[PDF] Top 20 Entity-centric representations in deep learning
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Entity-centric representations in deep learning
... Strategy In many environments a uniformly random policy is insufficient to produce action and observation sequences representative enough to be useful for downstream ...tasks. In this paper we suggest to ... Voir le document complet
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Some open questions on morphological operators and representations in the deep learning era
... hierarchical representations associated to dendrograms (rooted trees). In that case, the mathematical struc- ture corresponds to an ultrametric ...ubiquitous in mathematical morphology, ranging from ... Voir le document complet
21
Deep learning of orthographic representations in baboons
... effects in baboons [2] clearly suggested that the monkeys had learned some kind of orthographic ...baboons in the ...observed in baboons as well as for intriguing inter-individual ...letter ... Voir le document complet
10
Invariance and Stability of Deep Convolutional Representations
... signal representations that are near-invariant to the action of any group of ...norm in the functional space. In particular, the RKHS norm controls both stability and generalization, so that ... Voir le document complet
22
Towards deep semi supervised learning
... in particular, comparisons with generative models are at least intuitively appealing to get more insight about how the model learns about unlabeled ...produce representations that are going to be easy to ... Voir le document complet
57
Learning Activation Functions in Deep Neural Networks
... of deep neural networks (deep learning) achieved considerable success in pattern recognition and text ...of deep learning on images, video or text classification, the application ... Voir le document complet
171
Deep Quality Value (DQV) Learning
... (7) In [34] it is shown that QV(λ ) outperforms different offline and online RL algorithms in Sutton’s Dyna maze ...tabular representations and Shallow Neural Networks [35] and never in ... Voir le document complet
10
Random Matrix Theory Proves that Deep Learning Representations of GAN-data Behave as Gaussian Mixtures
... machine learning methods depends strongly on the choice of the data representation (or fea- tures) on which they are ...task in order to achieve learning with simple models and small amount of ... Voir le document complet
10
Advances in scaling deep learning algorithms
... representation learning algorithms, often for the purpose of building deep hierarchies of features 1 ...of Deep Learning algorithms, which are based on unsupervised learning of ... Voir le document complet
133
Learning representations for Information Retrieval
... estimation? In order to answer these questions, it is necessary to access computa- tional representations of documents and ...useful representations for documents and queries has always been central ... Voir le document complet
159
Comparing learned representations of deep neural networks
... similar representations than ...hidden representations, identify more similar features in in- put images, and have very high adversarial example ...desirable in certain use-cases where ... Voir le document complet
64
Deep learning in event-based neuromorphic systems
... One-hot representations for event-based image recognition The type of features learned by a WTA-based algorithm are so-called one- hot ...hand, representations learned by the backpropagation algorithm are ... Voir le document complet
147
Learning representations from functional MRI data
... study in Figure 7.3 . Our approach was driven by the recent successes of deep non-linear models in computer vision and medical ...linearities in our models leads to a drastic increase of ... Voir le document complet
183
Deep Tweets: from Entity Linking to Sentiment Analysis
... far in both the Italian and the English (Ghosh et ...campaigns. In particular, in the SENTIPOLC irony detection task, participants were required to develop systems able to decide whether a given ... Voir le document complet
7
Deep learning of representations and its application to computer vision
... value. In order to use a comparable amount of computational resources in all cases, we used at most 1600 hidden units and a 3 ⇥ 3 pooling grid for all three ... Voir le document complet
165
Frankenstein: Learning Deep Face Representations using Small Data
... information. In the view of NIR-VIS cross-modality, we also define ‘cross-modality synthesis’ which uses images from different modalities to synthesize a new ...detailed in Section ...shown in the ... Voir le document complet
12
Automatically learning optimal formula simplifiers and database entity matching rules
... interest in using deep learning for program syn- thesis ...idea in these approaches is to design neural architectures that can encode a specification (such as input-output examples or natural ... Voir le document complet
161
Deep Reinforcement Learning in Strategic Board Game Environments
... experiments in a pool with only jSettlers as ...built in [ 4 ], namely a DRL agent whose policy was pre-trained using about 500, 000 learning experiences while facing jSettler ...70 learning ... Voir le document complet
18
Deep Learning and Reinforcement Learning for Inventory Control
... system in supply chain which delivers beer from a beer producer to the end ...categorized in a group of games illustrating bullwhip effect (Devika et ...variation in upstream moving node increases ... Voir le document complet
69
Classifying logistic vehicles in cities using Deep learning
... vehicles in cities if we consider that today there are no other tools to do this type of ...keep in mind that these results were obtained with a reasonably small image base for a Deep Learning ... Voir le document complet
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