Papers Model Optimization
“Model Optimization” 태그가 달린 논문 380편 · 필터 해제
Unleashing the Power of Contrastive Self-Supervised Visual Models via Contrast-Regularized Fine-Tuning
Contrastive self-supervised learning (CSL) has attracted increasing attention for model pre-training via unlabeled data. The resulted CSL models provide instance-discriminative visual features that are uniformly scattere…
Contrastive Learningimage-classificationImage ClassificationModel Optimization+3Loss Function Discovery for Object Detection via Convergence-Simulation Driven Search
Designing proper loss functions for vision tasks has been a long-standing research direction to advance the capability of existing models. For object detection, the well-established classification and regression loss fun…
image-classificationModel Optimizationobject-detectionObject DetectionGraph Embedding for Recommendation against Attribute Inference Attacks
In recent years, recommender systems play a pivotal role in helping users identify the most suitable items that satisfy personal preferences. As user-item interactions can be naturally modelled as graph-structured data, …
AttributeGraph EmbeddingModel OptimizationRecommendation SystemsDeeplite Neutrino: An End-to-End Framework for Constrained Deep Learning Model Optimization
Designing deep learning-based solutions is becoming a race for training deeper models with a greater number of layers. While a large-size deeper model could provide competitive accuracy, it creates a lot of logistical ch…
Deep LearningModel OptimizationA stochastic geospatial epidemic model and simulation using an event modulated Gillespie algorithm
We developed a model and a software package for stochastic simulations of transmission of COVID-19 and other similar infectious diseases, that takes into account contact network structures and geographical distribution o…
Model OptimizationMultitask Learning for Emotion and Personality Detection
In recent years, deep learning-based automated personality trait detection has received a lot of attention, especially now, due to the massive digital footprints of an individual. Moreover, many researchers have demonstr…
Language ModelingLanguage ModellingModel OptimizationSwitchable K-Class Hyperplanes for Noise-Robust Representation Learning
Optimizing the K-class hyperplanes in the latent space has become the standard paradigm for efficient representation learning. However, it's almost impossible to find an optimal K-class hyperplane to accurately descr…
Model OptimizationRepresentation Learningvalid3D Building Reconstruction From Monocular Remote Sensing Images
3D building reconstruction from monocular remote sensing imagery is an important research problem and an economic solution to large-scale city modeling, compared with reconstruction from LiDAR data and multi-view ima…
3D ReconstructionModel OptimizationTemporal Cue Guided Video Highlight Detection With Low-Rank Audio-Visual Fusion
Video highlight detection plays an increasingly important role in social media content filtering, however, it remains highly challenging to develop automated video highlight detection methods because of the lack of t…
Highlight DetectionModel OptimizationOnline Pseudo Label Generation by Hierarchical Cluster Dynamics for Adaptive Person Re-Identification
Adaptive person re-identification (adaptive ReID) targets at transferring learned knowledge from the labeled source domain to the unlabeled target domain. Pseudo-label-based methods that alternatively generate pseudo…
Model OptimizationPerson Re-IdentificationPseudo LabelModel Optimization for Deep Space Exploration via Simulators and Deep Learning
Machine learning, and eventually true artificial intelligence techniques, are extremely important advancements in astrophysics and astronomy. We explore the application of deep learning using neural networks in order to …
AstronomyDeep LearningModel OptimizationSpatio-temporal Multi-task Learning for Cardiac MRI Left Ventricle Quantification
Quantitative assessment of cardiac left ventricle (LV) morphology is essential to assess cardiac function and improve the diagnosis of different cardiovascular diseases. In current clinical practice, LV quantification de…
Model OptimizationMulti-Task LearningPersonalized Federated Learning with First Order Model Optimization
While federated learning traditionally aims to train a single global model across decentralized local datasets, one model may not always be ideal for all participating clients. Here we propose an alternative, where each …
Federated LearningmodelModel OptimizationPersonalized Federated LearningUntangling tradeoffs between recurrence and self-attention in artificial neural networks
Attention and self-attention mechanisms, are now central to state-of-the-art deep learning on sequential tasks. However, most recent progress hinges on heuristic approaches with limited understanding of attention's role …
Model OptimizationRank Position Forecasting in Car Racing
Forecasting is challenging since uncertainty resulted from exogenous factors exists. This work investigates the rank position forecasting problem in car racing, which predicts the rank positions at the future laps for ca…
Car RacingDecoderModel OptimizationPositionDataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics
Large datasets have become commonplace in NLP research. However, the increased emphasis on data quantity has made it challenging to assess the quality of data. We introduce Data Maps---a model-based tool to characterize …
Model OptimizationOut-of-Distribution GeneralizationIterative Surrogate Model Optimization (ISMO): An active learning algorithm for PDE constrained optimization with deep neural networks
We present a novel active learning algorithm, termed as iterative surrogate model optimization (ISMO), for robust and efficient numerical approximation of PDE constrained optimization problems. This algorithm is based on…
Active LearningModel OptimizationPersonalized Federated Learning with Moreau Envelopes
Federated learning (FL) is a decentralized and privacy-preserving machine learning technique in which a group of clients collaborate with a server to learn a global model without sharing clients' data. One challenge asso…
DiversityFederated LearningMeta-LearningModel Optimization+3Untangling tradeoffs between recurrence and self-attention in neural networks
Attention and self-attention mechanisms, are now central to state-of-the-art deep learning on sequential tasks. However, most recent progress hinges on heuristic approaches with limited understanding of attention's role …
Model OptimizationDo the Machine Learning Models on a Crowd Sourced Platform Exhibit Bias? An Empirical Study on Model Fairness
Machine learning models are increasingly being used in important decision-making software such as approving bank loans, recommending criminal sentencing, hiring employees, and so on. It is important to ensure the fairnes…
AttributeBIG-bench Machine LearningDecision MakingFairness+1