Differentiable neural computer

둘러보기로 가기 검색하러 가기




  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]


  1. 1.0 1.1 The Differentiable Neural Computer
  2. 2.0 2.1 2.2 deepmind/dnc: A TensorFlow implementation of the Differentiable Neural Computer.
  3. 3.0 3.1 Differentiable Neural Computers: An Overview
  4. 4.0 4.1 Differentiable neural computer
  5. 5.0 5.1 5.2 Hybrid computing using a neural network with dynamic external memory
  6. 6.0 6.1 Improving Differentiable Neural Computers Through Memory Masking...
  7. 7.0 7.1 7.2 7.3 A bit-by-bit guide to the equations governing differentiable neural computers
  8. 8.0 8.1 Comparison Between SMILES-Based Differential Neural Computer and Recurrent Neural Network Architectures for De Novo Molecule Design
  9. 9.0 9.1 9.2 EEG data analysis with stacked differentiable neural computers
  10. 10.0 10.1 10.2 Context Aware Video Caption Generation with Consecutive Differentiable Neural Computer
  11. 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. 14.0 14.1 Distributed Memory based Self-Supervised Differentiable Neural Computer,arXiv
  15. 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. 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. 18.0 18.1 18.2 18.3 differentiable neural computer – Adeel's Corner



Spacy 패턴 목록

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