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Multi-Task

Sparse Multi-task Reinforcement Learning

Sparse Multi-task Reinforcement Learning

... as multi-task rein- forcement learning (MTRL), the objective is to simultaneously solve multiple tasks and exploit their similarity to improve the performance ...the multi-task learning ...

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Multi-task transfer learning for timescale graphical event models

Multi-task transfer learning for timescale graphical event models

... In the same work, they have proposed an asymptotically consistent greedy algorithm to learn the structure and parameters of one single TGEM from an event log file. However, one may want to learn multiple processes that ...

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Multi-Task Deep Learning for Satellite Image Pansharpening and Segmentation

Multi-Task Deep Learning for Satellite Image Pansharpening and Segmentation

... single task can be very effi- cient and concentrated; however, the knowledge gained by the model during training does not guarantee to generalize well to new data, whereas developing features useful for several ...

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Generalized Concomitant Multi-Task Lasso for Sparse Multimodal Regression

Generalized Concomitant Multi-Task Lasso for Sparse Multimodal Regression

... a multi-task setting. These results are still valid for single task problems ( q = 1), in which case the formulas and algorithms are simpler (see Appendix B ...

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Protein Structural Annotation: Multi-Task Learning and Feature Selection

Protein Structural Annotation: Multi-Task Learning and Feature Selection

... such multi-task approaches in this ...a multi-task approach for protein structure prediction has already been proposed by [ 2 ], by combining solvent accessibility prediction and secondary ...

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Punctual versus continuous auction coordination for multi-robot and multi-task topological navigation

Punctual versus continuous auction coordination for multi-robot and multi-task topological navigation

... on multi-task missions where each task is located in the environment and can be achieved by a single ...However, multi-robot missions require good coordination between the robots’ actions to ...

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Multi-task dialog act and sentiment recognition on Mastodon

Multi-task dialog act and sentiment recognition on Mastodon

... Figure 3: Sentiment F1 as a function of the number of dialogues used for training. both-rich: both tasks have the same training size; sentiment-poor: only 38 dialogues maximum are annotated with sentiment labels; ...

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A Domain-Specific Language for Multi-task Systems, applying Discrete Controller Synthesis

A Domain-Specific Language for Multi-task Systems, applying Discrete Controller Synthesis

... We propose a domain-specific language, called Nemo, encapsulating controller syn- thesis for multi-task systems. Its constructs describe domain-specific notions of resources and their constraints, tasks and ...

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Mitigating Bias in Gender, Age and Ethnicity Classification: a Multi-Task Convolution Neural Network Approach

Mitigating Bias in Gender, Age and Ethnicity Classification: a Multi-Task Convolution Neural Network Approach

... a Multi-Task Convolution Neural Net- work (MTCNN) employing joint dynamic loss weight adjustment towards classification of named soft biometrics, as well as towards mitigation of soft biometrics related ...

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Combining Multi-Task Learning and Multi-Channel Variational Auto-Encoders to Exploit Datasets with Missing Observations -Application to Multi-Modal Neuroimaging Studies in Dementia

Combining Multi-Task Learning and Multi-Channel Variational Auto-Encoders to Exploit Datasets with Missing Observations -Application to Multi-Modal Neuroimaging Studies in Dementia

... a multi-task generative latent-variable model where the common variability across datasets stems from the estimation of a shared latent representation across ...of multi-view datasets, even when the ...

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Empirical study and multi-task learning exploration for neural sequence labeling models

Empirical study and multi-task learning exploration for neural sequence labeling models

... learn task-specific knowledge and task-invariant knowledge can improve the ...and task-specific parameters and allows efficient multi-task training for sequential ...and ...

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Aligning and Updating Cadaster Maps with Aerial Images by Multi-Task, Multi-Resolution Deep Learning

Aligning and Updating Cadaster Maps with Aerial Images by Multi-Task, Multi-Resolution Deep Learning

... The multi-task, multi-resolution method presented in this paper can be used to effectively solve the common problem of aligning existing maps over a new satellite image while also detecting new ...

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Predicting fueling process on hydrogen refueling stations using multi-task machine learning

Predicting fueling process on hydrogen refueling stations using multi-task machine learning

... In this study, we collected large amounts of sensor data from three hy- drogen refueling stations of type H70-T40 with different time spans and built prediction models to predict the outcomes of the fueling events. We ...

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High-Dimensional Multi-Task Averaging and Application to Kernel Mean Embedding

High-Dimensional Multi-Task Averaging and Application to Kernel Mean Embedding

... combined with Proposition 3.5. Although we assumed equal bag sizes in the theoretical results, the proposed approaches provide accurate estimations also for the imbalanced setting. Figure 1(c) shows that the improvement ...

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Clustered Multi-Task Learning: A Convex Formulation

Clustered Multi-Task Learning: A Convex Formulation

... of multi-task learning, which has recently emerged as a very promising research direction for various applications ...In multi- task learning several related inference tasks are considered ...

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Multi-Task Learning For Option Pricing

Multi-Task Learning For Option Pricing

... the multi-task learning technique described in the previous section to simul- taneously train several neural networks, each using a different subset of the training ...case, multi-task ...

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Wasserstein regularization for sparse multi-task regression

Wasserstein regularization for sparse multi-task regression

... We argue in this paper that these techniques fail to leverage the spatial information associ- ated to regressors. Indeed, while sparse priors enforce that only a small subset of variables is used, the assumption that ...

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Multi-subject MEG/EEG source imaging with sparse multi-task regression

Multi-subject MEG/EEG source imaging with sparse multi-task regression

... group-level multi-task regression model lead to spurious activations next to secondary somatosensory cortices and on middle temporal ...cognitive task performed by the subjects is more advanced, ...

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Torch-Points3D: A modular multi-task framework for reproducible deep learning on 3D point clouds

Torch-Points3D: A modular multi-task framework for reproducible deep learning on 3D point clouds

... Torch-Points3D is intended for novices as much as ex- perts. It provides intuitive interfaces with most open- access 3D datasets, re-implementations of many of the top- performing networks, classic data augmentation ...

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Ship identification and characterization in Sentinel-1 SAR images with multi-task deep learning

Ship identification and characterization in Sentinel-1 SAR images with multi-task deep learning

... HAL Id: hal-02407571 https://hal-imt-atlantique.archives-ouvertes.fr/hal-02407571 Submitted on 12 Dec 2019 HAL is a multi-disciplinary open access archive for the deposit and dissemination of sci- entific ...

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