Deep learning framework with spatial transformer layers and dual CNN segmentation.
result Significantly improves iris recognition performance.
This study uses CNN-IOs to estimate MRI image reconstruction performance bounds.
problem Estimating task-based performance limits for MRI image reconstruction methods.
method Utilized stylized multi-coil SENSE MRI systems and deep-generated stochastic models to estimate IO performance.
result Estimation of IO performance provides guidance for designing under-sampled MRI systems.
SGD converges to critical points of normalized margin in late-stage training for homogeneous neural networks.
problem Analyzing the implicit bias of SGD on homogeneous neural networks.
method Interpreting SGD dynamics as an Euler-like discretization of a conservative field flow associated with the normalized classification margin.
result Normalized SGD iterates converge to the set of critical points of the normalized margin at late-stage training.
A new deep learning model for tabular data improves accuracy over GBDT.
problem Improving accuracy in tabular data classification.
method Differentiable forest with sparse attention mechanism.
result The differentiable forest achieves higher accuracy than GBDT on tabular datasets.
New BNN architecture achieves high accuracy without complex tricks.
problem Training accurate Binary Neural Networks (BNNs) from scratch is challenging.
method Revisited design principles and a simple training strategy.
result BinaryDenseNet achieves high accuracy on ImageNet.
V-CNN improves CNN performance in network intrusion detection.
problem Applying CNN directly to non-image data leads to poor performance.
method Integrates data visualization before CNN modeling.
result Significantly outperforms other studies in network intrusion detection.
This research uses machine learning to approximate ideal and hotelling observer performance for binary signal detection.
problem Approximating the Ideal and Hotelling Observers for binary signal detection tasks.
method Supervised learning methods, including CNNs and SLNNs, are employed to approximate the IO and HO test statistics.
result The proposed supervised learning methods provide accurate approximations of the IO and HO test statistics.
Deep learning improves defect classification in real-time surface inspection.
problem Real-time defect classification in manufacturing industry using limited datasets.
method Convolutional Neural Networks (CNNs) designed for speed and accuracy, neural data augmentation for class imbalance.
result 98.0% accuracy in binary defect classification with 22,000 labeled images.
1-D CNNs classify pupil size variations in scotopic conditions.
problem Handling wide inter-subjects variability in pupil analysis.
method 1-D Convolutional Neural Networks applied directly to raw pupil size data.
result 1-D CNNs provide high accuracy in classifying short-range pupil size sequences.
Enhances CNN generalization in early learning with hierarchical transfer.
problem Improving CNN generalization in limited training time for real-time applications.
method Hierarchical transfer CNN framework combining shallow and cloud CNNs.
result Significant improvement in testing accuracy, up to 20% for CIFAR-10.
New approximative kernels improve PDE-G-CNNs for geometric deep learning.
problem Inaccurate approximations of exact kernels in PDE-G-CNNs.
method Developed new approximative kernels that work regardless of spatial anisotropy.
result New kernels provide better error estimates and maintain reflectional symmetries.
CNNs improve medical image classification with few samples.
problem Classifying medical images with limited training data.
method Transfer learning using CNNs, representation extraction, and a novel metric for performance prediction.
result CNN-based transfer learning outperforms feature-based methods with high correlation to test set performance.
Paper proposes a method to accurately match soft skills in job ads.
problem Matching soft skills in job ads leads to false positives.
method Phrase-matching approach with context-based binary classification.
result LSTM tagging-based input representation achieved highest recall of 83.92%.
AT-CNNs show improved shape recognition over texture recognition.
problem Understanding adversarial training's impact on CNNs' feature learning.
method Systematic qualitative and quantitative approaches to interpret AT-CNNs.
result Adversarial training reduces texture bias and improves shape recognition.