Object detection and recognition are fundamental functions underlying the
success of species. Because the appearance of an object exhibits a large
variability, the brain has to group these different stimuli under the same
object identity, a process of generalization. Does the process of
generalization follow some general principles or is it an ad-hoc
"bag-of-tricks"? The Universal Law of Generalization provided evidence that
generalization follows similar properties across a variety of species and
tasks. Here we test the hypothesis that the internal representations underlying
generalization reflect the natural properties of object detection and
recognition in our environment rather than the specifics of the system solving
these problems. By training a deep-neural-network with images of "clear" and
"camouflaged" animals, we found that with a proper choice of category
prototypes, the generalization functions are monotone decreasing, similar to
the generalization functions of biological systems. Our findings support the
hypothesis of the study.

本文通过训练深度神经网络，并选取合适的类别原型，在动物的辨识中测试其广义化能力，结果显示其泛化函数随着类别数量的增加而逐渐递减，这一发现支持了研究的假设，即广义化过程反映了物体检测和识别的自然特性。

目标检测、识别、深度学习与普适性归纳法则

Object Detection, Recognition, Deep Learning, and the Universal Law of Generalization

A new deep-neural-network (DNN) based error correction encoder architecture
for channels with feedback, called Deep Extended Feedback (DEF), is presented
in this paper. The encoder in the DEF architecture transmits an information
message followed by a sequence of parity symbols which are generated based on
the message as well as the observations of the past forward channel outputs
sent to the transmitter through a feedback channel. DEF codes generalize
Deepcode [1] in several ways: parity symbols are generated based on
forward-channel output observations over longer time intervals in order to
provide better error correction capability; and high-order modulation formats
are deployed in the encoder so as to achieve increased spectral efficiency.
Performance evaluations show that DEF codes have better performance compared to
other DNN-based codes for channels with feedback.

本文提出了一种基于深度神经网络的误差校正编码器架构，名为 Deep Extended Feedback（DEF），旨在提高带反馈信道传输的编码效率和纠错能力，并且比其他深度神经网络编码方法更优秀。

深度延伸反馈码

Deep Extended Feedback Codes

We present an automatic piano transcription system that converts polyphonic
audio recordings into musical scores. This has been a long-standing problem of
music information processing, and recent studies have made remarkable progress
in the two main component techniques: multipitch detection and rhythm
quantization. Given this situation, we study a method integrating
deep-neural-network-based multipitch detection and statistical-model-based
rhythm quantization. In the first part, we conducted systematic evaluations and
found that while the present method achieved high transcription accuracies at
the note level, some global characteristics of music, such as tempo scale,
metre (time signature), and bar line positions, were often incorrectly
estimated. In the second part, we formulated non-local statistics of pitch and
rhythmic contents that are derived from musical knowledge and studied their
effects in inferring those global characteristics. We found that these
statistics are markedly effective for improving the transcription results and
that their optimal combination includes statistics obtained from separated hand
parts. The integrated method had an overall transcription error rate of 7.1%
and a downbeat F-measure of 85.6% on a dataset of popular piano music, and the
generated transcriptions can be partially used for music performance and
assisting human transcribers, thus demonstrating the potential for practical
applications.

提出了一种自动钢琴转录系统，该系统利用基于深度神经网络的多音高检测和基于统计模型的节奏量化相结合的方法提高了音乐信息处理的准确性，在系统性评估中发现一些全局特征误差较大，而由音乐知识推导的音高和节奏内容的非局部统计显著提高了转录结果的准确性。

音乐非局部统计作为音频转谱钢琴转录的指导

Non-Local Musical Statistics as Guides for Audio-to-Score Piano  Transcription

We present a Bayesian approach to adapting parameters of a well-trained
context-dependent, deep-neural-network, hidden Markov model (CD-DNN-HMM) to
improve automatic speech recognition performance. Given an abundance of DNN
parameters but with only a limited amount of data, the effectiveness of the
adapted DNN model can often be compromised. We formulate maximum a posteriori
(MAP) adaptation of parameters of a specially designed CD-DNN-HMM with an
augmented linear hidden networks connected to the output tied states, or
senones, and compare it to feature space MAP linear regression previously
proposed. Experimental evidences on the 20,000-word open vocabulary Wall Street
Journal task demonstrate the feasibility of the proposed framework. In
supervised adaptation, the proposed MAP adaptation approach provides more than
10% relative error reduction and consistently outperforms the conventional
transformation based methods. Furthermore, we present an initial attempt to
generate hierarchical priors to improve adaptation efficiency and effectiveness
with limited adaptation data by exploiting similarities among senones.

我们提出了一种贝叶斯方法，用于调整已训练好的文本相关、深度神经网络、隐马尔可夫模型（CD-DNN-HMM）的参数，以提高自动语音识别性能。