Unsupervised word embeddings have become a popular approach of word
representation in NLP tasks. However there are limitations to the semantics
represented by unsupervised embeddings, and inadequate fine-tuning of
embeddings can lead to suboptimal performance. We propose a novel learning
technique called Delta Embedding Learning, which can be applied to general NLP
tasks to improve performance by optimized tuning of the word embeddings. A
structured regularization is applied to the embeddings to ensure they are tuned
in an incremental way. As a result, the tuned word embeddings become better
word representations by absorbing semantic information from supervision without
"forgetting." We apply the method to various NLP tasks and see a consistent
improvement in performance. Evaluation also confirms the tuned word embeddings
have better semantic properties.

提出了一种新颖的学习技术 ——Delta Embedding Learning 以优化调整单词嵌入，并通过结构化正则化确保了单词嵌入的逐步调整，提高了各种 NLP 任务的性能，并证实调整后的单词嵌入具有更好的语义属性。

Delta Embedding Learning

We consider the problem of detecting robotic grasps in an RGB-D view of a
scene containing objects. In this work, we apply a deep learning approach to
solve this problem, which avoids time-consuming hand-design of features. This
presents two main challenges. First, we need to evaluate a huge number of
candidate grasps. In order to make detection fast, as well as robust, we
present a two-step cascaded structure with two deep networks, where the top
detections from the first are re-evaluated by the second. The first network has
fewer features, is faster to run, and can effectively prune out unlikely
candidate grasps. The second, with more features, is slower but has to run only
on the top few detections. Second, we need to handle multimodal inputs well,
for which we present a method to apply structured regularization on the weights
based on multimodal group regularization. We demonstrate that our method
outperforms the previous state-of-the-art methods in robotic grasp detection,
and can be used to successfully execute grasps on two different robotic
platforms.

运用深度学习方法解决机器人抓取问题，通过多模态组规则化结构化正则化权重来处理多模态输入，实现了比之前最先进方法更好的效果，可成功在两个不同的机器人平台上执行抓取。