Real-time uncertainty estimation for computer vision tasks.
problem Real-time inference of uncertainty in deep learning models.
method Uncertainty-Aware Distribution Distillation method for fast inference.
result Significantly reduced inference time with improved uncertainty and predictive performance.
Monte Carlo algorithms simulate some prescribed number of samples, taking some random real time to complete the computations necessary. This work considers the converse: to impose a real-time budget on the computation, which results in the number of samples simulated being random. To complicate matters, the real time t…
We create real-time geodesic rendering for non-isotropic geometries.
problem Challenging visualization of non-isotropic geometries.
method Novel methods for real-time native geodesic rendering.
result Methods can be applied to visualization, machine learning, and video games.
Robots detect and recognize objects in real-time for better manipulation.
problem Real-time object detection and recognition for humanoid robots.
method Modified YOLOv3 algorithm, quantization, and re-arrangement of layers for low-compute NAO robots.
result Robots can perform real-time detection, recognition, and localization of objects.
Optimized neural networks for Edge TPU achieve high accuracy in real-time image classification.
problem Designing neural networks for hardware accelerators to achieve optimal performance.
method Hardware-aware neural architecture search and model customization for Edge TPU.
result Improved accuracy-latency tradeoff on Pixel 4's Edge TPU compared to existing models.
Markov Decision Processes (MDPs), the mathematical framework underlying most algorithms in Reinforcement Learning (RL), are often used in a way that wrongfully assumes that the state of an agent's environment does not change during action selection. As RL systems based on MDPs begin to find application in real-world sa…
Deep learning speeds up real-time emission monitoring.
problem Real-time greenhouse gas emission monitoring under transient conditions.
method Bayesian inference with deep learning surrogate of CFD outputs.
result Near-real-time predictions with orders-of-magnitude faster runtimes.
SkyGP improves Gaussian process scalability for real-time learning.
problem Scalability issues with exact Gaussian processes for streaming data.
method Streaming kernel-induced progressively generated Gaussian process experts (SkyGP).
result SkyGP maintains performance guarantees while improving scalability.
The progress of deep convolutional neural networks has been successfully exploited in various real-time computer vision tasks such as image classification and segmentation. Owing to the development of computational units, availability of digital datasets, and improved performance of deep learning models, fully automati…
A new RL method handles uncertainty and constraints in real-time optimization.
problem Real-time optimization under process uncertainty and constraints.
method Chance-constrained reinforcement learning to handle probabilistic state constraints.
result Satisfies process constraints with high probability in real-time.
New algorithm detects and adapts to changes in real-time data streams.
problem Adapting to fast-changing data in real-time systems.
method Concept drift detection followed by prototype-based adaptation.
result Stable and quick adjustments during model adaptation.
FedCM measures contributions in real-time for federated learning.
problem Fairly allocating contributions in federated learning systems.
method FedCM calculates impact based on current and previous rounds with attention aggregation.
result FedCM is more sensitive to data quality and quantity in real-time.
New algorithm optimizes auction prices in real-time.
problem Maximizing revenue in online auctions with high frequency data.
method First real-time algorithm for online learning of monopoly prices.
result Achieves constant time and memory complexity for updates.
A new method infers neural trajectories in real-time, improving experimental design.
problem Real-time inference of neural trajectories for immediate feedback.
method Exponential family variational Kalman filter (eVKF) for online learning.
result eVKF achieves competitive performance on synthetic and real-world data.
Coherent Multiplex analyzes real-time wavelet coherence among multiple signals.
problem Identifying and visualizing coherence among multiple time series.
method Fast spectral similarity based on cosine similarity metrics of Fourier-transformed signals and sparse time-frequency wavelet coherence.
result Scalable real-time system for low-latency inference and monitoring of inter-signal relationships.
FPGA-based multi-layer equalizer adapts to changing channels.
problem Real-time adaptation to time-varying channel impairments.
method Multi-layer machine learning on FPGA with on-chip gradient backpropagation training.
result Real-time adaptation to changing channel conditions achieved.
Edge computing tackles dynamic data in IIoT with incremental learning.
problem Latency and bandwidth limitations in IoT devices.
method Incremental learning applied to edge-computing systems for continual learning.
result Reduces catastrophic forgetting and provides efficient real-time quality control.
