Institute for Anthropomatics and Robotics Interactive Systems Lab Toward Robust Neural Machine Translation for Noisy Input Sequences Matthias Sperber, Jan Niehues, Alex Waibel

Source URL: workshop2017.iwslt.org

Refining Architectures of Deep Convolutional Neural Networks Sukrit Shankar∗ Duncan Robertson† Yani Ioannou∗ Antonio Criminisi† Roberto Cipolla∗ ∗ Machine Intelligence Lab, University of Cambridge, UK †

Source URL: mi.eng.cam.ac.uk

Two Full Time PhD Positions in Biomedical Engineering at the University of Rhode Island The Neural Processing and Control Lab (NeuralPC Lab) at the University of Rhode Island (URI), Department of Electrical, Computer, an

Source URL: www.ele.uri.edu

Fast Optimization for t-SNE Laurens van der Maaten Department of Computer Science and Engineering, University of California, San Diego 9500 Gilman Drive, La Jolla, CA 92093, USA Pattern Recognition & Bioinformatics Lab,

Source URL: lvdmaaten.github.io

• Multivariate statistics
• Numerical analysis
• Statistics
• Applied mathematics
• Dimension reduction
• T-distributed stochastic neighbor embedding
• Computational statistics
• Nonlinear dimensionality reduction
• Mathematical optimization
• Dimensionality reduction
• Deep learning
• Artificial neural network

A deep-network solution towards model-less obstacle avoidance Lei Tai1 , Shaohua Li2 , Ming Liu1,2 Abstract— Obstacle avoidance is the core problem for mobile robots. Its objective is to allow mobile robots to explore

Source URL: ram-lab.com

• Computational neuroscience
• Artificial intelligence
• Cybernetics
• Neuroscience
• Artificial neural networks
• Formal sciences
• Machine learning
• Computer vision
• Convolutional neural network
• Deep learning
• Robotics
• Pattern recognition

MULTI-LOSS CONVOLUTIONAL NETWORKS FOR GLAND ANALYSIS IN MICROSCOPY A¨ıcha BenTaieb, Jeremy Kawahara and Ghassan Hamarneh Medical Image Analysis Lab, School of Computing Science, Simon Fraser University, Canada ABSTRACT

Source URL: www.cs.sfu.ca

• Computational neuroscience
• Computer vision
• Image segmentation
• Health informatics
• Medical technology
• Medical imaging
• Deep learning
• Convolutional neural network
• Medical image computing
• Market segmentation
• Artificial neural network
• Image analysis

DeepStance at SemEval-2016 Task 6: Detecting Stance in Tweets Using Character and Word-Level CNNs Prashanth Vijayaraghavan, Ivan Sysoev, Soroush Vosoughi and Deb Roy MIT Media Lab, Massachusetts Institute of Technology C

Source URL: socialmachines.media.mit.edu

• Computational neuroscience
• Artificial neural networks
• Machine learning
• Computer vision
• Convolutional neural network
• Deep learning
• Twitter
• Convolution
• Hashtag
• Word2vec
• Tweet
• Pattern recognition

AUTOMATIC DRUM TRANSCRIPTION USING BI-DIRECTIONAL RECURRENT NEURAL NETWORKS Carl Southall, Ryan Stables, Jason Hockman Digital Media Technology Laboratory (DMT Lab) Birmingham City University Birmingham, United Kingdom

Source URL: wp.nyu.edu

• Artificial neural networks
• Recurrent neural network
• Deep learning
• Long short-term memory
• Groove
• Drum kit
• International Society for Music Information Retrieval
• Random neural network

Deep Features to Classify Skin Lesions Jeremy Kawahara, A¨ıcha BenTaieb, and Ghassan Hamarneh Medical Image Analysis Lab, School of Computing Science, Simon Fraser University, Canada ABSTRACT Diagnosing an unknown skin

Source URL: www.cs.sfu.ca

• RTT
• Computational neuroscience
• Computer vision
• Convolutional neural network
• Deep learning
• ImageNet
• Melanoma
• Dermatoscopy
• Support vector machine
• Image segmentation
• K-nearest neighbors algorithm
• Feature

BPR: Bayesian Personalized Ranking from Implicit Feedback Steffen Rendle, Christoph Freudenthaler, Zeno Gantner and Lars Schmidt-Thieme {srendle, freudenthaler, gantner, schmidt-thieme}@ismll.de Machine Learning Lab, Un

Source URL: www.algo.uni-konstanz.de

• Algebra
• Mathematics
• Information science
• Linear algebra
• Matrix theory
• Recommender systems
• Statistical theory
• Collaborative filtering
• Machine learning
• Loss function
• Singular value decomposition
• Artificial neural network