PPT-Image Caption with Deep Learning

Yulia Kogan and Ron Shiff 19062016 References J Mao W Xu Y Yang J Wang and A L Yuille Explain images with multimodal recurrent neural networks arXiv preprint arXiv14101090 2014. Naiyan. Wang. Outline. Non-NN Approaches. Deep Convex Net. Extreme Learning Machine. PCAnet. Deep Fisher Net (Already . presented before). Discussion. Deep convex net. Each module is a two- layer convex network.. Carey . Nachenberg. Deep Learning for Dummies (Like me) – Carey . Nachenberg. (Like me). The Goal of this Talk?. Deep Learning for Dummies (Like me) – Carey . Nachenberg. 2. To provide you with . Presenter: . Yanming. . Guo. Adviser: Dr. Michael S. Lew. Deep learning. Human. Computer. 1:4. Human . v.s. . Computer. Deep learning. Human. Computer. 1:4. Human . v.s. . Computer. Deep Learning. Why better?. Recognition. Author : . Kaiming. He, . Xiangyu. Zhang, . Shaoqing. Ren, and Jian Sun. (accepted to CVPR 2016). Presenter : . Hyeongseok. Son. The deeper, the better. The deeper network can cover more complex problems. Continuous. Scoring in Practical Applications. Tuesday 6/28/2016. By Greg Makowski. Greg@Ligadata.com. www.Linkedin.com/in/GregMakowski. Community @. . http. ://. Kamanja.org. . . Try out. Future . The Future of Real-Time Rendering?. 1. Deep Learning is Changing the Way We Do Graphics. [Chaitanya17]. [Dahm17]. [Laine17]. [Holden17]. [Karras17]. [Nalbach17]. Video. “. Audio-Driven Facial Animation by Joint End-to-End Learning of Pose and Emotion”. CS 501:CS Seminar. Min Xian. Assistant Professor. Department of Computer Science. University of Idaho. Image from NVIDIA. Researchers:. Geoff Hinton. Yann . LeCun. Andrew Ng. Yoshua. . Bengio. …. Deep Learning for Expression Recognition in Image Sequences Daniel Natanael García Zapata Tutors: Dr. Sergio Escalera Dr. Gholamreza Anbarjafari April 27 2018 Introduction and Goals Introduction Dennis Hamester et al., “Face ExpressionRecognition with a 2-Channel ConvolutionalNeural Network”, International Joint Conference on Neural Networks (IJCNN), 2015. Garima Lalwani Karan Ganju Unnat Jain. Today’s takeaways. Bonus RL recap. Functional Approximation. Deep Q Network. Double Deep Q Network. Dueling Networks. Recurrent DQN. Solving “Doom”. the leading supplier of machine vision vision and image-based barcode reading technology Deployed by the world146s top manufacturers suppliers requirements of each industryCognex solutions help custo School . of Management. New Jersey Institute Of Technology. U. ncontrolled growth of abnormal skin cells. Often caused by ultraviolet radiation from sunshine or tanning beds. Potential Genetic basis for susceptibility. mentor:. . wei. . yang. mentee:. . Ximin. . lin. Deep Neural Networks. Deep Neural Networks. It is possible to fool the deep-learning system . Preliminary study - Identify characters in the image. Topics: 1. st. lecture wrap-up, difficulty training deep networks,. image classification problem, using convolutions,. tricks to train deep networks . . Resources: http://www.cs.utah.edu/~rajeev/cs7960/notes/ . Yonggang Cui. 1. , Zoe N. Gastelum. 2. , Ray Ren. 1. , Michael R. Smith. 2. , . Yuewei. Lin. 1. , Maikael A. Thomas. 2. , . Shinjae. Yoo. 1. , Warren Stern. 1. 1 . Brookhaven National Laboratory, Upton, USA.