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Model-Based Deep Learning
[article]
2021
arXiv
pre-print
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In this article we survey the leading approaches for studying and designing model-based deep learning systems. ...
Such model-based deep learning methods exploit both partial domain knowledge, via mathematical structures designed for specific problems, as well as learning from limited data. ...
Some representative issues and their relationship with the recommended model-based deep learning approaches include: 1) Missing domain knowledge -model-based deep learning can implement the model-based ...
arXiv:2012.08405v2
fatcat:4ilqi3vv4rar5gsveqzo4loqpy
Multi-task learning with deep model based reinforcement learning
[article]
2017
arXiv
pre-print
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In this paper, we present a model based approach to deep reinforcement learning which we use to solve different tasks simultaneously. ...
In recent years, model-free methods that use deep learning have achieved great success in many different reinforcement learning environments. ...
DISCUSSION We have presented a novel model based approach to deep reinforcement learning that opens new lines of research in this area. ...
arXiv:1611.01457v4
fatcat:l7bdberxpze3rej4asioliwa6u
Learning to Paint With Model-based Deep Reinforcement Learning
[article]
2019
arXiv
pre-print
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By employing a neural renderer in model-based Deep Reinforcement Learning (DRL), our agents learn to determine the position and color of each stroke and make long-term plans to decompose texture-rich images ...
The training is based on the Deep Reinforcement Learning framework, which encourages the agent to make long-term plans for sequential stroke-based painting. ...
Learning In this section, we introduce how to train the agent using the model-based DDPG algorithm. ...
arXiv:1903.04411v3
fatcat:j5dvpxwanzc4vda3aue3j4p6t4
Model-Based Deep Learning: On the Intersection of Deep Learning and Optimization
[article]
2022
arXiv
pre-print
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Model-based optimization and data-centric deep learning are often considered to be distinct disciplines. ...
deep learning. ...
form of model-based deep learning. ...
arXiv:2205.02640v2
fatcat:yclu5hqsx5bv7k2fg4gnfhyrma
Learning to Fly via Deep Model-Based Reinforcement Learning
[article]
2020
arXiv
pre-print
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In this work, by leveraging a learnt probabilistic model of drone dynamics, we learn a thrust-attitude controller for a quadrotor through model-based reinforcement learning. ...
Learning to control robots without requiring engineered models has been a long-term goal, promising diverse and novel applications. ...
Reinforcement Learning Model-free deep RL has received a tremendous amount of attention ever since Q-learning was successfully applied to playing Atari games directly from raw input images with the use ...
arXiv:2003.08876v3
fatcat:gonirfi77rdvxbditv2pnop5lu
Deep Learning Based Vehicle Make-Model Classification
[article]
2018
arXiv
pre-print
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Then, we feed them into the CNN model. It is reached approximately 4% better classification accuracy result than using a conventional CNN model. ...
A pipeline is proposed to combine an SSD (Single Shot Multibox Detector) model with a CNN (Convolutional Neural Network) model to train on the database. ...
Fig. 3 : 3 The architecture of the SSD model for detection
Fig. 5 : 5 Overall accuracy result of Experiment III, VGG based weights of SSDWe also tested the SSD based model on some videos. ...
arXiv:1809.00953v1
fatcat:olj2pzzrsjgmrijhfh5ssnre6e
MoDL: Model Based Deep Learning Architecture for Inverse Problems
[article]
2018
arXiv
pre-print
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We introduce a model-based image reconstruction framework with a convolution neural network (CNN) based regularization prior. ...
Since the forward model is explicitly accounted for, a smaller network with fewer parameters is sufficient to capture the image information compared to black-box deep learning approaches, thus reducing ...
The proposed framework, termed as MOdel-based reconstruction using Deep Learned priors (MoDL), merges the power of model-based reconstruction schemes with deep learning. ...
arXiv:1712.02862v3
fatcat:es47nwox2baktgrqpujm5mrura
Poisoning Deep Learning Based Recommender Model in Federated Learning Scenarios
[article]
2022
arXiv
pre-print
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For proving current federated recommendation is still vulnerable, in this work we probe to design attack approaches targeting deep learning based recommender models in federated learning scenarios. ...
Extensive experiments show that our well-designed attacks can effectively poison the target models, and the attack effectiveness sets the state-of-the-art. ...
We aim to poison deep learning based recommender model in FL scenarios without the prior knowledge. ...
arXiv:2204.13594v2
fatcat:qki4v777t5hhxfhxvqow4sb62i
Explaining Deep Learning-Based Driver Models
2021
Applied Sciences
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Different systems based on Artificial Intelligence (AI) techniques are currently used in relevant areas such as healthcare, cybersecurity, natural language processing, and self-driving cars. ...
