PPT-PANDA: Pose Aligned Networks for Deep Attribute Modeling

Author : phoebe-click | Published Date : 2017-09-19

Ning Zhang 12 Manohar Paluri 1 Marć Aurelio Ranzato 1 Trevor Darrell 2 Lumbomir Boudev 1 1 Facebook AI Research 2 EECS UC Berkeley

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PANDA: Pose Aligned Networks for Deep Attribute Modeling: Transcript

Ning Zhang 12 Manohar Paluri 1 Marć Aurelio Ranzato 1 Trevor Darrell 2 Lumbomir Boudev 1 1 Facebook AI Research 2 EECS UC Berkeley. Yu Chen, Tae-. K. yun. Kim, Roberto . Cipolla. Department of Engineering. University of Cambridge. Roadmap. Brief Introductions. Our Framework. Experimental Results. Summary. Motivation. +. 3D Shapes. The red panda has a radial sesamoid or modified thumb. Along with strong curved claws, this Deep Learning @ . UvA. UVA Deep Learning COURSE - Efstratios Gavves & Max Welling. LEARNING WITH NEURAL NETWORKS . - . PAGE . 1. Machine Learning Paradigm for Neural Networks. The Backpropagation algorithm for learning with a neural network. Ning. Zhang. 1,2. . . Manohar. . Paluri. 1. . . Marć. Aurelio . Ranzato. . 1. . Trevor Darrell. 2. . . Lumbomir. . Boudev. 1. . 1. . Facebook AI Research . 2. . EECS, UC Berkeley. Deep Neural Networks . Huan Sun. Dept. of Computer Science, UCSB. March 12. th. , 2012. Major Area Examination. Committee. Prof. . Xifeng. . Yan. Prof. . Linda . Petzold. Prof. . Ambuj. Singh. Marielle . Morris. May . 26, 2017. Project Goals. Attributes: . descriptive . labels. Ex. . a . trotting. horse. , a man with a . pointy. . nose. Identify and track attributes in videos. Focus . on time-dependent traits. Deep . Learning. James K . Baker, Bhiksha Raj. , Rita Singh. Opportunities in Machine Learning. Great . advances are being made in machine learning. Artificial Intelligence. Machine. Learning. After decades of intermittent progress, some applications are beginning to demonstrate human-level performance!. NIPS Highlights. Mike . Mozer. Department of Computer Science and. Institute of Cognitive Science. University of Colorado at Boulder. Y-W . Teh. . –. concrete VAE [discrete variables]. Deep sets. . Jude Shavlik. Yuting. . Liu (TA). Deep Learning (DL). Deep Neural Networks arguably the most exciting current topic in all of CS. Huge industrial and academic impact. Great intellectual challenges. Secada combs | bus-550. AI Superpowers: china, silicon valley, and the new world order. Kai Fu Lee. Author of AI Superpowers. Currently Chairman and CEO of . Sinovation. Ventures and President of . Sinovation. Rendevous. using CNN. Ryan McKennon-Kelly. Sharma, . Sumant. , Connor . Beierle. , and Simone D’Amico. “Pose Estimation for Non-Cooperative Spacecraft Rendezvous Using Convolutional Neural Networks,” September 19, 2018. . NEARC Spring 2013. Brian Hebert, Solutions Architect. www.scribekey.com. . www.scribekey.com . 1. Abstract and Goal. Classic relational database and object-oriented . modeling diagrams, tools, and techniques . What’s new in ANNs in the last 5-10 years?. Deeper networks, . m. ore data, and faster training. Scalability and use of GPUs . ✔. Symbolic differentiation. ✔. reverse-mode automatic differentiation. Management and Radio Performance Improvement. Faris B. Mismar and Brian L. Evans. faris.mismar@utexas.edu. and . bevans@ece.utexas.edu. . MOTIVATION. Self-Organizing Networks. Cellular network faults impact SINR and data rates.

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