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.

Q: Deterministic models are approximations of reality that are often easier to build and interpret than stochastic alternatives.  
 Unfortunately, as nature is capricious, observational data can never be fully explained by deterministic models in practice.  
 Observation and process noise need to be added to adapt deterministic models to behave stochastically, such that they are capable of explaining and extrapolating from noisy data.
 Adding process noise to deterministic simulators can induce a failure in the simulator resulting in no return value for certain inputs -- a property we describe as ``brittle.''
 We investigate and address the wasted computation that arises from these failures, and the effect of such failures on downstream inference tasks.
 We show that performing inference in this space can be viewed as rejection sampling, and train a conditional normalizing flow as a proposal over noise values such that there is a low probability that the simulator crashes, increasing computational efficiency and inference fidelity for a fixed sample budget when used as the proposal in an approximate inference algorithm.

A: We learn a conditional autoregressive flow to propose perturbations that don't induce simulator failure, improving inference performance.
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Q:     Some conventional transforms such as Discrete Walsh-Hadamard Transform (DWHT) and Discrete Cosine Transform (DCT) have been widely used as feature extractors in image processing but rarely applied in neural networks. However, we found that these conventional transforms have the ability to capture the cross-channel correlations without any learnable parameters in DNNs. This paper firstly proposes to apply conventional transforms on pointwise convolution, showing that such transforms significantly reduce the computational complexity of neural networks without accuracy performance degradation. Especially for DWHT, it requires no floating point multiplications but only additions and subtractions, which can considerably reduce computation overheads. In addition, its fast algorithm further reduces complexity of floating point addition from O(n^2) to O(nlog n). These non-parametric and low computational properties construct extremely efficient networks in the number parameters and operations, enjoying accuracy gain. Our proposed DWHT-based model gained 1.49% accuracy increase with 79.4% reduced parameters and 48.4% reduced FLOPs compared with its baseline model (MoblieNet-V1) on the CIFAR 100 dataset.

A: We introduce new pointwise convolution layers equipped with extremely fast conventional transforms in deep neural network.
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Q: Recurrent Neural Networks (RNNs) are widely used models for sequence data. Just like for feedforward networks, it has become common to build "deep" RNNs, i.e., stack multiple recurrent layers to obtain higher-level abstractions of the data. However, this works only for a handful of layers. Unlike feedforward networks, stacking more than a few recurrent units (e.g., LSTM cells) usually hurts model performance, the reason being vanishing or exploding gradients during training. We investigate the training of multi-layer RNNs and examine the magnitude of the gradients as they propagate through the network. We show that, depending on the structure of the basic recurrent unit, the gradients are systematically attenuated or amplified, so that with an increasing depth they tend to vanish, respectively explode. Based on our analysis we design a new type of gated cell that better preserves gradient magnitude, and therefore makes it possible to train deeper RNNs. We experimentally validate our design with five different sequence modelling tasks on three different datasets. The proposed stackable recurrent (STAR) cell allows for substantially deeper recurrent architectures, with improved performance.

A:
We analyze the gradient propagation in deep RNNs and from our analysis, we propose a new multi-layer deep RNN.
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