LOSSGRAD: automatic learning rate in gradient descent

In this paper, we propose a simple, fast and easy to implement algorithm LOSSGRAD (locally optimal step-size in gradient descent), which automatically modifies the step-size in gradient descent during neural networks training. Given a function $f$, a point $x$, and the gradient $\nabla_x f$ of $f$, we aim to find the step-size $h$ which is (locally) optimal, i.e. satisfies: $$h=arg\,min_{t \geq 0} f(x-t \nabla_x f).$$ Making use of quadratic approximation, we show that the algorithm satisfies the above assumption. We experimentally show that our method is insensitive to the choice of initial learning rate while achieving results comparable to other methods.Comment: TFML 201

Energy Management Programs at the John F. Kennedy Space Center

The Energy Management internship over the summer of 2011 involved a series of projects related to energy management on the John. F. Kennedy Space Center (KSC). This internship saved KSC $14.3 million through budgetary projections, saved KSC$400,000 through implementation of the recycling program, updated KSC Environmental Management System's (EMS) water and energy-related List of Requirements (LoR) which changed 25.7% of the list, provided a incorporated a 45% design review of the Ordnance Operations Facility (OOF) which noted six errors within the design plans, created a certification system and timeline for implementation regarding compliance to the federal Guiding Principles, and gave off-shore wind as the preferred alternative to on-site renewable energy generation

Controllable Image-to-Video Translation: A Case Study on Facial Expression Generation

The recent advances in deep learning have made it possible to generate photo-realistic images by using neural networks and even to extrapolate video frames from an input video clip. In this paper, for the sake of both furthering this exploration and our own interest in a realistic application, we study image-to-video translation and particularly focus on the videos of facial expressions. This problem challenges the deep neural networks by another temporal dimension comparing to the image-to-image translation. Moreover, its single input image fails most existing video generation methods that rely on recurrent models. We propose a user-controllable approach so as to generate video clips of various lengths from a single face image. The lengths and types of the expressions are controlled by users. To this end, we design a novel neural network architecture that can incorporate the user input into its skip connections and propose several improvements to the adversarial training method for the neural network. Experiments and user studies verify the effectiveness of our approach. Especially, we would like to highlight that even for the face images in the wild (downloaded from the Web and the authors' own photos), our model can generate high-quality facial expression videos of which about 50\% are labeled as real by Amazon Mechanical Turk workers.Comment: 10 page

Idomeneo, April 18-21, 2003

This is the concert program of the Boston University Opera Institute and Boston University Chamber Orchestra performance of Idomeneo with music by Wolfgang Amadeus Mozart and libretto by Giambattista Varesco, running Friday, April 18 - Monday, April 21, 2003 at the Boston University Theatre, 264 Huntington Avenue. Digitization for Boston University Concert Programs was supported by the Boston University Humanities Library Endowed Fund