DNM learns efficient representations of input data using deep neural maps.
problem Learning efficient representations of input data.
method Unsupervised representation learning and visualization using deep convolutional networks and self-organizing maps.
result DNM can learn efficient representations of the input data, reflecting class characteristics.
Deep neural network predicts traffic flow on city maps.
problem Short-term traffic flow prediction on high-resolution city maps.
method UNet-based deep convolutional neural network with densely connected layers.
result Best performance on the Traffic4cast challenge 2019.
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.
The feature map obtained from the denoising autoencoder (DAE) is investigated by determining transportation dynamics of the DAE, which is a cornerstone for deep learning. Despite the rapid development in its application, deep neural networks remain analytically unexplained, because the feature maps are nested and param…
Proposes a new method to prevent overfitting in deep neural networks.
problem Overfitting in deep neural networks with many trainable parameters.
method Randomly replaces elements in feature maps with specific values during training.
result Improves the testing performance of deep neural networks on benchmark datasets.
Maps are an important medium that enable people to comprehensively understand the configuration of cultural activities and natural elements over different times and places. Although massive maps are available in the digital era, how to effectively and accurately access the required map remains a challenge today. Previo…
Improved saliency maps for deep neural networks with reduced noise.
problem Noisy explanations in Integrated Gradients for deep neural networks.
method SmoothTaylor, adaptive noising, and SmoothGrad techniques.
result SmoothTaylor and adaptive noising generate better quality saliency maps.
New nonlinear saliency maps improve deep neural network interpretability.
problem Lack of understanding why and how deep neural networks make decisions.
method Developed novel nonlinear saliency maps to better interpret deep neural networks.
result Nonlinear saliency maps provide more specific drivers of classification on complex examples.
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.
Characterizes test error in learning with deep, structured feature maps.
problem Characterizing test error in learning with deep, structured feature maps.
method Asymptotic analysis of feature covariance and population covariance.
result Closed-form formula for feature covariance in Gaussian rainbow neural networks.
Deep neural networks classify chaotic time series.
problem Classifying chaotic time series with high accuracy.
method Train neural networks on simpler systems to classify more complex ones.
result Convolutional neural networks outperform other networks for time series classification.
Study neural networks by mapping correlations, revealing essential statistics.
problem Understanding information processing in trained neural networks.
method Characterize neural network as distribution transformations, focusing on correlation functions.
result Higher-order correlations are crucial for internal layers, while input layer captures more.
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.
This paper simplifies deep learning networks by mapping them to a linear function of a feature map.
problem Understanding how weights in deep networks coordinate across layers and generalize.
method Reparameterizes DNNs as a linear function of a feature map, transforming depth-dependencies into tensor products.
result Develops sample compression representation of neural networks in terms of support vectors, showing sample complexity of O(ns/epsilon).
Deep neural-kernel models combine neural networks and kernel machines for scalable large datasets.
problem Combining neural networks and kernel machines for efficient large-scale learning.
method Hybrid neural-kernel architecture using explicit feature mapping and pooling layers.
result The deep neural-kernel models are effective and scalable on benchmark datasets.
Paper tackles speaker verification by removing reverberation using deep LSTM networks.
problem Improving speaker verification accuracy in reverberant environments.
method Dual-label deep LSTM networks trained to map reverberant to clean speech features.
result Evaluates performance using EERs, showing improved accuracy.
Deep neural features identify unique vehicles from dash-cam feeds.
problem Identifying unique vehicles in dash-cam feeds for self-driving cars.
method Used pretrained YOLO network feature maps to create deep integrated feature signatures (DIFS) for 700 images of 35 vehicles and 340 images of 17 vehicles.
result Correctly identified unique vehicles at 96.7% for high resolution data and 86.8% for lower resolution data.
One pixel can significantly alter deep neural network outputs, revealing propagation patterns and vulnerability hotspots.
problem Understanding how a single pixel modification affects deep neural networks.
method Propagation Maps and locality analysis to visualize and understand the impact of pixel modifications.
result One pixel modifications can propagate through deep networks, affecting the final output and revealing vulnerability patterns.
