Concept

Neural machine translation

Related publications (122)

Learning Entailment-Based Sentence Embeddings from Natural Language Inference

James Henderson, Rabeeh Karimi Mahabadi, Jan Frederik Jonas Florian Mai

Large datasets on natural language inference are a potentially valuable resource for inducing semantic representations of natural language sentences. But in many such models the embeddings computed by the sentence encoder goes through an MLP-based interact ...

Idiap2019

Learn to synthesize and synthesize to learn

Jean-Philippe Thiran, Hazim Kemal Ekenel, Mohammad Saeed Rad

Attribute guided face image synthesis aims to manipulate attributes on a face image. Most existing methods for image-to-image translation can either perform a fixed translation between any two image domains using a single attribute or require training data ...

2019

Learn to synthesize and synthesize to learn

Jean-Philippe Thiran, Hazim Kemal Ekenel, Mohammad Saeed Rad

ACADEMIC PRESS INC ELSEVIER SCIENCE2019

Overcoming Multi-model Forgetting

Mathieu Salzmann, Anthony Christopher Davison, Martin Jaggi, Yassine Benyahia, Claudiu-Cristian Musat, Kaicheng Yu

We identify a phenomenon, which we refer to as multi-model forgetting, that occurs when sequentially training multiple deep networks with partially-shared parameters; the performance of previously-trained models degrades as one optimizes a subsequent one, ...

JMLR2019

Hough Forest With Optimized Leaves for Global Hand Pose Estimation With Arbitrary Postures

Daniel Thalmann, Hui Liang, Jun Lee

Vision-based hand pose estimation is important in human-computer interaction. While many recent works focus on full degree-of-freedom hand pose estimation, robust estimation of global hand pose remains a challenging problem. This paper presents a novel alg ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2019

Automatic Segmentation of Nuclei in Histopathology Images using Encoding-Decoding Convolutional Neural Networks

Pascal Frossard, Deniz Mercadier, Beril Besbinar

Accurate and fast segmentation of nuclei in histopathological images plays a crucial role in cancer research for detection and grading, as well as personal treatment. Despite the important efforts, current algorithms are still suboptimal in terms of speed, ...

IEEE2019

LAF-Net: Locally Adaptive Fusion Networks for Stereo Confidence Estimation

Seungryong Kim

We present a novel method that estimates confidence map of an initial disparity by making full use of tri-modal input, including matching cost, disparity, and color image through deep networks. The proposed network, termed as Locally Adaptive Fusion Networ ...

IEEE COMPUTER SOC2019

Ultra-Low-Power Intelligent Acoustic Sensing using Cochlea-Inspired Feature Extraction and DNN Classification

Christian Enz, Minhao Yang

We present our recent progress in ultra-low-power intelligent acoustic sensing that harnesses the high power and energy efficiency of cochlea-like analog feature extraction and binarized neural network classification. Compared with conventional methods inc ...

IEEE2019

Translating Data into Images: The Design Process for the Creation of Visualizations

Dario Rodighiero

The design process is a series of endeavors aimed to solve a problem, which is extensively defined as a need, a task, a situation, etc. Visualizations are objects that result from a design process that solves a problem of unreadable data. They are technica ...

2018

Word Sense Consistency in Statistical and Neural Machine Translation

Xiao Pu

Different senses of source words must often be rendered by different words in the target language when performing machine translation (MT). Selecting the correct translation of polysemous words can be done based on the contexts of use. However, state-of-th ...

EPFL2018

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