Papers Dynamic neural networks
“Dynamic neural networks” 태그가 달린 논문 36편 · 필터 해제
A Survey on Dynamic Neural Networks: from Computer Vision to Multi-modal Sensor Fusion
Model compression is essential in the deployment of large Computer Vision models on embedded devices. However, static optimization techniques (e.g. pruning, quantization, etc.) neglect the fact that different inputs have…
Dynamic neural networksModel CompressionSensor FusionParametric Taylor series based latent dynamics identification neural networks
Numerical solving parameterised partial differential equations (P-PDEs) is highly practical yet computationally expensive, driving the development of reduced-order models (ROMs). Recently, methods that combine latent spa…
Dynamic neural networksAn Introduction to Cognidynamics
This paper gives an introduction to \textit{Cognidynamics}, that is to the dynamics of cognitive systems driven by optimal objectives imposed over time when they interact either with a defined virtual or with a real-worl…
Dynamic neural networksDyFADet: Dynamic Feature Aggregation for Temporal Action Detection
Recent proposed neural network-based Temporal Action Detection (TAD) models are inherently limited to extracting the discriminative representations and modeling action instances with various lengths from complex scenes b…
Action DetectionDynamic neural networksTemporal Action LocalizationNeuroevolving Electronic Dynamical Networks
Neuroevolution is a powerful method of applying an evolutionary algorithm to refine the performance of artificial neural networks through natural selection; however, the fitness evaluation of these networks can be time-c…
Dynamic neural networksDynamic DNNs and Runtime Management for Efficient Inference on Mobile/Embedded Devices
Deep neural network (DNN) inference is increasingly being executed on mobile and embedded platforms due to several key advantages in latency, privacy and always-on availability. However, due to limited computing resource…
Dynamic neural networksGPUManagementModel CompressionSubnetwork-to-go: Elastic Neural Network with Dynamic Training and Customizable Inference
Deploying neural networks to different devices or platforms is in general challenging, especially when the model size is large or model complexity is high. Although there exist ways for model pruning or distillation, it …
Dynamic neural networksMusic Source SeparationJointly-Learned Exit and Inference for a Dynamic Neural Network : JEI-DNN
Large pretrained models, coupled with fine-tuning, are slowly becoming established as the dominant architecture in machine learning. Even though these models offer impressive performance, their practical application is o…
Dynamic neural networksDynamic Neural Network is All You Need: Understanding the Robustness of Dynamic Mechanisms in Neural Networks
Deep Neural Networks (DNNs) have been used to solve different day-to-day problems. Recently, DNNs have been deployed in real-time systems, and lowering the energy consumption and response time has become the need of the …
AllDynamic neural networksLong-Distance Gesture Recognition using Dynamic Neural Networks
Gestures form an important medium of communication between humans and machines. An overwhelming majority of existing gesture recognition methods are tailored to a scenario where humans and machines are located very close…
Dynamic neural networksGesture RecognitionMonadic Deep Learning
The Java and Scala community has built a very successful big data ecosystem. However, most of neural networks running on it are modeled in dynamically typed programming languages. These dynamically typed deep learning fr…
Deep LearningDynamic neural networksDyCL: Dynamic Neural Network Compilation Via Program Rewriting and Graph Optimization
DL compiler's primary function is to translate DNN programs written in high-level DL frameworks such as PyTorch and TensorFlow into portable executables. These executables can then be flexibly executed by the deployed ho…
Dynamic neural networksStock Price Prediction using Dynamic Neural Networks
This paper will analyze and implement a time series dynamic neural network to predict daily closing stock prices. Neural networks possess unsurpassed abilities in identifying underlying patterns in chaotic, non-linear, a…
Dynamic neural networksPredictionregressionStock Price Prediction+1Evolving Artificial Neural Networks To Imitate Human Behaviour In Shinobi III : Return of the Ninja Master
Our society is increasingly fond of computational tools. This phenomenon has greatly increased over the past decade following, among other factors, the emergence of a new Artificial Intelligence paradigm. Specifically, t…
Dynamic neural networksEvolutionary AlgorithmsGradMDM: Adversarial Attack on Dynamic Networks
Dynamic neural networks can greatly reduce computation redundancy without compromising accuracy by adapting their structures based on the input. In this paper, we explore the robustness of dynamic neural networks against…
Adversarial AttackDynamic neural networksFixing Overconfidence in Dynamic Neural Networks
Dynamic neural networks are a recent technique that promises a remedy for the increasing size of modern deep learning models by dynamically adapting their computational cost to the difficulty of the inputs. In this way, …
Decision MakingDeep LearningDynamic neural networksUncertainty QuantificationED-Batch: Efficient Automatic Batching of Dynamic Neural Networks via Learned Finite State Machines
Batching has a fundamental influence on the efficiency of deep neural network (DNN) execution. However, for dynamic DNNs, efficient batching is particularly challenging as the dataflow graph varies per input instance. As…
CPUDynamic neural networksGPUThe Dark Side of Dynamic Routing Neural Networks: Towards Efficiency Backdoor Injection
Recent advancements in deploying deep neural networks (DNNs) on resource-constrained devices have generated interest in input-adaptive dynamic neural networks (DyNNs). DyNNs offer more efficient inferences and enable…
Adversarial AttackDynamic neural networksHADAS: Hardware-Aware Dynamic Neural Architecture Search for Edge Performance Scaling
Dynamic neural networks (DyNNs) have become viable techniques to enable intelligence on resource-constrained edge devices while maintaining computational efficiency. In many cases, the implementation of DyNNs can be sub-…
Computational EfficiencyDynamic neural networksEdge-computingNeural Architecture SearchBoosted Dynamic Neural Networks
Early-exiting dynamic neural networks (EDNN), as one type of dynamic neural networks, has been widely studied recently. A typical EDNN has multiple prediction heads at different layers of the network backbone. During inf…
Dynamic neural networksPrediction