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.
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Question: Now GANs can generate more and more realistic face images that can easily fool human beings.   In contrast, a common convolutional neural network(CNN), e.g. ResNet-18, can achieve more than 99.9% accuracy in discerning fake/real faces if training and testing faces are from the same source. In this paper, we performed both human studies and CNN experiments, which led us to two important findings. One finding is that the textures of fake faces are substantially different from real ones. CNNs can capture local image texture information for recognizing fake/real face, while such cues are easily overlooked by humans. The other finding is that global image texture information is more robust to image editing and generalizable to fake faces from different GANs and datasets. Based on the above findings, we propose  a  novel  architecture  coined  as  Gram-Net,  which  incorporates  “Gram Block” in multiple semantic levels to extract global image texture representations. Experimental results demonstrate that our Gram-Net performs better than existing approaches for fake face detection.   Especially, our Gram-Net is more robust to image editing, e.g.  downsampling, JPEG compression, blur, and noise.   More importantly, our Gram-Net generalizes significantly better in detecting fake faces from GAN models not seen in the training phase.

Answer: An empirical study on fake images reveals that texture is an important cue that current fake images differ from real images. Our improved model capturing global texture statistics shows better cross-GAN fake image detection performance.


Question: Natural language is hierarchically structured: smaller units (e.g., phrases) are nested within larger units (e.g., clauses). When a larger constituent ends, all of the smaller constituents that are nested within it must also be closed. While the standard LSTM architecture allows different neurons to track information at different time scales, it does not have an explicit bias towards modeling a hierarchy of constituents. This paper proposes to add such inductive bias by ordering the neurons; a vector of master input and forget gates ensures that when a given neuron is updated, all the neurons that follow it in the ordering are also updated. Our novel recurrent architecture, ordered neurons LSTM (ON-LSTM), achieves good performance on four different tasks: language modeling, unsupervised parsing, targeted syntactic evaluation, and logical inference.

Answer: We introduce a new inductive bias that integrates tree structures in recurrent neural networks.


Question: Machine learning workloads are often expensive to train, taking weeks to converge. The current generation of frameworks relies on custom back-ends in order to achieve efficiency, making it impractical to train models on less common hardware where no such back-ends exist. Knossos builds on recent work that avoids the need for hand-written libraries, instead compiles machine learning models in much the same way one would compile other kinds of software. In order to make the resulting code efficient, the Knossos complier directly optimises the abstract syntax tree of the program. However in contrast to traditional compilers that employ hand-written optimisation passes, we take a rewriting approach driven by the $A^\star$ search algorithm and a learn value function that evaluates future potential cost reduction of taking various rewriting actions to the program. We show that Knossos can automatically learned optimisations that past compliers had to implement by hand. Furthermore, we demonstrate that Knossos can achieve wall time reduction compared to a hand-tuned compiler on a suite of machine learning programs, including basic linear algebra and convolutional networks. The Knossos compiler has minimal dependencies and can be used on any architecture that supports a \Cpp toolchain. 
 Since cost model the proposed algorithm optimises can be tailored to a particular hardware architecture, the proposed approach can potentially applied to a variety of hardware.

Answer:
We combine A* search with reinforcement learning to speed up machine learning code