Antoine Bordes

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Jason Weston67
Yoshua Bengio50
Christopher D. Manning41
Percy Liang38
Léon Bottou35
48Yoshua Bengio
48Jason Weston
40Jun Zhao
40Kang Liu
40Christopher D. Manning

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1965
2017
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Translating Embeddings for Modeling Multi-relational Data
We consider the problem of embedding entities and relationships of multi-relational data in low-dimensional vector spaces. Our objective is to propose a canonical model which is easy to train, contains a reduced number of parameters and can scale up to very large databases. Hence, we propose TransE, a method which models relationships by interpreting them… (More)
Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach
The exponential increase in the availability of online reviews and recommendations makes sentiment classification an interesting topic in academic and industrial research. Reviews can span so many different domains that it is difficult to gather annotated training data for all of them. Hence, this paper studies the problem of domain adaptation for sentiment… (More)
A semantic matching energy function for learning with multi-relational data
Large-scale relational learning becomes crucial for handling the huge amounts of structured data generated daily in many application domains ranging from computational biology or information retrieval, to natural language processing. In this paper, we present a new neural network architecture designed to embed multi-relational graphs into a flexible… (More)
Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks
One long-term goal of machine learning research is to produce methods that are applicable to reasoning and natural language, in particular building an intelligent dialogue agent. To measure progress towards that goal, we argue for the usefulness of a set of proxy tasks that evaluate reading comprehension via question answering. Our tasks measure… (More)
Learning Structured Embeddings of Knowledge Bases
Many Knowledge Bases (KBs) are now readily available and encompass colossal quantities of information thanks to either a long-term funding effort (e.g. WordNet, OpenCyc) or a col-laborative process (e.g. Freebase, DBpedia). However, each of them is based on a different rigid symbolic framework which makes it hard to use their data in other systems. It is… (More)
The Goldilocks Principle: Reading Children's Books with Explicit Memory Representations
We introduce a new test of how well language models capture meaning in chil-dren's books. Unlike standard language modelling benchmarks, it distinguishes the task of predicting syntactic function words from that of predicting lower-frequency words, which carry greater semantic content. We compare a range of state-of-the-art models, each with a different way… (More)
Fast Kernel Classifiers with Online and Active Learning
Very high dimensional learning systems become theoretically possible when training examples are abundant. The computing cost then becomes the limiting factor. Any efficient learning algorithm should at least take a brief look at each example. But should all examples be given equal attention? This contribution proposes an empirical answer. We first present… (More)
Large-scale Simple Question Answering with Memory Networks
Training large-scale question answering systems is complicated because training sources usually cover a small portion of the range of possible questions. This paper studies the impact of multitask and transfer learning for simple question answering ; a setting for which the reasoning required to answer is quite easy, as long as one can retrieve the correct… (More)
SGD-QN: Careful Quasi-Newton Stochastic Gradient Descent
The SGD-QN algorithm is a stochastic gradient descent algorithm that makes careful use of second-order information and splits the parameter update into independently scheduled components. Thanks to this design, SGD-QN iterates nearly as fast as a first-order stochastic gradient descent but requires less iterations to achieve the same accuracy. This… (More)