Differentiable neural computer

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  1. The Differentiable Neural Computer model came from deep mind a few months ago, and is the successor to the Turing machine.[1]
  2. “This DNC is working towards achieving meta learning, in other words - learning to learn.[1]
  3. The access module is where the main DNC logic happens; as this is where memory is written to and read from.[2]
  4. The dnc simply wraps the access module and the control module, and forms the basic RNNCore unit of the overall architecture.[2]
  5. The DNC requires an installation of TensorFlow and Sonnet.[2]
  6. The Differentiable Neural Computer is a neural network which takes advantage of memory augmentation and, at the same time, the attention mechanism.[3]
  7. What is cool in the DNC is the system of vectors and operations mediating between controller and memory.[3]
  8. In artificial intelligence, a differentiable neural computer (DNC) is a memory augmented neural network architecture (MANN), which is typically (not by definition) recurrent in its implementation.[4]
  9. DNC can be trained to navigate rapid transit systems, and apply that network to a different system.[4]
  10. This video shows a DNC successfully finding the shortest path between two nodes in a randomly generated graph.[5]
  11. By decoding the memory usage of the DNC (as in Fig.[5]
  12. During the initial query phase, the DNC receives the labels for the start and end goal ("390" and "040" respectively).[5]
  13. The Differentiable Neural Computer (DNC) can learn algorithmic and question answering tasks.[6]
  14. Second, DNC's de-allocation of memory results in aliasing, which is a problem for content-based look-up.[6]
  15. The primary source of information regarding the DNC model is obviously the original paper and its contents prevail over any statement made in this guide.[7]
  16. Throughout this guide, I will often use what I call dualistic simplification in order to introduce discussions regarding many of the components of a DNC in a educational manner.[7]
  17. A DNC is composed of a controller, a memory and an output module.[7]
  18. The DNC memory is time-varying (like regular computer memories), repeatedly being read and written.[7]
  19. Recent study shows that the differentiable neural computer (DNC) can make considerable improvement over the RNN for modeling of sequential data.[8]
  20. In the current study, DNC has been implemented as an extension to REINVENT, an RNN-based model that has already been used successfully to make de novo molecular design.[8]
  21. Differentiable neural computer (DNC) has demonstrated remarkable capabilities in solving complex problems.[9]
  22. In this paper, we propose to stack an enhanced version of differentiable neural computer together to extend its learning capabilities.[9]
  23. Firstly, we give an intuitive interpretation of DNC to explain the architectural essence and demonstrate the stacking feasibility by contrasting it with the conventional recurrent neural network.[9]
  24. We introduce an external memory, differential neural computer (DNC), to improve video context understanding.[10]
  25. DNC naturally learns to use its internal memory for context understanding and also provides contents of its memory as an output for additional connection.[10]
  26. Our consecutive DNC is sequentially trained with its language model (LSTM) for each video clip to generate context-aware captions with superior quality.[10]
  27. The differentiable neural computer (DNC), a memory-augmented neural network, is designed as a general problem solver which can be used in a wide range of tasks.[11]
  28. We analyze the DNC and identify possible improvements within the application of question answering.[11]
  29. This motivates a more robust and scalable DNC (rsDNC).[11]
  30. Differentiable neural computer (DNC) refers to a new architecture of computers equipped with artificial intelligence that can access the memory and process it to answer new questions.[12]
  31. propose to keep track of consecutively modified memory locations, thereby enabling a DNC to recover sequences in the written order.[13]
  32. Furthermore, DNC does not explicitly consider the memorization itself as a target objective, which inevitably leads to a very slow learning speed of the model.[14]
  33. To address those issues, we propose a novel distributed memory-based self-supervised DNC architecture for enhanced memory augmented neural network performance.[14]
  34. In a new paper published in Nature, the Google subsidiary DeepMind explained a new approach to machine learning that uses something called a differentiable neural computer.[15]
  35. DeepMind tested its differentiable neural computer on the London Underground and was successful at generating routes from the structured data.[15]
  36. The agent and DNC model are trained in conjunction iteratively.[16]
  37. In this thesis, a DNC has been implemented as an extension to REINVENT, an RNN based model that has already been successfully shown to generate molecules with high validity.[17]
  38. The DNC shows some improvement on all tests conducted at the cost of greatly increased computational time and memory consumption, which puts its practical use into question.[17]
  39. This project also gives some insight into the effect of the DNC hyperparameters for the task of generative modeling of molecules.[17]
  40. These negative results can hopefully provide important information for others working with the Differentiable Neural Computer (DNC).[18]
  41. In this post I’ll cover a series of experiments I performed to test what is going on in the external memory of a DNC, without being able to find anything positively conclusive.[18]
  42. The DNC is a form of a memory augmented Neural Network that has shown promise on solving complex tasks that are difficult for traditional Neural Networks.[18]
  43. The external memory of the DNC also presents an additional mechanism to observe what the DNC is doing at each timestep.[18]

소스

  1. ↑ 이동: 1.0 1.1 The Differentiable Neural Computer
  2. ↑ 이동: 2.0 2.1 2.2 deepmind/dnc: A TensorFlow implementation of the Differentiable Neural Computer.
  3. ↑ 이동: 3.0 3.1 Differentiable Neural Computers: An Overview
  4. ↑ 이동: 4.0 4.1 Differentiable neural computer
  5. ↑ 이동: 5.0 5.1 5.2 Hybrid computing using a neural network with dynamic external memory
  6. ↑ 이동: 6.0 6.1 Improving Differentiable Neural Computers Through Memory Masking...
  7. ↑ 이동: 7.0 7.1 7.2 7.3 A bit-by-bit guide to the equations governing differentiable neural computers
  8. ↑ 이동: 8.0 8.1 Comparison Between SMILES-Based Differential Neural Computer and Recurrent Neural Network Architectures for De Novo Molecule Design
  9. ↑ 이동: 9.0 9.1 9.2 EEG data analysis with stacked differentiable neural computers
  10. ↑ 이동: 10.0 10.1 10.2 Context Aware Video Caption Generation with Consecutive Differentiable Neural Computer
  11. ↑ 이동: 11.0 11.1 11.2 Robust and Scalable Differentiable Neural Computer for Question Answering
  12. ↑ Differentiable neural computer
  13. ↑ DNC: Differential Neural Network
  14. ↑ 이동: 14.0 14.1 Distributed Memory based Self-Supervised Differentiable Neural Computer,arXiv
  15. ↑ 이동: 15.0 15.1 DeepMind’s differentiable neural computer helps you navigate the subway with its memory – TechCrunch
  16. ↑ Iterative model-based Reinforcement Learning using simulations in the Differentiable Neural Computer
  17. ↑ 이동: 17.0 17.1 17.2 Chalmers Open Digital Repository: Differentiable Neural Computers for in silico molecular design: Benchmarks of architectures in generative modeling of molecules
  18. ↑ 이동: 18.0 18.1 18.2 18.3 differentiable neural computer – Adeel's Corner

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  • [{'LOWER': 'differentiable'}, {'LOWER': 'neural'}, {'LEMMA': 'computer'}]
  • [{'LEMMA': 'DNC'}]
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