41 research outputs found
Learning advanced mathematical computations from examples
Using transformers over large generated datasets, we train models to learn
mathematical properties of differential systems, such as local stability,
behavior at infinity and controllability. We achieve near perfect prediction of
qualitative characteristics, and good approximations of numerical features of
the system. This demonstrates that neural networks can learn to perform complex
computations, grounded in advanced theory, from examples, without built-in
mathematical knowledge
Fader Networks: Manipulating Images by Sliding Attributes
This paper introduces a new encoder-decoder architecture that is trained to
reconstruct images by disentangling the salient information of the image and
the values of attributes directly in the latent space. As a result, after
training, our model can generate different realistic versions of an input image
by varying the attribute values. By using continuous attribute values, we can
choose how much a specific attribute is perceivable in the generated image.
This property could allow for applications where users can modify an image
using sliding knobs, like faders on a mixing console, to change the facial
expression of a portrait, or to update the color of some objects. Compared to
the state-of-the-art which mostly relies on training adversarial networks in
pixel space by altering attribute values at train time, our approach results in
much simpler training schemes and nicely scales to multiple attributes. We
present evidence that our model can significantly change the perceived value of
the attributes while preserving the naturalness of images.Comment: NIPS 201
Towards Knowledge-Based Personalized Product Description Generation in E-commerce
Quality product descriptions are critical for providing competitive customer
experience in an e-commerce platform. An accurate and attractive description
not only helps customers make an informed decision but also improves the
likelihood of purchase. However, crafting a successful product description is
tedious and highly time-consuming. Due to its importance, automating the
product description generation has attracted considerable interests from both
research and industrial communities. Existing methods mainly use templates or
statistical methods, and their performance could be rather limited. In this
paper, we explore a new way to generate the personalized product description by
combining the power of neural networks and knowledge base. Specifically, we
propose a KnOwledge Based pErsonalized (or KOBE) product description generation
model in the context of e-commerce. In KOBE, we extend the encoder-decoder
framework, the Transformer, to a sequence modeling formulation using
self-attention. In order to make the description both informative and
personalized, KOBE considers a variety of important factors during text
generation, including product aspects, user categories, and knowledge base,
etc. Experiments on real-world datasets demonstrate that the proposed method
out-performs the baseline on various metrics. KOBE can achieve an improvement
of 9.7% over state-of-the-arts in terms of BLEU. We also present several case
studies as the anecdotal evidence to further prove the effectiveness of the
proposed approach. The framework has been deployed in Taobao, the largest
online e-commerce platform in China.Comment: KDD 2019 Camera-ready. Website:
https://sites.google.com/view/kobe201