162 research outputs found
Entropic optimal transport is maximum-likelihood deconvolution
We give a statistical interpretation of entropic optimal transport by showing
that performing maximum-likelihood estimation for Gaussian deconvolution
corresponds to calculating a projection with respect to the entropic optimal
transport distance. This structural result gives theoretical support for the
wide adoption of these tools in the machine learning community
End-to-End Differentiable Proving
We introduce neural networks for end-to-end differentiable proving of queries
to knowledge bases by operating on dense vector representations of symbols.
These neural networks are constructed recursively by taking inspiration from
the backward chaining algorithm as used in Prolog. Specifically, we replace
symbolic unification with a differentiable computation on vector
representations of symbols using a radial basis function kernel, thereby
combining symbolic reasoning with learning subsymbolic vector representations.
By using gradient descent, the resulting neural network can be trained to infer
facts from a given incomplete knowledge base. It learns to (i) place
representations of similar symbols in close proximity in a vector space, (ii)
make use of such similarities to prove queries, (iii) induce logical rules, and
(iv) use provided and induced logical rules for multi-hop reasoning. We
demonstrate that this architecture outperforms ComplEx, a state-of-the-art
neural link prediction model, on three out of four benchmark knowledge bases
while at the same time inducing interpretable function-free first-order logic
rules.Comment: NIPS 2017 camera-ready, NIPS 201
XNMT: The eXtensible Neural Machine Translation Toolkit
This paper describes XNMT, the eXtensible Neural Machine Translation toolkit.
XNMT distin- guishes itself from other open-source NMT toolkits by its focus on
modular code design, with the purpose of enabling fast iteration in research
and replicable, reliable results. In this paper we describe the design of XNMT
and its experiment configuration system, and demonstrate its utility on the
tasks of machine translation, speech recognition, and multi-tasked machine
translation/parsing. XNMT is available open-source at
https://github.com/neulab/xnmtComment: To be presented at AMTA 2018 Open Source Software Showcas
- …