Study uses DNNs for real-time EM inversion, highlighting model errors and proposing solutions.
problem Model errors in DNNs affect real-time geosteering decisions in EM measurements.
method Bayesian ensemble smoothing with DNNs for thousands of model evaluations, identifying multimodality.
result Model errors can lead to biased estimates, necessitating error reduction techniques.
Noise2Filter improves 3D tomography reconstruction efficiency and accuracy.
problem Efficiently reconstructing 3D tomographic images in real-time with limited data.
method Self-supervised learning and a learned filter method.
result Noise2Filter achieves real-time reconstruction with limited loss of accuracy.
There has been an explosion of interest in functional Magnetic Resonance Imaging (MRI) during the past two decades. Naturally, this has been accompanied by many major advances in the understanding of the human connectome. These advances have served to pose novel challenges as well as open new avenues for research. One …
We propose a real-time context-aware learning system along with the architecture that runs on the mobile devices, provide services to the user and manage the IoT devices. In this system, an application running on mobile devices collected data from the sensors, learned about the user-defined context, made predictions in…
ReRe detects anomalies in real-time for time series data.
problem Real-time anomaly detection for time series data requires human intervention or domain knowledge and high computation complexity.
method ReRe uses two lightweight LSTM models to predict and determine anomalies based on historical data and adaptive thresholds.
result ReRe detects anomalies in real-time without requiring human intervention or domain knowledge.
Real-time detection of out-of-distribution data in CPS control systems.
problem Detecting out-of-distribution data in CPS control systems for safety.
method Inductive conformal prediction and anomaly detection using variational autoencoders and deep support vector data description.
result Efficient real-time detection with low false alarm rates and comparable execution time.
Dividing local Gaussian processes improve real-time prediction efficiency.
problem Efficient online prediction for large data sets.
method Iterative data-driven division of input space for sublinear computational complexity.
result Sublinear computational complexity in real-time prediction.
This paper optimizes object tracking on edge devices with small matrices.
problem Efficiently tracking objects in video sequences on edge devices with small matrices.
method Parallelized a Simple Online and Real-time Tracking (SORT) application on shared-memory multicores.
result Throughput-based parallelization technique outperforms multi-threading for small matrices.
In online advertising, display ads are increasingly being placed based on real-time auctions where the advertiser who wins gets to serve the ad. This is called real-time bidding (RTB). In RTB, auctions have very tight time constraints on the order of 100ms. Therefore mechanisms for bidding intelligently such as clickth…
Develops an efficient method for real-time data analysis and visualization.
problem Challenges of analyzing high-dimensional data.
method Incremental non-linear manifold approximation using GMRA framework.
result Accurately represents non-linear manifolds with small initial samples.
A real-time federated neural architecture search approach reduces costs and improves performance.
problem High communication and computational demands in federated learning for large models.
method Evolutionary approach with double-sampling technique to optimize model performance and reduce costs.
result Effective real-time federated neural architecture search for deep models on edge devices.
Paper proposes an online anomaly detection method for real-time systems.
problem Rare events endanger profitability, safety, and environmental aspects.
method Online inverse cumulative distribution-based approach with dynamic process limits.
result Eliminates common problems of offline anomaly detectors and provides low-latency detection.
A new method for real-time CCA on streaming data.
problem Finding correlated features in online data streams.
method Sliding Window Informative Canonical Correlation Analysis (SWICCA) using streaming PCA.
result SWICCA provides real-time CCA components in high dimensions with theoretical guarantees.
Paper presents attacks on real-time object detection systems.
problem Adversarial attacks on real-time object detection systems.
method Three targeted adversarial Objectness Gradient attacks (TOG).
result Adversarial attacks can cause object-vanishing, object-fabrication, and object-mislabeling.
IVMs help identify dangerous traffic conditions in real-time.
problem Real-time crash risk analysis in urban traffic systems.
method Import Vector Machines (IVMs) applied to historical crash and traffic data.
result IVMs successfully identify dangerous traffic conditions with computational advantage.
