**CANINE** is a pre-trained encoder for language understanding that operates directly on character sequences—without explicit tokenization or vocabulary—and a pre-training strategy with soft inductive biases in place of hard token boundaries. To use its finer-grained input effectively and efficiently, Canine combines downsampling, which reduces the input sequence length, with a deep [transformer](https://paperswithcode.com/method/transformer) stack, which encodes context.

A **Double Deep Q-Network**, or **Double DQN** utilises [Double Q-learning](https://paperswithcode.com/method/double-q-learning) to reduce overestimation by decomposing the max operation in the target into action selection and action evaluation. We evaluate the greedy policy according to the online network, but we use the target network to estimate its value.  The update is the same as for [DQN](https://paperswithcode.com/method/dqn), but replacing the target $Y^{DQN}\_{t}$ with:

$$ Y^{DoubleDQN}\_{t} = R\_{t+1}+\gamma{Q}\left(S\_{t+1}, \arg\max\_{a}Q\left(S\_{t+1}, a; \theta\_{t}\right);\theta\_{t}^{-}\right) $$

Compared to the original formulation of Double [Q-Learning](https://paperswithcode.com/method/q-learning), in Double DQN the weights of the second network $\theta^{'}\_{t}$ are replaced with the weights of the target network $\theta\_{t}^{-}$ for the evaluation of the current greedy policy.

Double DQN

Deep Reinforcement Learning with Double Q-learning

CANINE

CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation

**ResNet-D** is a modification on the [ResNet](https://paperswithcode.com/method/resnet) architecture that utilises an [average pooling](https://paperswithcode.com/method/average-pooling) tweak for downsampling. The motivation is that in the unmodified ResNet, the 1 × 1 [convolution](https://paperswithcode.com/method/convolution) for the downsampling block ignores 3/4 of input feature maps, so this is modified so no information will be ignored

Source	CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation
Year	2000
Data Source	Papers with Code archive (CC BY-SA)