Papers Model Optimization
“Model Optimization” 태그가 달린 논문 380편 · 필터 해제
PIXOR: Real-time 3D Object Detection from Point Clouds
We address the problem of real-time 3D object detection from point clouds in the context of autonomous driving. Computation speed is critical as detection is a necessary component for safety. Existing approaches are, how…
3D Object DetectionAutonomous DrivingBirds Eye View Object DetectionModel Optimization+4Dependency or Span, End-to-End Uniform Semantic Role Labeling
Semantic role labeling (SRL) aims to discover the predicateargument structure of a sentence. End-to-end SRL without syntactic input has received great attention. However, most of them focus on either span-based or depend…
Model OptimizationSemantic Role LabelingSentenceDifferentiable Satisfiability and Differentiable Answer Set Programming for Sampling-Based Multi-Model Optimization
We propose Differentiable Satisfiability and Differentiable Answer Set Programming (Differentiable SAT/ASP) for multi-model optimization. Models (answer sets or satisfying truth assignments) are sampled using a novel SAT…
Model OptimizationDynamic Transfer Learning for Named Entity Recognition
State-of-the-art named entity recognition (NER) systems have been improving continuously using neural architectures over the past several years. However, many tasks including NER require large sets of annotated data to a…
Model Optimizationnamed-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)+2A Bridging Framework for Model Optimization and Deep Propagation
Optimizing task-related mathematical model is one of the most fundamental methodologies in statistic and learning areas. However, generally designed schematic iterations may hard to investigate complex data distributions…
Model OptimizationMeta-Learning with Individualized Feature Space for Few-Shot Classification
Meta-learning provides a promising learning framework to address few-shot classification tasks. In existing meta-learning methods, the meta-learner is designed to learn about model optimization, parameter initialization,…
ClassificationMeta-LearningModel OptimizationProximal Policy Optimization and its Dynamic Version for Sequence Generation
In sequence generation task, many works use policy gradient for model optimization to tackle the intractable backpropagation issue when maximizing the non-differentiable evaluation metrics or fooling the discriminator in…
ChatbotModel Optimizationreinforcement-learningReinforcement Learning+1Highly Efficient 8-bit Low Precision Inference of Convolutional Neural Networks with IntelCaffe
High throughput and low latency inference of deep neural networks are critical for the deployment of deep learning applications. This paper presents the efficient inference techniques of IntelCaffe, the first Intel optim…
Deep LearningModel OptimizationBelief Propagation Min-Sum Algorithm for Generalized Min-Cost Network Flow
Belief Propagation algorithms are instruments used broadly to solve graphical model optimization and statistical inference problems. In the general case of a loopy Graphical Model, Belief Propagation is a heuristic which…
Model OptimizationEnd-to-End Waveform Utterance Enhancement for Direct Evaluation Metrics Optimization by Fully Convolutional Neural Networks
Speech enhancement model is used to map a noisy speech to a clean speech. In the training stage, an objective function is often adopted to optimize the model parameters. However, in most studies, there is an inconsistenc…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Model OptimizationSpeech Enhancement+2Variational Inference via Transformations on Distributions
Variational inference methods often focus on the problem of efficient model optimization, with little emphasis on the choice of the approximating posterior. In this paper, we review and implement the various methods that…
Model OptimizationVariational InferenceLifelong Metric Learning
The state-of-the-art online learning approaches are only capable of learning the metric for predefined tasks. In this paper, we consider lifelong learning problem to mimic "human learning", i.e., endowing a new capabilit…
Lifelong learningMetric LearningModel OptimizationProbabilistic Vehicle Trajectory Prediction over Occupancy Grid Map via Recurrent Neural Network
In this paper, we propose an efficient vehicle trajectory prediction framework based on recurrent neural network. Basically, the characteristic of the vehicle's trajectory is different from that of regular moving objects…
Model OptimizationPredictionTrajectory PredictionGate-Variants of Gated Recurrent Unit (GRU) Neural Networks
The paper evaluates three variants of the Gated Recurrent Unit (GRU) in recurrent neural networks (RNN) by reducing parameters in the update and reset gates. We evaluate the three variant GRU models on MNIST and IMDB dat…
Model OptimizationCross-Domain Visual Matching via Generalized Similarity Measure and Feature Learning
Cross-domain visual data matching is one of the fundamental problems in many real-world vision tasks, e.g., matching persons across ID photos and surveillance videos. Conventional approaches to this problem usually invol…
Face VerificationModel OptimizationPerson Re-IdentificationRepresentation LearningHuman judgment vs. theoretical models for the management of ecological resources
Despite major advances in quantitative approaches to natural resource management, there has been resistance to using these tools in the actual practice of managing ecological populations. Given a managed system and a set…
Decision MakingManagementModel OptimizationLearning Contour-Fragment-based Shape Model with And-Or Tree Representation
This paper proposes a simple yet effective method to learn the hierarchical object shape model consisting of local contour fragments, which represents a category of shapes in the form of an And-Or tree. This model extend…
ClusteringEdge DetectionModel OptimizationInference by Learning: Speeding-up Graphical Model Optimization via a Coarse-to-Fine Cascade of Pruning Classifiers
We propose a general and versatile framework that significantly speeds-up graphical model optimization while maintaining an excellent solution accuracy. The proposed approach, refereed as Inference by Learning or IbyL, r…
Model OptimizationSpeeding-up Graphical Model Optimization via a Coarse-to-fine Cascade of Pruning Classifiers
We propose a general and versatile framework that significantly speeds-up graphical model optimization while maintaining an excellent solution accuracy. The proposed approach relies on a multi-scale pruning scheme that i…
Model OptimizationExtending Universal Intelligence Models with Formal Notion of Representation
Solomonoff induction is known to be universal, but incomputable. Its approximations, namely, the Minimum Description (or Message) Length (MDL) principles, are adopted in practice in the efficient, but non-universal form.…
Model Optimization