[PDF] Top 20 MEAL: Multi-Model Ensemble via Adversarial Learning
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MEAL: Multi-Model Ensemble via Adversarial Learning
... traditional ensemble, or called true ensemble, has some disadvantages that are often ...2) Ensemble is always large and slow: Ensem- ble requires more computing operations than an individual network, ... See full document
8
Adversarial Multitask Learning for Joint Multi Feature and Multi Dialect Morphological Modeling
... multitask learning architectures in several configurations for cross-dialectal ...multitask learning model, and whether mapping the various pretrained word embedding spaces is ... See full document
12
Multi turn Dialogue Response Generation in an Adversarial Learning Framework
... the multi-turn context and the gen- erated responses from the models to 3 judges and asked them to rank the general response quality in terms of relevance and ...the model with the lowest quality is as- ... See full document
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Adversarial Multi Criteria Learning for Chinese Word Segmentation
... (2) In the second block, our proposed three mo- dels based on multi-criteria learning boost per- formance. Model-I gains 0.75% improvement on averaging F-measure score compared with Bi- LSTM result ... See full document
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MULTI-VIEW DOCUMENT CLUSTERING WITH DIFFERENT SIMILARITY MEASUREMENTS VIA ENSEMBLE
... the ensemble members and P*, a subset of ensemble members is selected and combined to obtain the final ...for ensemble learning: cluster-based similarity partitioning algorithm, ... See full document
12
DUT NLP at MEDIQA 2019: An Adversarial Multi Task Network to Jointly Model Recognizing Question Entailment and Question Answering
... novel Adversarial Multi-Task Network (AMTN) to jointly model these two ...representation learning and inter-sentence relationship modeling, which allows knowledge transfer from other ...the ... See full document
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A Unified Multi task Adversarial Learning Framework for Pharmacovigilance Mining
... Inspired by the success of stacked attentive RNN in solving other NLP tasks (Wu et al., 2016; Graves et al., 2013; Dyer et al., 2015; Prakash et al., 2016), we use the stacked GRU to encode the input text. The stacked ... See full document
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MICE:Multi layer multi model images classifier ensemble
... Autonomous Learning Multiple Model (ALMMo) method working in ...regression model as in the first ...classification model, full repeatability (unlike the methods that use probabilistic ... See full document
8
Dual Adversarial Neural Transfer for Low Resource Named Entity Recognition
... Adversarial Learning Adversarial learn- ing originates from Generative Adversarial Nets (GAN) (Goodfellow et ...apply adversarial learn- ing to NLP ...an adversarial ... See full document
11
An Adversarial Learning Framework For A Persona Based Multi Turn Dialogue Model
... the adversarial discrimi- nator, collaboratively predicts the attribute(s) that generated the input ...Seq2Seq model, we exper- iment with two conversational datasets, the Ubuntu Dialogue Corpus (UDC) and ... See full document
10
Adversarial Multi task Learning for Text Classification
... for multi-task learning, which focus on learning the shared layers to extract the common and task-invariant ...an adversarial multi-task learning frame- work, alleviating the ... See full document
10
A multi-model ensemble method that combines imperfect models through learning
... the learning phase is the difference in time-scale between atmosphere and ...the learning phase do not probe these ...certain model errors develop very ...particular model error, namely the ... See full document
17
Multi-model ensemble hydrologic prediction and uncertainties analysis
... the model parameter uncertainty using the SCE-UA and SCEM-UA ...the model input and model parameters’ ...the model input, model parameter and model structure ...XAJ model ... See full document
6
Consistency of the multi-model CMIP5/PMIP3-past1000 ensemble
... Analyses under the paradigm of statistical indistinguisha- bility require special care if we use them in the context of palaeoclimatology. Any simulated or reconstructed time series over the last 1000 yr includes ... See full document
17
Adversarial Label Learning
... of adversarial learning has recently be- come popular for deep learning (Goodfellow et ...Generative adversarial networks (GANs) pit a data genera- tor and a discriminator against each other ... See full document
8
Order-Revealing Encryption: File-Injection Attack and Forward Security
... Experiment results. We first executed our FIA schemes on the ideally secure OPE proposed by Popa et al. [24]. We remark that the experiment results will be the same for other OPE/ORE schemes without forward security, as ... See full document
23
Random subspacing for regression ensembles
... regression ensemble techniques of SR and DS in combination with Random Subspace method proved a more effective mechanism of improving the gen- eralization performance of simple regressors than the popular ... See full document
6
Heterogeneous Transfer Learning via Deep Matrix Completion with Adversarial Kernel Embedding
... transfer learning methods focus on learning problems where the source domain and target domain are represented by the same type of features (Pan et ...for multi-class clas- sification problems by ... See full document
8
Response to marine cloud brightening in a multi model ensemble
... in model- differences in estimates of the first indirect ...the model average, but a geographical distribution of those low clouds that could imply a reduced efficiency of the CDNC ...this model has ... See full document
14
Domain Adaptation with Adversarial Training and Graph Embeddings
... deep learning paradigm, Glo- rot et ...noise. Adversarial training of neural networks has shown big impact recently, especially in areas such as computer vision, where generative unsu- pervised models have ... See full document
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