Run a single M-step update for the Baum-Welch/Forward-Backward algorithm. Parameters ---------- O : :py:clas

Maximize the VLB with respect to the model parameters, α and β

r""" Penalized negative log likelihood of the targets under the current model. .. math:: \text{NLL} = -\frac{1}{

Gradient of the penalized negative log likelihood wrt beta

True if EnvModel contains `key`, else False

A dropout regularization wrapper. Notes ----- During training, a dropout layer zeroes each element of the layer inpu

r""" Compute the backward probability trellis for an HMM parameterized by :math:`(A, B, \pi)`. Notes ----- Th

FC1 -> ReLU -> FC2 -> ReLU -> FC3 -> ReLU -> FC4

MLP decoder FC1 -> ReLU -> FC2 -> Sigmoid

CNN encoder Conv1 -> ReLU -> MaxPool1 -> Conv2 -> ReLU -> MaxPool2 -> Flatten -> FC1 -> ReLU -> FC2

FC1 -> ReLU -> FC2 -> ReLU -> FC3 -> ReLU -> FC4

Construct the noise distribution for use during negative sampling. For a word ``w`` in the corpus, the noise distribution is::

Construct Huffman Tree

Actual computation of the gradient of the loss wrt. q, k, and v

Actual computation of gradient of the loss wrt. each input

Actual computation of gradient of loss wrt. each input

Computation of gradient of loss wrt. X, scaler, and intercept

Computation of gradient of loss wrt X, scaler, and intercept

Computation of gradient of the loss wrt X, scaler, intercept

Computation of gradient of the loss wrt X, scaler, intercept

Actual computation of gradient of the loss wrt. W

Actual computation of gradient of the loss wrt. X, W, and b

Actual computation of the gradient of the loss wrt. the input X. The Jacobian, J, of the softmax for input x = [x1, ..., xn] is:

Actual computation of gradient of the loss wrt. X, W, and b

Actual computation of gradient of the loss wrt. X, W, and b

Actual computation of gradient of the loss wrt. X, W, and b

Actual computation of gradient of the loss wrt. X, W, and b

Compute second derivatives / deriv. of loss wrt. dX, dW, and db

Values from: https://pytorch.org/docs/stable/nn.html?#torch.nn.init.calculate_gain

Compute the (smoothed-) inverse-document frequency for each token in the corpus. For a word token `w`, the IDF is simply

Compute the "priority" for state-action pair (s, a). The priority P is defined as: P = sum_{s_} p(s_) * abs(r + temporal

Compute the gradient penalty's contribution to the critic loss and update the parameter gradients accordingly. Parameters

Replace all tokens that occur less than `min_count` with the `<unk>` token.

Replace all tokens that occur less than `min_count` with the `<unk>` token.

Perform tokenization and compute token counts for a single document

Execute agent on an episode. Parameters ---------- max_steps : int The maximum number of steps to run th

Compute an approximation of the conditional probability that token ii is assigned to topic jj given all previous topic assignments an

Perform the averaging transform proposed by Church and Gale (1991): estimate the expected count-of-counts by the *density* of

Actual computation of forward pass

Actual computation of forward pass

Actual computation of forward pass

Actual computation of forward pass

Collect bigram counts for the tokens in vocab

Collapsed Gibbs sampler for estimating the posterior distribution over topic assignments.

Actual computation of gradient wrt. loss weights + input

A greedy behavior policy. Notes ----- Only used when off-policy is True. Parameters ----------

A greedy behavior policy. Only used when off-policy is true. Parameters ---------- s : int, float, or tuple

A greedy behavior policy. Parameters ---------- s : int, float, or tuple The state number for the curren

Compute the impurity gain associated with a given split. IG(split) = loss(parent) - weighted_avg[loss(left_child), loss(right_child)

Randomly initialize the factor matrices

Initialize the factor matrices using vanilla ALS