A **Connectionist Temporal Classification Loss**, or **CTC Loss**, is designed for tasks where we need alignment between sequences, but where that alignment is difficult - e.g. aligning each character to its location in an audio file. It calculates a loss between a continuous (unsegmented) time series and a target sequence. It does this by summing over the probability of possible alignments of input to target, producing a loss value which is differentiable with respect to each input node. The alignment of input to target is assumed to be “many-to-one”, which limits the length of the target sequence such that it must be $\leq$ the input length.

A resnet-like architecture with deconvolution feature normalization (Ye et al. 2020, ICLR) layers in the first few layers for sparse low-level feature identification, and batch normalization layers in the later layers.

hdxresnet

Predicting galaxy spectra from images with hybrid convolutional neural networks

CTC Loss

**Protagonist Antagonist Induced Regret Environment Design**, or **PAIRED**, is an adversarial method for approximate minimax regret to generate environments for reinforcement learning. It introduces an antagonist which is allied with the environment generating adversary. The primary agent we are trying to train is the protagonist. The environment adversary’s goal is to design environments in which the antagonist achieves high reward and the protagonist receives low reward. If the adversary generates unsolvable environments, the antagonist and protagonist would perform the same and the adversary would get a score of zero, but if the adversary finds environments the antagonist solves and the protagonist does not solve, the adversary achieves a positive score. Thus, the environment adversary is incentivized to create challenging but feasible environments, in which the antagonist can outperform the protagonist. Moreover, as the protagonist learns to solves the simple environments, the antagonist must generate more complex environments to make the protagonist fail, increasing the complexity of the generated tasks and leading to automatic curriculum generation.

Year	2000
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