The advent of neural-networks in NLP brought with it substantial improvements
in supervised relation extraction. However, obtaining a sufficient quantity of
training data remains a key challenge. In this work we propose a process for
bootstrapping training datasets which can be performed quickly by
non-NLP-experts. We take advantage of search engines over syntactic-graphs
(Such as Shlain et al. (2020)) which expose a friendly by-example syntax. We
use these to obtain positive examples by searching for sentences that are
syntactically similar to user input examples. We apply this technique to
relations from TACRED and DocRED and show that the resulting models are
competitive with models trained on manually annotated data and on data obtained
from distant supervision. The models also outperform models trained using NLG
data augmentation techniques. Extending the search-based approach with the NLG
method further improves the results.

本文提出了一种用于快速启动训练数据集的过程，利用搜索引擎在句法图上获取正例，我们将此技术应用于 TACRED 和 DocRED 的关系，其结果模型与手动注释数据和远程监督方法所训练的模型相比具有竞争力，并且优于使用 NLG 数据增强技术所训练模型的模型。扩展基于搜索的方法使用 NLG 方法可以进一步提高结果。

使用语法搜索和示例引导关系抽取器

Bootstrapping Relation Extractors using Syntactic Search by Examples

End-to-end data-driven machine learning (ML) of multiple-input
multiple-output (MIMO) systems has been shown to have the potential of
exceeding the performance of engineered MIMO transceivers, without any a priori
knowledge of communication-theoretic principles. In this work, we aim to
understand to what extent and for which scenarios this claim holds true when
comparing with fair benchmarks. We study closed-loop MIMO, open-loop MIMO, and
multi-user MIMO and show that the gains of ML-based communication in the former
two cases can be to a large extent ascribed to implicitly learned geometric
shaping and bit and power allocation, not to learning new spatial encoders. For
MU-MIMO, we demonstrate the feasibility of a novel method with centralized
learning and decentralized executing, outperforming conventional zero-forcing.
For each scenario, we provide explicit descriptions as well as open-source
implementations of the selected neural-network architectures.

本文目的在于探讨在何种场景下，数据驱动的机器学习可以超越传统的 MIMO 收发器，并展示了多种场景中 ML 的性能表现，其中包括闭环 MIMO，开环 MIMO 和多用户 MIMO，并提供了相应神经网络模型的代码实现。

基于端到端学习的 MIMO 物理层通信基准测试

Benchmarking End-to-end Learning of MIMO Physical-Layer Communication

We present a novel approach to learning a point-wise, meaningful embedding
for point-clouds in an unsupervised manner, through the use of neural-networks.
The domain of point-cloud processing via neural-networks is rapidly evolving,
with novel architectures and applications frequently emerging. Within this
field of research, the availability and plethora of unlabeled point-clouds as
well as their possible applications make finding ways of characterizing this
type of data appealing. Though significant advancement was achieved in the
realm of unsupervised learning, its adaptation to the point-cloud
representation is not trivial. Previous research focuses on the embedding of
entire point-clouds representing an object in a meaningful manner. We present a
deep learning framework to learn point-wise description from a set of shapes
without supervision. Our approach leverages self-supervision to define a
relevant loss function to learn rich per-point features. We train a
neural-network with objectives based on context derived directly from the raw
data, with no added annotation. We use local structures of point-clouds to
incorporate geometric information into each point's latent representation. In
addition to using local geometric information, we encourage adjacent points to
have similar representations and vice-versa, creating a smoother, more
descriptive representation. We demonstrate the ability of our method to capture
meaningful point-wise features through three applications. By clustering the
learned embedding space, we perform unsupervised part-segmentation on point
clouds. By calculating euclidean distance in the latent space we derive
semantic point-analogies. Finally, by retrieving nearest-neighbors in our
learned latent space we present meaningful point-correspondence within and
among point-clouds.

本文提出了一种无监督学习点云的有意义的点对点嵌入方法，利用神经网络的自我监督来训练，从而实现对无标签数据进行语义分割。

PointWise: 一种无监督的点级特征学习网络

PointWise: An Unsupervised Point-wise Feature Learning Network

The word2vec software of Tomas Mikolov and colleagues
(this https URL ) has gained a lot of traction lately, and
provides state-of-the-art word embeddings. The learning models behind the
software are described in two research papers. We found the description of the
models in these papers to be somewhat cryptic and hard to follow. While the
motivations and presentation may be obvious to the neural-networks
language-modeling crowd, we had to struggle quite a bit to figure out the
rationale behind the equations.
This note is an attempt to explain equation (4) (negative sampling) in
"Distributed Representations of Words and Phrases and their Compositionality"
by Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado and Jeffrey Dean.

本文旨在解释 Tomas Mikolov 等人在论文 “Distributed Representations of Words and Phrases and their Compositionality” 中的第四个方程（负采样），该论文解释了 word2vec 软件的学习模型及其最新的词嵌入技术。