text: P ⋆(Eθ(x)) = argmin 
  
  P (Eθ(x)) 
  Eθ(x)). 
  | 
  
  Y  1 
  
  P ∥ 
  = median(Y 
  −  ∥ 
  
  Substituting this expression for the optimal predictor 
  into the loss function and evaluating the expected gradi- 
  ent of the encoder gives 
  
  θE 
  P ⋆(Eθ(x)) 
  ∥ 
  θMAD(Y  Y 
  1 = 
  ∥ 
  Eθ(x)), 
  
  ∇ 
  Eθ(x)) is the median absolute deviation 
  where MAD( 
  of a random variable conditioned on Eθ(x). Thus, in the 
  case where the predictor is optimal, the encoder must 
  learn to capture as much information about the video 
  as possible to minimize the deviation of the target. The 
  hypothesis is that incorporating an exponential moving 
  average to compute the representation of y ensures that 
  the predictor evolves faster than the encoder and remains 
  close to optimal, thereby preventing collapse. 
  
  −  ∇  | 
  
  · | 
  
  3.1 Training Objective 
  We train our visual encoder Eθ( 
  ) to satisfy the con- 
  straint that representations computed from one part of 
  the video, y, should be predictable from representations 