The state of the art in music source separation employs neural networks
trained in a supervised fashion on multi-track databases to estimate the
sources from a given mixture. With only few datasets available, often extensive
data augmentation is used to combat overfitting. Mixing random tracks, however,
can even reduce separation performance as instruments in real music are
strongly correlated. The key concept in our approach is that source estimates
of an optimal separator should be indistinguishable from real source signals.
Based on this idea, we drive the separator towards outputs deemed as realistic
by discriminator networks that are trained to tell apart real from separator
samples. This way, we can also use unpaired source and mixture recordings
without the drawbacks of creating unrealistic music mixtures. Our framework is
widely applicable as it does not assume a specific network architecture or
number of sources. To our knowledge, this is the first adoption of adversarial
training for music source separation. In a prototype experiment for singing
voice separation, separation performance increases with our approach compared
to purely supervised training.

本文介绍了一种新的音乐源分离算法，使用对抗训练让分离器的输出更加真实，并取得了对于歌声分离的较好效果。

应用于歌声分离的对抗半监督音频源分离技术

Adversarial Semi-Supervised Audio Source Separation applied to Singing  Voice Extraction

In this paper, we introduce the Action Schema Network (ASNet): a neural
network architecture for learning generalised policies for probabilistic
planning problems. By mimicking the relational structure of planning problems,
ASNets are able to adopt a weight-sharing scheme which allows the network to be
applied to any problem from a given planning domain. This allows the cost of
training the network to be amortised over all problems in that domain. Further,
we propose a training method which balances exploration and supervised training
on small problems to produce a policy which remains robust when evaluated on
larger problems. In experiments, we show that ASNet's learning capability
allows it to significantly outperform traditional non-learning planners in
several challenging domains.

介绍了 Action Schema Network (ASNet)：一种用于学习概率规划问题通用策略的神经网络模型。通过模拟规划问题的关系结构，ASNet 能够采用权重共享方案，使网络能够应用于给定规划域中的任何问题。此外，提出一种平衡探索和监督训练的训练方法，以产生在评估较大问题时仍保持稳健的策略。在实验中，我们展示了 ASNet 的学习能力让其在多个具有挑战性的领域中明显优于传统的非学习规划者。

行动模式网络：深度学习的广义策略

Action Schema Networks: Generalised Policies with Deep Learning

The identification and quantification of markers in medical images is
critical for diagnosis, prognosis and management of patients in clinical
practice. Supervised- or weakly supervised training enables the detection of
findings that are known a priori. It does not scale well, and a priori
definition limits the vocabulary of markers to known entities reducing the
accuracy of diagnosis and prognosis. Here, we propose the identification of
anomalies in large-scale medical imaging data using healthy examples as a
reference. We detect and categorize candidates for anomaly findings untypical
for the observed data. A deep convolutional autoencoder is trained on healthy
retinal images. The learned model generates a new feature representation, and
the distribution of healthy retinal patches is estimated by a one-class support
vector machine. Results demonstrate that we can identify pathologic regions in
images without using expert annotations. A subsequent clustering categorizes
findings into clinically meaningful classes. In addition the learned features
outperform standard embedding approaches in a classification task.

提出基于健康样本作为参考来识别大规模医学影像数据中的异常区域，并使用深度卷积自编码器来进行训练和病理区域的分类，结果表明相比于标准嵌入方法，学习特征在分类任务中表现更好。

辨识和分类视网膜成像数据中的异常

Identifying and Categorizing Anomalies in Retinal Imaging Data

We consider the supervised training setting in which we learn task-specific
word embeddings. We assume that we start with initial embeddings learned from
unlabelled data and update them to learn task-specific embeddings for words in
the supervised training data. However, for new words in the test set, we must
use either their initial embeddings or a single unknown embedding, which often
leads to errors. We address this by learning a neural network to map from
initial embeddings to the task-specific embedding space, via a multi-loss
objective function. The technique is general, but here we demonstrate its use
for improved dependency parsing (especially for sentences with
out-of-vocabulary words), as well as for downstream improvements on sentiment
analysis.

该研究在监督式训练中，通过利用神经网络将词向量映射到任务特定的向量空间，以解决测试集中新词的嵌入问题。实验结果表明，该技术可以显著提高依赖分析和情感分析的效果。