paper-writing.html
Please excuse the retro formatting. This page was created back in early days when raw HTML was the norm, for those of us who actually made web pages back then. I haven't bothered to modernize it, figuring it's the content that actually counts. Here are the notes from a presentation I gave at the Stanford InfoLab Friday lunch, 1/27/06, with a few (not many) revisions when I reprised the talk on 12/
Referring Relationships
Images are not simply sets of objects: each image represents a web of interconnected relationships. These relationships between entities carry semantic meaning and help a viewer differentiate between instances of an entity. For example, in an image of a soccer match, there may be multiple persons present, but each participates in different relationships: one is kicking the ball, and the other is g
Adverarial Nets
Generative Adversarial Networks (GANs): a fun new framework for estimating generative models, introduced by Ian Goodfellow et al. We are using a 2-layer network from scalar to scalar (with 30 hidden units and tanh nonlinearities) for modeling both generator and discriminator network. We are trying to reproduce this figure from the paper: Except as a live demo. As we'll see below things are not qui
ConvNetJS: Deep Learning in your browser
Description The library allows you to formulate and solve Neural Networks in Javascript, and was originally written by @karpathy (I am a PhD student at Stanford). However, the library has since been extended by contributions from the community and more are warmly welcome. Current support includes: Common Neural Network modules (fully connected layers, non-linearities) Classification (SVM/Softmax)
Quoc Viet Le
Quoc V. Le Quick links to: All publications (Google Scholar version) -- Media/Awards -- Coauthors -- Professional services -- Cool robot videos Infomation: Current (2013-Present): Research Scientist, Google. Address: 1600 Amphitheatre Parkway Mountain View, CA 94043. Past: PhD Student, AI Lab, Computer Science Department, Stanford University. Advisor: Professor Andrew Ng. Address: Room 110A, Gates
CSCapacity
Eric Roberts March 7, 2016 A History of Capacity Challenges in Computer Science Those who cannot remember the past are condemned to repeat it. —George Santayana, Reason in Common Sense, 1905 Since the 1970s, the number of students graduating with bachelor’s degrees in computer science has fluctuated significantly. As shown in Figure 1, computer science degree production in the United States has ex
Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data
Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data David Hallac, Sagar Vare, Stephen Boyd, Jure Leskovec Stanford University {hallac,svare,boyd,jure}@stanford.edu ABSTRACT Subsequence clustering of multivariate time series is a useful tool for discovering repeated patterns in temporal data. Once these pat- terns have been discovered, seemingly complicated datasets can be
Adam Coates
Formerly: Director of the Silicon Valley AI Lab at Baidu. Post-doctoral researcher at Stanford University in Computer Science. Visiting Scholar: Indiana University, Bloomington in Informatics. Research: Deep learning and feature learning; HPC systems for ML; Perception and vision; Reinforcement learning. E-mail: Twitter: @AdamPaulCoates "Why a four year old child could understand this. Run out and
Visual Relationship Detection with Language Priors
Visual relationships capture a wide variety of interactions between pairs of objects in images (e.g. "man riding bicycle" and "man pushing bicycle"). Consequently, the set of possible relationships is extremely large and it is difficult to obtain sufficient training examples for all possible relationships. Because of this limitation, previous work on visual relationship detection has concentrated
PASCAL-Context Dataset
Training and Validation Set The following download contains only the annotations for the training/validation set and a text file (labels.txt) containing the integer-to-name mapping. trainval.tar.gz (30.7 MB) MD5: df034edb2c12aa7d33b42b20bb1796e3 SHA1: e1375777199dcc02097e68aea755415ca173aa2d Original images for the dataset can be downloaded from the PASCAL VOC 2010 website. Project Specific Downlo
YellowFin: An automatic tuner for momentum SGD
YellowFin and the Art of Momentum Tuning by Jian Zhang, Ioannis Mitliagkas and Chris Ré. TLDR; Hand-tuned momentum SGD is competitive with state-of-the-art adaptive methods, like Adam. We introduce YellowFin, an automatic tuner for the hyperparameters of momentum SGD. YellowFin trains large ResNets and LSTMs in fewer iterations than the state-of-the-art. It performs even better in asynchronous set
Demystifying the American Graduate Admissions Process
© 2010 Karthik Raghunathan Demystifying the American Graduate Admissions Process Karthik Raghunathan Graduate Class of 2010 Stanford Computer Science kr@cs.stanford.edu Abstract This paper attempts to discuss at length the various factors (along with their respective weightage) that the admissions committee at a top graduate school in the US takes into account while reviewing a Master of Science (
Inferring and Executing Programs for Visual Reasoning
Abstract Existing methods for visual reasoning attempt to directly map inputs to outputs using black-box architectures without explicitly modeling the underlying reasoning processes. As a result, these black-box models often learn to exploit biases in the data rather than learning to perform visual reasoning. Inspired by module networks, this paper proposes a model for visual reasoning that consis
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https://cs.stanford.edu/%257Ekaichun/adobeindoornav/
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https://cs.stanford.edu/~acoates/ba_dls_speech2016.pdf
JESC
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https://cs.stanford.edu/people/danqi/papers/emnlp2014.pdf
https://cs.stanford.edu/people/jcjohns/papers/eccv16/JohnsonECCV16Supplementary.pdf