**Bridge-net** is an audio model block used in the [ClariNet](https://paperswithcode.com/method/clarinet) text-to-speech architecture. Bridge-net maps frame-level hidden representation to sample-level through several [convolution](https://paperswithcode.com/method/convolution) blocks and [transposed convolution](https://paperswithcode.com/method/transposed-convolution) layers interleaved with softsign non-linearities.

**Multi-View Entity Representations**, or **MuVER**, is an approach for entity retrieval that constructs multi-view representations for entity descriptions and approximates the optimal view for mentions via a heuristic searching method. It matches a mention to the appropriate entity by comparing it with entity descriptions. Motivated by the fact that mentions with different contexts correspond to different parts in descriptions, multi-view representations are constructed for each description. Specifically, we segment a description into several sentences. We refer to each sentence as a view $v$, which contains partial information, to form a view set $\mathcal{V}$ of the entity $e$. The Figure illustrates an example that constructs a view set $\mathcal{V}$ for “Kobe Bryant”.

MuVER

MuVER: Improving First-Stage Entity Retrieval with Multi-View Entity Representations

Bridge-net

ClariNet: Parallel Wave Generation in End-to-End Text-to-Speech

**Expected Sarsa** is like [Q-learning](https://paperswithcode.com/method/q-learning) but instead of taking the maximum over next state-action pairs, we use the expected value, taking into account how likely each action is under the current policy.

$$Q\left(S\_{t}, A\_{t}\right) \leftarrow Q\left(S\_{t}, A\_{t}\right) + \alpha\left[R_{t+1} + \gamma\sum\_{a}\pi\left(a\mid{S\_{t+1}}\right)Q\left(S\_{t+1}, a\right) - Q\left(S\_{t}, A\_{t}\right)\right] $$

Except for this change to the update rule, the algorithm otherwise follows the scheme of Q-learning. It is more computationally expensive than [Sarsa](https://paperswithcode.com/method/sarsa) but it eliminates the variance due to the random selection of $A\_{t+1}$.

Source: Sutton and Barto, Reinforcement Learning, 2nd Edition

Source	ClariNet: Parallel Wave Generation in End-to-End Text-to-Speech
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com