4 research outputs found
Recurrent and Contextual Models for Visual Question Answering
We propose a series of recurrent and contextual neural network models for
multiple choice visual question answering on the Visual7W dataset. Motivated by
divergent trends in model complexities in the literature, we explore the
balance between model expressiveness and simplicity by studying incrementally
more complex architectures. We start with LSTM-encoding of input questions and
answers; build on this with context generation by LSTM-encodings of neural
image and question representations and attention over images; and evaluate the
diversity and predictive power of our models and the ensemble thereof. All
models are evaluated against a simple baseline inspired by the current
state-of-the-art, consisting of involving simple concatenation of bag-of-words
and CNN representations for the text and images, respectively. Generally, we
observe marked variation in image-reasoning performance between our models not
obvious from their overall performance, as well as evidence of dataset bias.
Our standalone models achieve accuracies up to , while the ensemble of
all models achieves the best accuracy of , within of the
current state-of-the-art for Visual7W
Using machine learning for medium frequency derivative portfolio trading
We use machine learning for designing a medium frequency trading strategy for
a portfolio of 5 year and 10 year US Treasury note futures. We formulate this
as a classification problem where we predict the weekly direction of movement
of the portfolio using features extracted from a deep belief network trained on
technical indicators of the portfolio constituents. The experimentation shows
that the resulting pipeline is effective in making a profitable trade
SnapVX: A Network-Based Convex Optimization Solver
SnapVX is a high-performance Python solver for convex optimization problems
defined on networks. For these problems, it provides a fast and scalable
solution with guaranteed global convergence. SnapVX combines the capabilities
of two open source software packages: Snap.py and CVXPY. Snap.py is a large
scale graph processing library, and CVXPY provides a general modeling framework
for small-scale subproblems. SnapVX offers a customizable yet easy-to-use
interface with out-of-the-box functionality. Based on the Alternating Direction
Method of Multipliers (ADMM), it is able to efficiently store, analyze, and
solve large optimization problems from a variety of different applications.
Documentation, examples, and more can be found on the SnapVX website at
http://snap.stanford.edu/snapvx
Driver Identification Using Automobile Sensor Data from a Single Turn
As automotive electronics continue to advance, cars are becoming more and
more reliant on sensors to perform everyday driving operations. These sensors
are omnipresent and help the car navigate, reduce accidents, and provide
comfortable rides. However, they can also be used to learn about the drivers
themselves. In this paper, we propose a method to predict, from sensor data
collected at a single turn, the identity of a driver out of a given set of
individuals. We cast the problem in terms of time series classification, where
our dataset contains sensor readings at one turn, repeated several times by
multiple drivers. We build a classifier to find unique patterns in each
individual's driving style, which are visible in the data even on such a short
road segment. To test our approach, we analyze a new dataset collected by AUDI
AG and Audi Electronics Venture, where a fleet of test vehicles was equipped
with automotive data loggers storing all sensor readings on real roads. We show
that turns are particularly well-suited for detecting variations across
drivers, especially when compared to straightaways. We then focus on the 12
most frequently made turns in the dataset, which include rural, urban, highway
on-ramps, and more, obtaining accurate identification results and learning
useful insights about driver behavior in a variety of settings