The proposed model is based on the cumulative prospect theory (CPT) [49] , and the model parameters are learned using a hierarchical learning algorithm based on inverse reinforcement learning [50] and ...
In [34] , a deep facial expression recognition algorithm for emotions based on CNNs and an ensemble deep learning algorithm to predict facial expressions are proposed. ...
doi:10.3390/app11083321
fatcat:cyiwwkqiqvggdhb3zfqztbqqgi
LSTM-based Deep Learning Models for Non-factoid Answer Selection
[article]
2016
arXiv
pre-print
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In this paper, we apply a general deep learning (DL) framework for the answer selection task, which does not depend on manually defined features or linguistic tools. ...
The basic framework is to build the embeddings of questions and answers based on bidirectional long short-term memory (biLSTM) models, and measure their closeness by cosine similarity. ...
RESULTS
CONCLUSION In this paper, we study the answer selection task by employing a bidirectional-LSTM based deep learning framework. ...
arXiv:1511.04108v4
fatcat:6hubuxrlxrampmjk6quic2st4i
SOLAR: Deep Structured Representations for Model-Based Reinforcement Learning
[article]
2019
arXiv
pre-print
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2019 pre-print arXiv:1808.09105v3 fatcat:gz324guwe5h2nolqvtygfcqgkqOther Versions
Model-based reinforcement learning (RL) has proven to be a data efficient approach for learning control tasks but is difficult to utilize in domains with complex observations such as images. ...
In this paper, we present a method for learning representations that are suitable for iterative model-based policy improvement, even when the underlying dynamical system has complex dynamics and image ...
For the real world tasks, we also compare to deep visual foresight (DVF; Ebert et al., 2018) , a state-of-the-art model-based method for images which does not use representation learning. ...
arXiv:1808.09105v4
fatcat:jpgdhn6b35ec5k3erhgdhp4ofy
Learning Tree-based Deep Model for Recommender Systems
2018
Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining - KDD '18
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We propose a novel tree-based method which can provide logarithmic complexity w.r.t. corpus size even with more expressive models such as deep neural networks. ...
Model-based methods for recommender systems have been studied extensively in recent years. ...
Then we propose the joint learning framework of the tree-based index and deep model. In the last subsection, we specify the hierarchical user preference representation used in model training. ...
doi:10.1145/3219819.3219826
dblp:conf/kdd/ZhuLZLHLG18
fatcat:zp7goqjacrgvbglzq2tv2gmapi
Auto-Ensemble: An Adaptive Learning Rate Scheduling based Deep Learning Model Ensembling
[article]
2020
arXiv
pre-print
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This paper proposes Auto-Ensemble (AE) to collect checkpoints of deep learning model and ensemble them automatically by adaptive learning rate scheduling algorithm. ...
Ensembling deep learning models is a shortcut to promote its implementation in new scenarios, which can avoid tuning neural networks, losses and training algorithms from scratch. ...
And the feature engineering is highly based on manual selection.To avoid huge training budget and complicated feature engineering, this paper attempts to provide a deep learning based simple and automatic ...
arXiv:2003.11266v1
fatcat:tc6rz5gl4jbdxebdcrsaqipylm
Algorithmic Framework for Model-based Deep Reinforcement Learning with Theoretical Guarantees
[article]
2021
arXiv
pre-print
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2019 pre-print arXiv:1807.03858v4 fatcat:nwxxf6lqgncmbfiy6mvnefof6mOther Versions
Model-based reinforcement learning (RL) is considered to be a promising approach to reduce the sample complexity that hinders model-free RL. ...
This paper introduces a novel algorithmic framework for designing and analyzing model-based RL algorithms with theoretical guarantees. ...
Despite promising empirical findings, many of theoretical properties of model-based deep reinforcement learning are not well-understood. ...
arXiv:1807.03858v5
fatcat:4g56r23yoje73g2imvc73nd7oy
Recent Progresses in Deep Learning based Acoustic Models (Updated)
[article]
2018
arXiv
pre-print
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In this paper, we summarize recent progresses made in deep learning based acoustic models and the motivation and insights behind the surveyed techniques. ...
, and the attention-based sequence-to-sequence model. ...
With the application of deep learning models, now the ASR systems on close-talking scenario perform very well. ...
arXiv:1804.09298v2
fatcat:yfxzxu6qanbndcnmt3loikqeym
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