With the advent of modern AI architectures, a shift has happened towards
end-to-end architectures. This pivot has led to neural architectures being
trained without domain-specific biases/knowledge, optimized according to the
task. We in this paper, learn audio embeddings via diverse feature
representations, in this case, domain-specific. For the case of audio
classification over hundreds of categories of sound, we learn robust separate
embeddings for diverse audio properties such as pitch, timbre, and neural
representation, along with also learning it via an end-to-end architecture. We
observe handcrafted embeddings, e.g., pitch and timbre-based, although on their
own, are not able to beat a fully end-to-end representation, yet adding these
together with end-to-end embedding helps us, significantly improve performance.
This work would pave the way to bring some domain expertise with end-to-end
models to learn robust, diverse representations, surpassing the performance of
just training end-to-end models.

通过领域特定的多样化特征表示，本文学习音频嵌入，比如音高、音色和神经表示等多种属性，并通过端到端架构学习，观察到虽然单独的手工制作嵌入无法超越完全的端到端表示，但将它们与端到端嵌入合并，显著提高性能。这项工作为将领域专业知识与端到端模型结合，学习出鲁棒且多样化的表示方式铺平了道路，超越了仅仅训练端到端模型的性能。

多样化神经音频嵌入：引领特征回来！

Diverse Neural Audio Embeddings -- Bringing Features back !

Text generation aims to produce human-like natural language output for
down-stream tasks. It covers a wide range of applications like machine
translation, document summarization, dialogue generation and so on. Recently
deep neural network-based end-to-end architectures have been widely adopted.
The end-to-end approach conflates all sub-modules, which used to be designed by
complex handcrafted rules, into a holistic encode-decode architecture. Given
enough training data, it is able to achieve state-of-the-art performance yet
avoiding the need of language/domain-dependent knowledge. Nonetheless, deep
learning models are known to be extremely data-hungry, and text generated from
them usually suffer from low diversity, interpretability and controllability.
As a result, it is difficult to trust the output from them in real-life
applications. Deep latent-variable models, by specifying the probabilistic
distribution over an intermediate latent process, provide a potential way of
addressing these problems while maintaining the expressive power of deep neural
networks. This dissertation presents how deep latent-variable models can
improve over the standard encoder-decoder model for text generation.

使用深度潜变量模型进行文本生成可以提高标准编码器 - 解码器模型的效果，同时保持深度神经网络的表达能力。

文本生成的深度潜在变量模型

Deep Latent-Variable Models for Text Generation

Large pre-trained language models (LMs) have been shown to perform
surprisingly well when fine-tuned on tasks that require commonsense and world
knowledge. However, in end-to-end architectures, it is difficult to explain
what is the knowledge in the LM that allows it to make a correct prediction. In
this work, we propose a model for multi-choice question answering, where a
LM-based generator generates a textual hypothesis that is later used by a
classifier to answer the question. The hypothesis provides a window into the
information used by the fine-tuned LM that can be inspected by humans. A key
challenge in this setup is how to constrain the model to generate hypotheses
that are meaningful to humans. We tackle this by (a) joint training with a
simple similarity classifier that encourages meaningful hypotheses, and (b) by
adding loss functions that encourage natural text without repetitions. We show
on several tasks that our model reaches performance that is comparable to
end-to-end architectures, while producing hypotheses that elucidate the
knowledge used by the LM for answering the question.

本论文提出了一种模型，通过生成文本提供细节信息，使多选题回答模型更容易理解，并采用多种损失函数来鼓励自然文本输出，从而达到与端到端架构相当的性能水平。

通过文本生成解释问答模型

Explaining Question Answering Models through Text Generation

The prediction of salient areas in images has been traditionally addressed
with hand-crafted features based on neuroscience principles. This paper,
however, addresses the problem with a completely data-driven approach by
training a convolutional neural network (convnet). The learning process is
formulated as a minimization of a loss function that measures the Euclidean
distance of the predicted saliency map with the provided ground truth. The
recent publication of large datasets of saliency prediction has provided enough
data to train end-to-end architectures that are both fast and accurate. Two
designs are proposed: a shallow convnet trained from scratch, and a another
deeper solution whose first three layers are adapted from another network
trained for classification. To the authors knowledge, these are the first
end-to-end CNNs trained and tested for the purpose of saliency prediction.

本文提出了一种使用卷积神经网络（Convnet）进行显著区域预测的全数据驱动方法，其训练过程通过损失函数对 Euclidean 距离进行测量，利用大量的显著性预测数据进行端到端的快速和精确的架构设计，并提出了两种模型架构解决方案，是首个训练和测试用于显著性预测的端到端卷积神经网络。