Deep networks don't improve on shallow ones for finding minima.
problem Improving representation of multidimensional mappings with deep neural networks.
method Numerical training methods to find minima in deep and shallow networks.
result Minima found with deep networks are worse than those found with shallow networks.
Deep neural networks map brain lesions to deficits for better brain function understanding.
problem Mapping the functional brain organization from pathological lesions.
method Deep generative neural network architectures, specifically variational convolutional volumetric auto-encoders.
result Our model outperforms established methods in lesion-deficit inference across various scenarios.
New method calibrates neural network predictions for better reliability.
problem Improper probability estimates from deep networks leading to unreliable predictions.
method Proposes a constrained optimization approach for a monotonic calibration map.
result Achieves state-of-the-art performance across various datasets and models.
AGOP mechanism explains deep neural collapse in neural networks.
problem Explaining the rigid structure of data representations in deep neural networks.
method Introducing AGOP and Deep RFM to demonstrate DNC.
result AGOP mechanism causes deep neural collapse in neural networks.
A new method quantifies feature-map discriminativeness for efficient pruning of deep neural networks.
problem Efficiently pruning deep neural networks to reduce computation while maintaining accuracy.
method Presented a novel mathematical formulation (Discriminant Information, DI) to quantify feature-map discriminativeness, enabling efficient pruning and intra-layer mixed precision quantization.
result Our pruned ResNet50 achieves 44% FLOPs reduction without any Top-1 accuracy loss.
Bayesian deep learning improves geostatistical mapping with auxiliary data.
problem Traditional geostatistical methods are limited in feature learning and uncertainty estimation.
method Deep neural networks learn complex relationships from auxiliary data for probabilistic mapping.
result Deep learning produces detailed, probabilistic maps with uncertainty estimates.
The paper introduces Causal Neural Operators to approximate operators in stochastic analysis.
problem Leveraging temporal structure in non-linear operators for deep learning models.
method Designing a deep learning model framework for infinite-dimensional linear metric spaces.
result Causal Neural Operators can uniformly approximate Hölder or smooth trace class operators.
Deep learning maps road safety features from streetview imagery.
problem Accurate mapping of road safety features for safer roads.
method Proposes a deep learning approach with LSTM to capture spatial autocorrelation.
result Model outperforms baseline methods in mapping road safety features.
DeepLocalization uses neural networks to localize vehicles from landmarks.
problem Vehicle self-localization from multi-modal sensor data and a reference map.
method Deep neural network that regresses vehicle pose from unordered landmarks.
result DeepLocalization achieves state-of-the-art accuracy and is faster than related work.
Stochastic neural networks can approximate any function, even with correlated outputs.
problem Approximating functions with stochastic outputs and correlations.
method Investigating deep sigmoid belief networks to approximate any stochastic mapping.
result Minimal number of layers and units needed for approximation.
Constructs classifiers for neural networks with specific data configurations.
problem Finding global minima of deep ReLU neural networks on sequentially separable data.
method Explicitly constructs zero loss neural network classifiers using cumulative parameters and truncation maps.
result Global minimizers can be described with a limited number of parameters based on the data structure.
Study on limits and cut-off phenomena in deep neural networks.
problem Understanding the behavior of deep neural networks as the number of layers increases.
method Analysis of semi-invariant metrics and application of non-commutative ergodic theorems.
result Observation of a cut-off phenomenon in the number of layers for random network initialization.
Deep polynomial neural networks measure their expressiveness by the dimension of their functional space.
problem Measuring the expressiveness of deep polynomial neural networks.
method Analyzing the algebraic variety defined by the polynomial neural network's weights and activations.
result The dimension of the algebraic variety is a precise measure of the network's expressiveness.
We propose using category theory to unify deep learning architectures.
problem Lack of a coherent bridge between model constraints and implementations.
method Apply category theory to unify neural network design.
result Theory recovers constraints from geometric deep learning and encodes standard constructs.
Deep neural networks solve parameter estimation for FitzHugh-Nagumo ODEs.
problem Estimating parameters of a nonlinear dynamical system from noisy time series data.
method Dense and convolutional neural networks for inverse problem solving.
result Deep neural networks accurately estimate FitzHugh-Nagumo model parameters from noisy data.
