readme: ---
  tags:
  - summarization
  - summary
  - booksum
  - long-document
  - long-form
  license:
  - apache-2.0
  - bsd-3-clause
  datasets:
  - 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 \u20AC 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 \u20AC 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: 'Is a else or outside the cob and tree written being of early client rope
      and you have is for good reasons. On to the ocean in Orange for time. By''s the
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      be okay. Good afternoon everybody. Welcome to this lecture of Computational Statistics.
      As you can see, I''m not socially my name is Michael Zelinger. I''m one of the
      task for this class and you might have already seen me in the first lecture where
      I made a quick appearance. I''m also going to give the tortillas in the last third
      of this course. So to give you a little bit about me, I''m a old student here
      with better Bulman and my res