readme: ---
  language:
  - en
  license: apache-2.0
  tags:
  - summarization
  - pegasus
  datasets:
  - kmfoda/booksum
  metrics:
  - rouge
  widget:
  - text: large earthquakes along a given fault segment do not occur at random intervals
      because it takes time to accumulate the strain energy for the rupture. The rates
      at which tectonic plates move and accumulate strain at their boundaries are approximately
      uniform. Therefore, in first approximation, one may expect that large ruptures
      of the same fault segment will occur at approximately constant time intervals.
      If subsequent main shocks have different amounts of slip across the fault, then
      the recurrence time may vary, and the basic idea of periodic mainshocks must be
      modified. For great plate boundary ruptures the length and slip often vary by
      a factor of 2. Along the southern segment of the San Andreas fault the recurrence
      interval is 145 years with variations of several decades. The smaller the standard
      deviation of the average recurrence interval, the more specific could be the long
      term prediction of a future mainshock.
    example_title: earthquakes
  - text: ' A typical feed-forward neural field algorithm. Spatiotemporal coordinates
      are fed into a neural network that predicts values in the reconstructed domain.
      Then, this domain is mapped to the sensor domain where sensor measurements are
      available as supervision. Class and Section Problems Addressed Generalization
      (Section 2) Inverse problems, ill-posed problems, editability; symmetries. Hybrid
      Representations (Section 3) Computation & memory efficiency, representation capacity,
      editability: Forward Maps (Section 4) Inverse problems Network Architecture (Section
      5) Spectral bias, integration & derivatives. Manipulating Neural Fields (Section
      6) Edit ability, constraints, regularization. Table 2: The five classes of techniques
      in the neural field toolbox each addresses problems that arise in learning, inference,
      and control. (Section 3). We can supervise reconstruction via differentiable forward
      maps that transform Or project our domain (e.g, 3D reconstruction via 2D images;
      Section 4) With appropriate network architecture choices, we can overcome neural
      network spectral biases (blurriness) and efficiently compute derivatives and integrals
      (Section 5). Finally, we can manipulate neural fields to add constraints and regularizations,
      and to achieve editable representations (Section 6). Collectively, these classes
      constitute a ''toolbox'' of techniques to help solve problems with neural fields
      There are three components in a conditional neural field: (1) An encoder or inference
      function € that outputs the conditioning latent variable 2 given an observation
      0 E(0) =2. 2 is typically a low-dimensional vector, and is often referred to aS
      a latent code Or feature code_ (2) A mapping function 4 between Z and neural field
      parameters O: Y(z) = O; (3) The neural field itself $. The encoder € finds the
      most probable z given the observations O: argmaxz P(2/0). The decoder maximizes
      the inverse conditional probability to find the most probable 0 given Z: arg-
      max P(Olz). We discuss different encoding schemes with different optimality guarantees
      (Section 2.1.1), both global and local conditioning (Section 2.1.2), and different
      mapping functions Y (Section 2.1.3) 2. Generalization Suppose we wish to estimate
      a plausible 3D surface shape given a partial or noisy point cloud. We need a suitable
      prior over the sur- face in its reconstruction domain to generalize to the partial
      observations. A neural network expresses a prior via the function space of its
      architecture and parameters 0, and generalization is influenced by the inductive
      bias of this function space (Section 5).'
    example_title: scientific paper
  - text: ' the big variety of data coming from diverse sources is one of the key properties
      of the big data phenomenon. It is, therefore, beneficial to understand how data
      is generated in various environments and scenarios, before looking at what should
      be done with this data and how to design the best possible architecture to accomplish
      this The evolution of IT architectures, described in Chapter 2, means that the
      data is no longer processed by a few big monolith systems, but rather by a group
      of services In parallel to the processing layer, the underlying data storage has
      also changed and became more distributed This, in turn, required a significant
      paradigm shift as the traditional approach to transactions (ACID) could no longer
      be supported. On top of this, cloud computing is becoming a major approach with
      the benefits of reducing costs and providing on-demand scalability but at the
      same time introducing concerns about privacy, data ownership, etc In the meantime
      the Internet continues its exponential growth: Every day both structur