In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.

[EX Q]: Computational imaging systems jointly design computation and hardware to retrieve information which is not traditionally accessible with standard imaging systems. Recently, critical aspects such as experimental design and image priors are optimized through deep neural networks formed by the unrolled iterations of classical physics-based reconstructions (termed physics-based networks). However, for real-world large-scale systems, computing gradients via backpropagation restricts learning due to memory limitations of graphical processing units. In this work, we propose a memory-efficient learning procedure that exploits the reversibility of the network’s layers to enable data-driven design for large-scale computational imaging. We demonstrate our methods practicality on two large-scale systems: super-resolution optical microscopy and multi-channel magnetic resonance imaging.
[EX A]: We propose a memory-efficient learning procedure that exploits the reversibility of the network’s layers to enable data-driven design for large-scale computational imaging.

[EX Q]: Hierarchical label structures widely exist in many machine learning tasks, ranging from those with explicit label hierarchies such as image classification to the ones that have latent label hierarchies such as semantic segmentation. Unfortunately, state-of-the-art methods often utilize cross-entropy loss which in-explicitly assumes the independence among class labels. Motivated by the fact that class members from the same hierarchy need to be similar to each others, we design a new training diagram called Hierarchical Complement Objective Training (HCOT). In HCOT, in addition to maximizing the probability of the ground truth class, we also neutralize the probabilities of rest of the classes in a hierarchical fashion, making the model take advantage of the label hierarchy explicitly. We conduct our method on both image classification and semantic segmentation. Results show that HCOT outperforms state-of-the-art models in CIFAR100, Imagenet, and PASCAL-context. Our experiments also demonstrate that HCOT can be applied on tasks with latent label hierarchies, which is a common characteristic in many machine learning tasks.
[EX A]: We propose Hierarchical Complement Objective Training, a novel training paradigm to effectively leverage category hierarchy in the labeling space on both image classification and semantic segmentation.

[EX Q]: Neural reading comprehension models have recently achieved impressive gener- alisation results, yet still perform poorly when given adversarially selected input. Most prior work has studied semantically invariant text perturbations which cause a model’s prediction to change when it should not. In this work we focus on the complementary problem: excessive prediction undersensitivity where input text is meaningfully changed, and the model’s prediction does not change when it should. We formulate a noisy adversarial attack which searches among semantic variations of comprehension questions for which a model still erroneously pro- duces the same answer as the original question – and with an even higher prob- ability. We show that – despite comprising unanswerable questions – SQuAD2.0 and NewsQA models are vulnerable to this attack and commit a substantial frac- tion of errors on adversarially generated questions. This indicates that current models—even where they can correctly predict the answer—rely on spurious sur- face patterns and are not necessarily aware of all information provided in a given comprehension question. Developing this further, we experiment with both data augmentation and adversarial training as defence strategies: both are able to sub- stantially decrease a model’s vulnerability to undersensitivity attacks on held out evaluation data. Finally, we demonstrate that adversarially robust models gener- alise better in a biased data setting with a train/evaluation distribution mismatch; they are less prone to overly rely on predictive cues only present in the training set and outperform a conventional model in the biased data setting by up to 11% F1.
[EX A]:
We demonstrate vulnerability to undersensitivity attacks in SQuAD2.0 and NewsQA neural reading comprehension models, where the model predicts the same answer with increased confidence to adversarially chosen questions, and compare defence strategies.