Papers Model Optimization
“Model Optimization” 태그가 달린 논문 380편 · 필터 해제
Making Models Shallow Again: Jointly Learning to Reduce Non-Linearity and Depth for Latency-Efficient Private Inference
Large number of ReLU and MAC operations of Deep neural networks make them ill-suited for latency and compute-efficient private inference. In this paper, we present a model optimization method that allows a model to learn…
Model OptimizationSensitivityAI-coherent data-driven forecasting model for a combined cycle power plant
This study investigates the transformation of energy models to align with machine learning requirements as a promising tool for optimizing the operation of combined cycle power plants (CCPPs). By modeling energy producti…
Decision MakingModel OptimizationTransfer-Once-For-All: AI Model Optimization for Edge
Weight-sharing neural architecture search aims to optimize a configurable neural network model (supernet) for a variety of deployment scenarios across many devices with different resource constraints. Existing approaches…
AllModel OptimizationNeural Architecture SearchEnergy-efficient Task Adaptation for NLP Edge Inference Leveraging Heterogeneous Memory Architectures
Executing machine learning inference tasks on resource-constrained edge devices requires careful hardware-software co-design optimizations. Recent examples have shown how transformer-based deep neural network models such…
Data CompressionModel OptimizationMulti-modal reward for visual relationships-based image captioning
Deep neural networks have achieved promising results in automatic image captioning due to their effective representation learning and context-based content generation capabilities. As a prominent type of deep features us…
Caption GenerationDeep Reinforcement LearningImage CaptioningModel Optimization+3Whole-slide-imaging Cancer Metastases Detection and Localization with Limited Tumorous Data
Recently, various deep learning methods have shown significant successes in medical image analysis, especially in the detection of cancer metastases in hematoxylin and eosin (H&E) stained whole-slide images (WSIs). Howev…
Knowledge DistillationMedical Image AnalysisModel Optimizationwhole slide imagesOptimization Design for Federated Learning in Heterogeneous 6G Networks
With the rapid advancement of 5G networks, billions of smart Internet of Things (IoT) devices along with an enormous amount of data are generated at the network edge. While still at an early age, it is expected that the …
Federated LearningManagementModel OptimizationPrivacy PreservingR2 Loss: Range Restriction Loss for Model Compression and Quantization
Model quantization and compression is widely used techniques to reduce usage of computing resource at inference time. While state-of-the-art works have been achieved reasonable accuracy with higher bit such as 4bit or 8b…
ClassificationModel CompressionModel OptimizationQuantizationMOISST: Multimodal Optimization of Implicit Scene for SpatioTemporal calibration
With the recent advances in autonomous driving and the decreasing cost of LiDARs, the use of multimodal sensor systems is on the rise. However, in order to make use of the information provided by a variety of complimenta…
Autonomous DrivingModel OptimizationNeRFDISCO: Distributed Inference with Sparse Communications
Deep neural networks (DNNs) have great potential to solve many real-world problems, but they usually require an extensive amount of computation and memory. It is of great difficulty to deploy a large DNN model to a singl…
Distributed Computingimage-classificationImage ClassificationImage Super-Resolution+5Online Arbitrary Shaped Clustering through Correlated Gaussian Functions
There is no convincing evidence that backpropagation is a biologically plausible mechanism, and further studies of alternative learning methods are needed. A novel online clustering algorithm is presented that can produc…
ClusteringModel OptimizationOnline ClusteringConfidence-based Reliable Learning under Dual Noises
Deep neural networks (DNNs) have achieved remarkable success in a variety of computer vision tasks, where massive labeled images are routinely required for model optimization. Yet, the data collected from the open world …
Model OptimizationMultimodal Recommender Systems: A Survey
The recommender system (RS) has been an integral toolkit of online services. They are equipped with various deep learning techniques to model user preference based on identifier and attribute information. With the emerge…
AttributeModel OptimizationRecommendation SystemsSurveyCurriculum Graph Machine Learning: A Survey
Graph machine learning has been extensively studied in both academia and industry. However, in the literature, most existing graph machine learning models are designed to conduct training with data samples in a random or…
Model OptimizationSurveyInvestigating Pulse-Echo Sound Speed Estimation in Breast Ultrasound with Deep Learning
Ultrasound is an adjunct tool to mammography that can quickly and safely aid physicians with diagnosing breast abnormalities. Clinical ultrasound often assumes a constant sound speed to form B-mode images for diagnosis. …
Image ReconstructionModel OptimizationGradient Estimation for Unseen Domain Risk Minimization with Pre-Trained Models
Domain generalization aims to build generalized models that perform well on unseen domains when only source domains are available for model optimization. Recent studies have shown that large-scale pre-trained models can …
Domain GeneralizationModel OptimizationExecution-based Code Generation using Deep Reinforcement Learning
The utilization of programming language (PL) models, pre-trained on large-scale code corpora, as a means of automating software engineering processes has demonstrated considerable potential in streamlining various code g…
Code CompletionCode GenerationCode TranslationDeep Reinforcement Learning+6Validation of machine learning based scenario generators
Machine learning (ML) methods are becoming increasingly important in the design economic scenario generators for internal models. Validation of data-driven models differs from classical theory-based models. We discuss tw…
MemorizationModel OptimizationALIM: Adjusting Label Importance Mechanism for Noisy Partial Label Learning
Noisy partial label learning (noisy PLL) is an important branch of weakly supervised learning. Unlike PLL where the ground-truth label must conceal in the candidate label set, noisy PLL relaxes this constraint and allows…
Model OptimizationPartial Label LearningWeakly-supervised LearningVictoria Amazonica Optimization (VAO): An Algorithm Inspired by the Giant Water Lily Plant
The Victoria Amazonica plant, often known as the Giant Water Lily, has the largest floating spherical leaf in the world, with a maximum leaf diameter of 3 meters. It spreads its leaves by the force of its spines and crea…
feature selectionImage SegmentationModel OptimizationQuantization+3