[PDF] Top 20 Refining Generative Language Models using Discriminative Learning
Has 10000 "Refining Generative Language Models using Discriminative Learning" found on our website. Below are the top 20 most common "Refining Generative Language Models using Discriminative Learning".
Refining Generative Language Models using Discriminative Learning
... Our method bears some similarity to the recently developed Contrastive Estimation method (Smith and Eisner, 2004). Contrastive estimation (CE) was proposed as a means for training log-linear prob- abilistic ... See full document
8
Probabilistic Substrate Classification with Multispectral Acoustic Backscatter: A Comparison of Discriminative and Generative Models
... for discriminative substrate characterization, to support geological and biological habitat mapping in aquatic ...demonstrated using multispectral and monospectral acoustic backscatter from heterogeneous ... See full document
20
Integration of discriminative and generative models for activity recognition in smart homes
... In generative approaches, NB classifier is used for activity recognition, which assigns the label of activity class with the highest probability corresponding to the sequence of activated sensor values ...HHMM ... See full document
15
IRGAN: a minimax game for unifying generative and discriminative information retrieval models
... Retrieval Models. For unsupervised learning problem that estimates the data ...supervised learning problem that estimates the conditional ...standard learning solution ...retrieval ... See full document
11
Comparing Top Down and Bottom Up Neural Generative Dependency Models
... dependency models make good ...joint generative mod- els offer very sample-efficient estimates of condi- tional distributions (Yogatama et ...dependency models are less effective as language ... See full document
11
Adaptive Approach of Fault Prediction in Software Modules by using Discriminative and Generative Model of Machine Learning
... prediction models or techniques are very important in order to deliver efficient ...machine learning algorithms are used to predict three main prediction performance measures ...by using important ... See full document
6
Neural Net Models of Open domain Discourse Coherence
... ural language generation and understand- ing. Yet existing models of coherence focus on measuring individual aspects of coher- ence (lexical overlap, rhetorical structure, entity centering) in narrow ... See full document
12
Early Gains Matter: A Case for Preferring Generative over Discriminative Crowdsourcing Models
... crowdsourcing models that are data-aware, most model the data discriminatively (Carroll et ...work models the data generatively (Lam and Stork, 2005; Simpson and Roberts, In ...a generative ... See full document
10
Neural Syntactic Generative Models with Exact Marginalization
... our models primarily on the language models of Zaremba et ...(2014). Models are based on two- layer LSTMs with embedding and hidden state size 650 with dropout of ...all models weights ... See full document
11
Fast Syntactic Analysis for Statistical Language Modeling via Substructure Sharing and Uptraining
... both generative and discriminative LMs with long-span dependencies can be slow, for they often cannot work directly with lattices and require rescoring large N -best lists (Khudanpur and Wu, 2000; Collins ... See full document
9
Generative Models from the perspective of Continual Learning
... For Generative Replay, at the end of the task sequence, VAE decreases its performance in several classes when GAN does ...with Generative Replay, they benefit from their samples quality and their ...based ... See full document
19
Adapting Discriminative Reranking to Grounded Language Learning
... a generative model, discriminative reranking (Collins, 2000) could po- tentially improve its ...a discriminative classifier that uses global features of complete parses to identify correct ... See full document
10
Semi Supervised Structured Output Learning Based on a Hybrid Generative and Discriminative Approach
... 4 indicate that HySOL is rather robust with respect to the hyper-parameter since we can obtain fairly good performance without a prior distribution. 5.4 Comparison with Previous Top Systems With respect to the ... See full document
10
A Generative Parser with a Discriminative Recognition Algorithm
... Generative models defining joint distributions over parse trees and sentences are good theoretical models for interpreting natural language data, and appealing tools for tasks such as parsing, ... See full document
7
The generative learning and discriminative fitting of linear deformable models
... The results of experiments (a) to (f) using the first template with optimally initialised cor- respondences and hyperparameters are presented in Figure 3.11. Comparing the results for experiments (a) with (b), and ... See full document
189
Confidence Weighted Learning of Factored Discriminative Language Models
... for language modeling, where even human experts will argue about whether a given sentence is fluent or ...effective language models must be trained on large datasets, so the option of requiring ... See full document
6
Discriminative Language Models as a Tool for Machine Translation Error Analysis
... training discriminative LMs, we follow Roark et al. (2007) in using the structured perceptron as a simple and effective method for LM ...on-line learning method that examines one training instance ... See full document
9
Efficient Heuristics for Discriminative Structure Learning of Bayesian Network Classifiers
... in learning the discriminative structure of a gener- ative ...a generative model, even discriminatively structured, some aspect of the joint distribution p(C,X) is still being ...structured ... See full document
38
Statistical Ranking in Tactical Generation
... This paper describes the application of several dif- ferent statistical models for the task of realiza- tion ranking in tactical generation, i.e. the problem of choosing among multiple paraphrases that are ... See full document
9
Improving Web Learning through model Optimization using Bootstrap for a Tour-Guide Robot
... Web Learning process by refining page selection using Bootstrap technique to evaluate, refine and compare models based on patterns implemented for binary ... See full document
7
Related subjects