[PDF] Top 20 Advances in scaling deep learning algorithms
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Advances in scaling deep learning algorithms
... Introduction Deep learning algorithms are a new development in machine ...results in significant benchmarks for artificial ...These advances and theoreti- cal considerations have ... Voir le document complet
133
A new method to control error rates in automated species identification with deep learning algorithms
... bottleneck in ecology. Deep Learning Algorithms (DLAs) have been increasingly used to automatically identify organisms on ...recent advances, it remains difficult to control the error ... Voir le document complet
14
Teacher algorithms for curriculum learning of Deep RL in continuously parameterized environments
... Keywords: Deep Reinforcement Learning, Teacher-Student Learning, Curriculum Learning, Learning Progress, Curiosity, Parameterized Procedural Environments 1 Introduction We address the ... Voir le document complet
20
Deep learning for brains?: Different linear and nonlinear scaling in UK Biobank brain images vs. machine-learning datasets
... machine learning reference datasets To verify that we can obtain empirical estimates of the sample complexity of linear, kernel, and deep models, we initially examined two reference datasets that have been ... Voir le document complet
31
Advances in deep learning methods for speech recognition and understanding
... works in end-to-end speech recognition [51, 7, 22, ...vanishing. In order to reduce the sequence length processed by GRUs, we sub-sample [7, 22] the hidden activations along the time domain for every ... Voir le document complet
108
Advances in deep learning with limited supervision and computational resources
... 2015). In Mnih et ...look in the image, i.e. which region of the image is considered in each time ...reinforcement learning using policy search. In practice, this approach can be ... Voir le document complet
139
The Deep Quality-Value Family of Deep Reinforcement Learning Algorithms
... DRL algorithms which in addition to learning an approximation of the Q function also aim at learning an approximation of the V ...DQV-Max Learning to DRL algorithms which only ... Voir le document complet
8
Scaling MAP-Elites to Deep Neuroevolution
... resulting in high standard deviations. Ant Maze. In the Ant Maze task, pure exploitation will lead the agent to the deceptive trap ...exploration algorithms (NS-ES, ME- ES explore) and the poor ... Voir le document complet
10
Deep Reinforcement Learning in Strategic Board Game Environments
... algorithms [ 4 , 28 ]. Also, it has been used in the field of natural language under- standing (parsing discourse used during multi-agent negotiations) [ 1 ], but such work has not dealt with strategic ... Voir le document complet
18
Entity-centric representations in deep learning
... Recent advances in deep reinforcement learning are in part driven by a capacity to learn good representations that can be used by an agent to update its ...of learning ... Voir le document complet
73
Towards deep semi supervised learning
... Representation Learning The success of many Machine Learning algorithms depends on data represen- ...perfect learning via simply a linear classifier in this representa- tion ...space. ... Voir le document complet
57
Deep learning in systems medicine
... data, Deep Learning holds great promise in this ...of Deep Learning algorithms and a set of general topics where Deep Learning is decisive; namely, within the ... Voir le document complet
54
Deep learning in event-based neuromorphic systems
... bio-inspired learning algorithms (different variants of STDP), which are applied on populations of inhibitory and excitatory leaky integrate-and-fire ...neurons. Learning in their implemen- ... Voir le document complet
147
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 ...never in combination with Deep Artificial Neural ...extension: ... Voir le document complet
10
Deep learning for cloud detection
... Conclusion Deep learning offers the possibility to build really complex and robust ...addressed in this paper was to investigate the use of convo- lutional networks for cloud detection and to compare ... Voir le document complet
7
Advances in deep generative modeling for clinical data
... representation learning is building deep generative models with identifiable latent ...However deep generative models typically rely on conditional probability distributions defined using ... Voir le document complet
221
Deep learning for cloud detection
... Conclusion Deep learning offers the possibility to build really complex and robust ...addressed in this paper was to investigate the use of convo- lutional networks for cloud detection and to compare ... Voir le document complet
8
Deep Reinforcement Learning in Strategic Board Game Environments
... algorithms [ 4 , 28 ]. Also, it has been used in the field of natural language under- standing (parsing discourse used during multi-agent negotiations) [ 1 ], but such work has not dealt with strategic ... Voir le document complet
17
Deep Learning and Reinforcement Learning for Inventory Control
... displayed in Figure ...presented in Table ...correlations in the observation sequence ...when learning is carried out several ...(i.i.d.) in most of proofs for the convergence of ... Voir le document complet
69
Coflow scheduling in input-queued switches: Optimal delay scaling and algorithms
... delay in the context of an N × N input-queued switch with stochastic coflow ...arrivals. In each slot, a random number of coflows, each of them consisting of multiple parallel flows, arrive to the ... Voir le document complet
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