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Chapter and Conference Paper
The Second Conversational Intelligence Challenge (ConvAI2)
We describe the setting and results of the ConvAI2 NeurIPS competition that aims to further the state-of-the-art in open-domain chatbots. Some key takeaways from the competition are: (1) pretrained Transformer...
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Article
Introduction to the special issue on learning semantics
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Article
Learning semantic representations of objects and their parts
Recently, large scale image annotation datasets have been collected with millions of images and thousands of possible annotations. Latent variable models, or embedding methods, that simultaneously learn semant...
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Article
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 natu...
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Chapter and Conference Paper
Deep Learning for Character-Based Information Extraction
In this paper we introduce a deep neural network architecture to perform information extraction on character-based sequences, e.g. named-entity recognition on Chinese text or secondary-structure detection on p...
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Chapter and Conference Paper
Open Question Answering with Weakly Supervised Embedding Models
Building computers able to answer questions on any subject is a long standing goal of artificial intelligence. Promising progress has recently been achieved by methods that learn to map questions to logical fo...
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Chapter
Statistical Learning Theory in Practice
In this chapter we discuss the practical application of statistical learning theory: the design of learning algorithms and their use on real datasets. We review some of the most well-known methods and discuss ...
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Chapter and Conference Paper
Joint Image and Word Sense Discrimination for Image Retrieval
We study the task of learning to rank images given a text query, a problem that is complicated by the issue of multiple senses. That is, the senses of interest are typically the visually distinct concepts that...
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Chapter
Deep Learning via Semi-supervised Embedding
We show how nonlinear semi-supervised embedding algorithms popular for use with “shallow” learning techniques such as kernel methods can be easily applied to deep multi-layer architectures, either as a regular...
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Article
Large scale image annotation: learning to rank with joint word-image embeddings
Image annotation datasets are becoming larger and larger, with tens of millions of images and tens of thousands of possible annotations. We propose a strongly performing method that scales to such datasets by ...
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Article
Learning to rank with (a lot of) word features
In this article we present Supervised Semantic Indexing which defines a class of nonlinear (quadratic) models that are discriminatively trained to directly map from the word content in a query-document or docu...
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Protocol
A User’s Guide to Support Vector Machines
The Support Vector Machine (SVM) is a widely used classifier in bioinformatics. Obtaining the best results with SVMs requires an understanding of their workings and the various ways a user can influence their ...
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Chapter and Conference Paper
Semi-supervised Abstraction-Augmented String Kernel for Multi-level Bio-Relation Extraction
Bio-relation extraction (bRE), an important goal in bio-text mining, involves subtasks identifying relationships between bio-entities in text at multiple levels, e.g., at the article, sentence or relation leve...
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Chapter and Conference Paper
Supervised Semantic Indexing
We present a class of models that are discriminatively trained to directly map from the word content in a query-document or document- document pair to a ranking score. Like Latent Semantic Indexing (LSI), our ...
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Article
Open AccessCombining classifiers for improved classification of proteins from sequence or structure
Predicting a protein's structural or functional class from its amino acid sequence or structure is a fundamental problem in computational biology. Recently, there has been considerable interest in using discri...
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Chapter and Conference Paper
Large-Scale Clustering through Functional Embedding
We present a new framework for large-scale data clustering. The main idea is to modify functional dimensionality reduction techniques to directly optimize over discrete labels using stochastic gradient descent...
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Article
Semi-supervised learning for peptide identification from shotgun proteomics datasets
Shotgun proteomics uses liquid chromatography–tandem mass spectrometry to identify proteins in complex biological samples. We describe an algorithm, called Percolator, for improving the rate of confident pepti...
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Article
Open AccessSVM-Fold: a tool for discriminative multi-class protein fold and superfamily recognition
Predicting a protein's structural class from its amino acid sequence is a fundamental problem in computational biology. Much recent work has focused on develo** new representations for protein sequences, cal...
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Article
Open AccessProtein Ranking by Semi-Supervised Network Propagation
Biologists regularly search DNA or protein databases for sequences that share an evolutionary or functional relationship with a given query sequence. Traditional search methods, such as BLAST and PSI-BLAST, fo...
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Chapter
Embedded Methods
Although many embedded feature selection methods have been introduced during the last few years, a unifying theoretical framework has not been developed to date. We start this chapter by defining such a framew...
