PPT-Mineral interpretation results using deep learning with hyperspectral imagery

Andrés Bell Navas Carlos Roberto del Blanco Adán Fernando Jaureguizar Núñez Narciso García Santos María José Jurado Rodrígue z Grupo de Tratamiento de Imágenes GTI Universidad . Quoc V. Le. Stanford University and Google. Purely supervised. Quoc V. . Le. Almost abandoned between 2000-2006. - . Overfitting. , slow, many local minima, gradient vanishing. In 2006, Hinton, et. al. proposed RBMs to . 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.. to Speech . EE 225D - . Audio Signal Processing in Humans and Machines. Oriol Vinyals. UC Berkeley. This is my biased view about deep learning and, more generally, machine learning past and current research!. Aaron Crandall, 2015. What is Deep Learning?. Architectures with more mathematical . transformations from source to target. Sparse representations. Stacking based learning . approaches. Mor. e focus on handling unlabeled data. INCO Innovation Centre. Memorial University. St. John’s, Newfoundland mshaffer@mun.ca. Advanced Techniques in EPMA Seminar . August 7, 2010. University of Oregon. Eugene, Oregon. A brief introduction to the. Professor Qiang Yang. Outline. Introduction. Supervised Learning. Convolutional Neural Network. Sequence Modelling: RNN and its extensions. Unsupervised Learning. Autoencoder. Stacked . Denoising. . 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 . Sarah Dean. Lecture Overview. Types of Samples. Types of Stains. Interpretation of results. Clinical . vs. Research. How do we ensure those results are valid?. Use of controls. UK NEQAS scheme. Types of analyses/statistical tests. 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”. Kexin Pei. 1. , Yinzhi Cao. 2. , Junfeng Yang. 1. , Suman Jana. 1. 1. Columbia University, . 2. Lehigh University. 1. Deep learning (DL) has matched human performance!. Image recognition, speech recognition, machine translation, intrusion detection.... . Hyperspectral Remote Sensing of Urban Tress. . Factsheet # 19. Remote Sensing and Geospatial Application Laboratory, University of Washington, Seattle, WA. Digital version of the fact sheet can be downloaded at: . 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”. Asmitha Rathis. Why Bioinformatics?. Protein structure . Genetic Variants . Anomaly classification . Protein classification. Segmentation/Splicing . Why is Deep Learning beneficial?. scalable with large datasets and are effective in identifying complex patterns from feature-rich datasets . Geological models – “Mineral Systems”. after Kelley et al., 2006 . Supergene : regolith processes. Hypogene : source, pathway, depositional site, outflow. Contrasting physicochemical conditions.