**DeeBERT** is a method for accelerating [BERT](https://paperswithcode.com/method/bert) inference. It inserts extra classification layers (which are referred to as off-ramps) between each [transformer](https://paperswithcode.com/method/transformer) layer of BERT. All transformer layers and off-ramps are jointly fine-tuned on a given downstream dataset. At inference time, after a sample goes through a transformer layer, it is passed to the following off-ramp. If the off-ramp is confident of the prediction, the result is returned; otherwise, the sample is sent to the next transformer layer.

**Experience Replay** is a replay memory technique used in reinforcement learning where we store the agent’s experiences at each time-step, $e\_{t} = \left(s\_{t}, a\_{t}, r\_{t}, s\_{t+1}\right)$ in a data-set $D = e\_{1}, \cdots, e\_{N}$ , pooled over many episodes into a replay memory. We then usually sample the memory randomly for a minibatch of experience, and use this to learn off-policy, as with Deep Q-Networks. This tackles the problem of autocorrelation leading to unstable training, by making the problem more like a supervised learning problem.

Image Credit: [Hands-On Reinforcement Learning with Python, Sudharsan Ravichandiran](https://subscription.packtpub.com/book/big_data_and_business_intelligence/9781788836524)

Experience Replay

DeeBERT

DeeBERT: Dynamic Early Exiting for Accelerating BERT Inference

**GBlock** is a type of [residual block](https://paperswithcode.com/method/residual-block) used in the [GAN-TTS](https://paperswithcode.com/method/gan-tts) text-to-speech architecture - it is a stack of two residual blocks. As the generator is producing raw audio (e.g. a 2s training clip corresponds
to a sequence of 48000 samples), dilated convolutions are used to ensure that the receptive field of $G$ is large enough to capture long-term dependencies. The four kernel size-3 convolutions in each GBlock have increasing dilation factors: 1, 2, 4, 8. Convolutions are preceded by Conditional Batch Normalisation, conditioned on the linear embeddings of the noise term $z \sim N\left(0, \mathbf{I}\_{128}\right)$ in the single-speaker case, or the concatenation of $z$ and a one-hot representation of the speaker ID in the multi-speaker case. The embeddings are different for
each BatchNorm instance. 

A GBlock contains two skip connections, the first of which in [GAN](https://paperswithcode.com/method/gan)-TTS performs upsampling if the output frequency is higher than the input, and it also contains a size-1 [convolution](https://paperswithcode.com/method/convolution)
if the number of output channels is different from the input.

Source	DeeBERT: Dynamic Early Exiting for Accelerating BERT Inference
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com