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Year Published

557 Results

August 16, 2018

Constrained Bayesian Optimization with Noisy Experiments

Bayesian Analysis 2018

We derive an expression for expected improvement under greedy batch optimization with noisy observations and noisy constraints, and develop a quasi-Monte Carlo approximation that allows it to be efficiently optimized.

By: Ben Letham, Brian Karrer, Guilherme Ottoni, Eytan Bakshy
August 14, 2018

Deep Appearance Models for Face Rendering

ACM SIGGRAPH

We introduce a deep appearance model for rendering the human face. Inspired by Active Appearance Models, we develop a data-driven rendering pipeline that learns a joint representation of facial geometry and appearance from a multiview capture setup.

By: Stephen Lombardi, Jason Saragih, Tomas Simon, Yaser Sheikh
August 13, 2018

Unsupervised Generation of Free-Form and Parameterized Avatars

TPAMI: SI: ICCV

We study two problems involving the task of mapping images between different domains. The first problem, transfers an image in one domain to an analog image in another domain. The second problem, extends the previous one by mapping an input image to a tied pair, consisting of a vector of parameters and an image that is created using a graphical engine from this vector of parameters.

By: Adam Polyak, Yaniv Taigman, Lior Wolf
July 29, 2018

Online Optical Marker-based Hand Tracking with Deep Labels

Special Interest Group on Computer Graphics and Interactive Techniques (SIGGRAPH)

We propose a technique that frames the labeling problem as a keypoint regression problem conducive to a solution using convolutional neural networks.

By: Shangchen Han, Beibei Liu, Robert Wang, Yuting Ye, Christopher D. Twigg, Kenrick Kin
July 20, 2018

Controllable Abstractive Summarization

ACL NMT Workshop

We present a neural summarization model with a simple but effective mechanism to enable users to specify these high level attributes in order to control the shape of the final summaries to better suit their needs.

By: Angela Fan, David Grangier, Michael Auli
July 18, 2018

What you can cram into a single $&!#* vector: Probing sentence embeddings for linguistic properties

Association for Computational Linguistics (ACL)

We introduce here 10 probing tasks designed to capture simple linguistic features of sentences, and we use them to study embeddings generated by three different encoders trained in eight distinct ways, uncovering intriguing properties of both encoders and training methods.

By: Alexis Conneau, Germán Kruszewski, Guillaume Lample, LoÏc Barrault, Marco Baroni
July 16, 2018

Hearst Patterns Revisited: Automatic Hypernym Detection from Large Text Corpora

Association for Computational Linguistics (ACL)

Methods for unsupervised hypernym detection may broadly be categorized according to two paradigms: pattern-based and distributional methods. In this paper, we study the performance of both approaches on several hypernymy tasks and find that simple pattern-based methods consistently outperform distributional methods on common benchmark datasets.

By: Stephen Roller, Douwe Kiela, Maximilian Nickel
July 15, 2018

Personalizing Dialogue Agents: I have a dog, do you have pets too?

Association for Computational Linguistics (ACL)

Chit-chat models are known to have several problems: they lack specificity, do not display a consistent personality and are often not very captivating. In this work we present the task of making chit-chat more engaging by conditioning on profile information.

By: Saizheng Zhang, Emily Dinan, Jack Urbanek, Arthur Szlam, Douwe Kiela, Jason Weston
July 15, 2018

Hierarchical Neural Story Generation

Association for Computational Linguistics (ACL)

We explore story generation: creative systems that can build coherent and fluent passages of text about a topic. We collect a large dataset of 300K human-written stories paired with writing prompts from an online forum.

By: Angela Fan, Michael Lewis, Yann Dauphin
July 15, 2018

Filtering and Mining Parallel Data in a Joint Multilingual Space

Association for Computational Linguistics (ACL)

We learn a joint multilingual sentence embedding and use the distance between sentences in different languages to filter noisy parallel data and to mine for parallel data in large news collections.

By: Holger Schwenk