Deep neural networks can store and recall data efficiently.
problem Identifying computational mechanisms for memorization and retrieval of data.
method Training overparameterized autoencoders and sequence encoders using standard optimization methods.
result Overparameterized autoencoders and sequence encoders store and recall data efficiently as attractors.
Complex-valued neural networks improve seismic data analysis by preserving phase information.
problem Low-frequency aliasing in seismic data due to discarded phase information.
method Developed complex-valued deep convolutional networks to leverage phase information in deterministic physical data.
result Complex-valued networks outperform real-valued networks in training and inference from deterministic physical data.
Deep equilibrium models estimate latent variables from data.
problem Estimating latent variables from data.
method Generalized exponential family models, deep equilibrium networks.
result Deep equilibrium models solve MAP estimates for latent and transformation parameters.
New framework analyzes deep learning optimization with finite width networks, revealing generalization gaps and excess risks.
problem Analyzing generalization error of deep learning with finite width networks.
method Formulating neural network training as transportation map estimation and analyzing via infinite dimensional Langevin dynamics.
result Achieves fast learning rate and minimax optimal rates for classification and regression problems.
Ensembled neural networks improve MRI image quality.
problem Accelerated parallel MR imaging with high SSIM scores.
method Ensembled Σ-net combining parallel coil and sensitivity networks, trained with supervised and semi-supervised methods. result Ensembling models achieves visually sharp and textured images with robust SSIM scores.
FML uses neural networks to model unknown systems accurately.
problem Modeling unknown dynamical systems with incomplete data.
method Flow map learning (FML) combined with deep neural networks.
result Accurate predictive models for partially observed systems.
NKN deep neural network learns governing equations and classifies images.
problem Learning governing equations and classifying images with deep neural networks.
method Nonlocal kernel network (NKN) that is resolution independent, deep, and handles various tasks.
result NKN outperforms baseline methods in learning governing equations and image classification tasks.
We study the complexity of functions computable by deep feedforward neural networks with piecewise linear activations in terms of the symmetries and the number of linear regions that they have. Deep networks are able to sequentially map portions of each layer's input-space to the same output. In this way, deep models c…
Novel neural operator predicts complex spatiotemporal dynamics from partial observations.
problem Capturing complex operator dynamics in infinite-dimensional function spaces.
method Integrates Koopman operator theory with deep neural networks to approximate nonlinear operators between Banach spaces.
result BNO achieves robust zero-shot super-resolution in unsteady flow prediction and outperforms conventional methods.
XRAI improves saliency maps for deep neural networks.
problem Improving understanding of deep neural networks through better saliency methods.
method XRAI is a region-based attribution method built upon integrated gradients, with new evaluation methods and sanity checks.
result XRAI produces better results than other saliency methods for common models and the ImageNet dataset.
Deep Relevance Regularization improves neural network performance in tumor typing.
problem Confounding factors hinder neural network performance in multi-laboratory imaging mass spectrometry data.
method Introduces Deep Relevance Regularization to restrict neural network focus.
result Deep Relevance Regularization robustifies neural networks and improves interpretability.
The paper extends lossy coding to nonlinear latent representations.
problem Learning finite-dimensional coding schemes with nonlinear reconstruction maps.
method Generalizes Maurer--Pontil framework to nonlinear maps, connects to generative modeling, and provides generalization bounds.
result Established a connection to approximate generative modeling and presented generalization bounds.
Deep networks partition input space into regions with complex affine mappings.
problem Understanding the geometry and complexity of deep neural networks.
method Analyzed deep networks as max-affine spline operators and power diagrams.
result Composition of deep networks results in a progressively subdivided power diagram.
Develops methods to show model confidence and feature importance in medical imaging.
problem Ensuring safety and understanding model confidence in medical applications.
method Creates a pipeline to visualize uncertainty and saliency maps for deep neural networks.
result Demonstrates how deep neural networks can be made more transparent and safe for medical applications.
Deep learning predicts chess player attention using eye-tracking and game data.
problem Predicting chess player attention during game.
method Convolutional neural networks with skip-layer architecture and autoencoder.
result Deep features from natural images help in predicting saliency maps for chess.