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: Unsupervised bilingual dictionary induction (UBDI) is useful for unsupervised machine translation and for cross-lingual transfer of models into low-resource languages. One approach to UBDI is to align word vector spaces in different languages using Generative adversarial networks (GANs) with linear generators, achieving state-of-the-art performance for several language pairs. For some pairs, however, GAN-based induction is unstable or completely fails to align the vector spaces. We focus on cases where linear transformations provably exist, but the performance of GAN-based UBDI depends heavily on the model initialization. We show that the instability depends on the shape and density of the vector sets, but not on noise; it is the result of local optima, but neither over-parameterization nor changing the batch size or the learning rate consistently reduces instability. Nevertheless, we can stabilize GAN-based UBDI through best-of-N model selection, based on an unsupervised stopping criterion.

Answer: An empirical investigation of GAN-based alignment of word vector spaces, focusing on cases, where linear transformations provably exist, but training is unstable.


Question: Understanding the groundbreaking performance of Deep Neural Networks is one
 of the greatest challenges to the scientific community today. In this work, we
 introduce an information theoretic viewpoint on the behavior of deep networks
 optimization processes and their generalization abilities. By studying the Information
 Plane, the plane of the mutual information between the input variable and
 the desired label, for each hidden layer. Specifically, we show that the training of
 the network is characterized by a rapid increase in the mutual information (MI)
 between the layers and the target label, followed by a longer decrease in the MI
 between the layers and the input variable. Further, we explicitly show that these
 two fundamental information-theoretic quantities correspond to the generalization
 error of the network, as a result of introducing a new generalization bound that is
 exponential in the representation compression. The analysis focuses on typical
 patterns of large-scale problems. For this purpose, we introduce a novel analytic
 bound on the mutual information between consecutive layers in the network.
 An important consequence of our analysis is a super-linear boost in training time
 with the number of non-degenerate hidden layers, demonstrating the computational
 benefit of the hidden layers.

Answer: Introduce an information theoretic viewpoint on the behavior of deep networks optimization processes and their generalization abilities


Question: 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.

Answer:
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.