DeepONet accelerates nuclear DT inference with high accuracy and efficiency.
problem Real-time prediction and model evaluation in nuclear systems.
method Deep Neural Operator (DeepONet) for surrogate modeling.
result DeepONet outperforms traditional ML methods in accuracy and speed.
This survey analyzes deep learning methods for real-time semantic image segmentation.
problem Efficient and accurate semantic image segmentation for real-time applications.
method Comprehensive analysis of deep learning architectures and techniques for fast inference.
result Best-performing architectures and techniques for real-time semantic image segmentation.
TAKDE optimizes kernel density estimation for real-time dynamic processes.
problem Real-time density estimation in applications like computer vision and signal processing.
method Derives asymptotic mean integrated squared error (AMISE) upper bound for 'sliding window' kernel density estimator and proposes TAKDE as a novel, theoretically optimal estimator.
result TAKDE outperforms other dynamic density estimators in terms of test log-likelihood and runtime.
Neuromorphic Computing is a nascent research field in which models and devices are designed to process information by emulating biological neural systems. Thanks to their superior energy efficiency, analog neuromorphic systems are highly promising for embedded, wearable, and implantable systems. However, optimizing neu…
Unified framework for accelerating DNNs on resource-limited platforms.
problem Accelerating DNN execution on resource-limited platforms.
method Block-based pruning framework with reweighted regularization.
result First universal framework for both CNNs and RNNs with real-time acceleration and no accuracy compromise.
We introduce a methodology for efficiently computing a lower bound to empowerment, allowing it to be used as an unsupervised cost function for policy learning in real-time control. Empowerment, being the channel capacity between actions and states, maximises the influence of an agent on its near future. It has been sho…
This thesis builds a real-time VaR calculation workflow for crypto derivatives.
problem Managing risk in volatile cryptocurrency markets.
method Applied EMWA, GARCH, and HAR models to forecast volatility; used delta-gamma-theta approach and Cornish-Fisher expansion.
result Real-time VaR estimates with millisecond calculation latencies.
This paper proposes a real-time embedded fall detection system using a DVS(Dynamic Vision Sensor) that has never been used for traditional fall detection, a dataset for fall detection using that, and a DVS-TN(DVS-Temporal Network). The first contribution is building a DVS Falls Dataset, which made our network to recogn…
Interleaved RNNs detect fraud without costly features.
problem Real-time fraud detection in payment cards.
method Use interleaved sequence RNNs for fraud detection.
result Interleaved RNNs outperform state-of-the-art models in fraud detection.
Paper introduces online tensor inference for real-time data analysis.
problem Real-time processing of high-dimensional tensor data.
method Stochastic Gradient Descent (SGD) for efficient online inference.
result Establishes non-asymptotic convergence and optimal estimation error rate.
New method allows real-time audio synthesis using non-causal convolutions.
problem Real-time audio synthesis limitations due to offline model constraints.
method Post-training reconfiguration of non-causal models for real-time buffer-based processing.
result Non-causal streaming models can be transformed from offline-trained models without quality loss.
Low precision networks in the reinforcement learning (RL) setting are relatively unexplored because of the limitations of binary activations for function approximation. Here, in the discrete action ATARI domain, we demonstrate, for the first time, that low precision policy distillation from a high precision network pro…
Efficient superpixel method for real-time segmentation.
problem Real-time superpixel generation for computer vision tasks.
method Two-stage graph-based framework with Deep Affinity Learning and Hierarchical Entropy Rate Segmentation.
result HERS produces superpixels in near real-time.
Real-time personalization for HAR models learns from new users without prior data.
problem Poor performance of HAR models on new users without labeled data.
method Incremental online domain adaptation using batch normalization.
result Personalized HAR models adapt to new users in real-time.
A framework for real-time edge intelligence using federated meta-learning.
problem Real-time intelligent decisions at edge devices with limited resources and data.
method Federated meta-learning approach for rapid adaptation of learned models.
result Effective framework demonstrated on various datasets.
AdaKoop efficiently models nonlinear dynamics from nonstationary data streams.
problem Capturing nonlinear dynamics in nonstationary data streams with computational efficiency.
method Koopman operator theory and probabilistic framework for streaming data.
result AdaKoop outperforms state-of-the-art methods in real-time forecasting accuracy and efficiency.