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.

Input: Consider Input: We present a simple idea that allows to record a speaker in a given language and synthesize their voice in other languages that they may not even know. These techniques open a wide range of potential applications such as cross-language communication, language learning or automatic video dubbing. We call this general problem multi-language speaker-conditioned speech synthesis and we present a simple but strong baseline for it.

 Our model architecture is similar to the encoder-decoder Char2Wav model or Tacotron. The main difference is that, instead of conditioning on characters or phonemes that are specific to a given language, we condition on a shared phonetic representation that is universal to all languages. This cross-language phonetic representation of text allows to synthesize speech in any language while preserving the vocal characteristics of the original speaker. Furthermore, we show that fine-tuning the weights of our model allows us to extend our results to speakers outside of the training dataset.

Output: We present a simple idea that allows to record a speaker in a given language and synthesize their voice in other languages that they may not even know.


Input: Consider Input: Many large text collections exhibit graph structures, either inherent to the content itself or encoded in the metadata of the individual documents.
 Example graphs extracted from document collections are co-author networks, citation networks, or named-entity-cooccurrence networks.
 Furthermore, social networks can be extracted from email corpora, tweets, or social media. 
 When it comes to visualising these large corpora, either the textual content or the network graph are used.

 In this paper, we propose to incorporate both, text and graph, to not only visualise the semantic information encoded in the documents' content but also the relationships expressed by the inherent network structure.
 To this end, we introduce a novel algorithm based on multi-objective optimisation to jointly position embedded documents and graph nodes in a two-dimensional landscape.
 We illustrate the effectiveness of our approach with real-world datasets and show that we can capture the semantics of large document collections better than other visualisations based on either the content or the network information.

Output: Dimensionality reduction algorithm to visualise text with network information, for example an email corpus or co-authorships.


Input: Consider Input: Capsule networks are constrained by the parameter-expensive nature of their layers, and the general lack of provable equivariance guarantees. We present a variation of capsule networks that aims to remedy this. We identify that learning all pair-wise part-whole relationships between capsules of successive layers is inefficient. Further, we also realise that the choice of prediction networks and the routing mechanism are both key to equivariance. Based on these, we propose an alternative framework for capsule networks that learns to projectively encode the manifold of pose-variations, termed the space-of-variation (SOV), for every capsule-type of each layer. This is done using a trainable, equivariant function defined over a grid of group-transformations. Thus, the prediction-phase of routing involves projection into the SOV of a deeper capsule using the corresponding function. As a specific instantiation of this idea, and also in order to reap the benefits of increased parameter-sharing, we use type-homogeneous group-equivariant convolutions of shallower capsules in this phase. We also introduce an equivariant routing mechanism based on degree-centrality. We show that this particular instance of our general model is equivariant, and hence preserves the compositional representation of an input under transformations. We conduct several experiments on standard object-classification datasets that showcase the increased transformation-robustness, as well as general performance, of our model to several capsule baselines.


Output: A new scalable, group-equivariant model for capsule networks that preserves compositionality under transformations, and is empirically more transformation-robust to older capsule network models.


