pwc-archive/papers-with-abstracts huggingface dataset:
Image Identification Using SIFT Algorithm: Performance Analysis against Different Image Deformations
Authors: Ebrahim Karami, Mohamed Shehata, Andrew Smith
Date: 2017-10-07 00:00:00

Image identification is one of the most challenging tasks in different areas
of computer vision. Scale-invariant feature transform is an algorithm to detect
and describe local features in images to further use them as an image matching
criteria. In this paper, the performance of the SIFT matching algorithm against
various image distortions such as rotation, scaling, fisheye and motion
distortion are evaluated and false and true positive rates for a large number
of image pairs are calculated and presented. We also evaluate the distribution
of the matched keypoint orientation difference for each image deformation.
Investigating the Effect of Music and Lyrics on Spoken-Word Recognition
Authors: Odette Scharenborg, Martha Larson
Date: 2018-03-13 00:00:00

Background music in social interaction settings can hinder conversation. Yet,
little is known of how specific properties of music impact speech processing.
This paper addresses this knowledge gap by investigating 1) whether the masking
effect of background music with lyrics is larger than that of music without
lyrics, and 2) whether the masking effect is larger for more complex music. To
answer these questions, a word identification experiment was run in which Dutch
participants listened to Dutch CVC words embedded in stretches of background
music in two conditions, with and without lyrics, and at three SNRs. Three
songs were used of different genres and complexities. Music stretches with and
without lyrics were sampled from the same song in order to control for factors
beyond the presence of lyrics. The results showed a clear negative impact of
the presence of lyrics in background music on spoken-word recognition. This
impact is independent of complexity. The results suggest that social spaces
(e.g., restaurants, caf\'es and bars) should make careful choices of music to
promote conversation, and open a path for future work.
Combating Reinforcement Learning's Sisyphean Curse with Intrinsic Fear
Authors: Zachary C. Lipton, Kamyar Azizzadenesheli, Abhishek Kumar, Lihong Li, Jianfeng Gao, Li Deng
Date: 2016-11-03 00:00:00
Tasks: Atari Games, Deep Reinforcement Learning, General Classification, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Many practical environments contain catastrophic states that an optimal agent
would visit infrequently or never. Even on toy problems, Deep Reinforcement
Learning (DRL) agents tend to periodically revisit these states upon forgetting
their existence under a new policy. We introduce intrinsic fear (IF), a learned
reward shaping that guards DRL agents against periodic catastrophes. IF agents
possess a fear model trained to predict the probability of imminent
catastrophe. This score is then used to penalize the Q-learning objective. Our
theoretical analysis bounds the reduction in average return due to learning on
the perturbed objective. We also prove robustness to classification errors. As
a bonus, IF models tend to learn faster, owing to reward shaping. Experiments
demonstrate that intrinsic-fear DQNs solve otherwise pathological environments
and improve on several Atari games.
Fractal AI: A fragile theory of intelligence
Authors: Sergio Hernandez Cerezo, Guillem Duran Ballester
Date: 2018-03-13 00:00:00
Tasks: Atari Games, Reinforcement Learning

Fractal AI is a theory for general artificial intelligence. It allows deriving new mathematical tools that constitute the foundations for a new kind of stochastic calculus, by modelling information using cellular automaton-like structures instead of smooth functions. In the repository included we are presenting a new Agent, derived from the first principles of the theory, which is capable of solving Atari games several orders of magnitude more efficiently than other similar techniques, like Monte Carlo Tree Search. The code provided shows how it is now possible to beat some of the current State of The Art benchmarks on Atari games, without previous learning and using less than 1000 samples to calculate each one of the actions when standard MCTS uses 3 Million samples. Among other things, Fractal AI makes it possible to generate a huge database of top performing examples with a very little amount of computation required, transforming Reinforcement Learning into a supervised problem. The algorithm presented is capable of solving the exploration vs exploitation dilemma on both the discrete and continuous cases, while maintaining control over any aspect of the behaviour of the Agent. From a general approach, new techniques presented here have direct applications to other areas such as Non-equilibrium thermodynamics, chemistry, quantum physics, economics, information theory, and non-linear control theory.
SAF- BAGE: Salient Approach for Facial Soft-Biometric Classification - Age, Gender, and Facial Expression
Authors: Ayesha Gurnani, Kenil Shah, Vandit Gajjar, Viraj Mavani, Yash Khandhediya
Date: 2018-03-13 00:00:00
Tasks: Age And Gender Classification, Classification, Gender Classification, General Classification

How can we improve the facial soft-biometric classification with help of the
human visual system? This paper explores the use of saliency which is
equivalent to the human visual system to classify Age, Gender and Facial
Expression soft-biometric for facial images. Using the Deep Multi-level Network
(ML-Net) [1] and off-the-shelf face detector [2], we propose our approach -
SAF-BAGE, which first detects the face in the test image, increases the
Bounding Box (B-Box) margin by 30%, finds the saliency map using ML-Net, with
30% reweighted ratio of saliency map, it multiplies with the input cropped face
and extracts the Convolutional Neural Networks (CNN) predictions on the
multiplied reweighted salient face. Our CNN uses the model AlexNet [3], which
is pre-trained on ImageNet. The proposed approach surpasses the performance of
other approaches, increasing the state-of-the-art by approximately 0.8% on the
widely-used Adience [28] dataset for Age and Gender classification and by
nearly 3% on the recent AffectNet [36] dataset for Facial Expression
classification. We hope our simple, reproducible and effective approach will
help ease future research in facial soft-biometric classification using
saliency.
Analysis of Nonautonomous Adversarial Systems
Authors: Arash Mehrjou
Date: 2018-03-13 00:00:00

Generative adversarial networks are used to generate images but still their
convergence properties are not well understood. There have been a few studies
who intended to investigate the stability properties of GANs as a dynamical
system. This short writing can be seen in that direction. Among the proposed
methods for stabilizing training of GANs, {\ss}-GAN was the first who proposed
a complete annealing strategy to change high-level conditions of the GAN
objective. In this note, we show by a simple example how annealing strategy
works in GANs. The theoretical analysis is supported by simple simulations.
Variational zero-inflated Gaussian processes with sparse kernels
Authors: Pashupati Hegde, Markus Heinonen, Samuel Kaski
Date: 2018-03-13 00:00:00
Tasks: Gaussian Processes, Variational Inference

Zero-inflated datasets, which have an excess of zero outputs, are commonly
encountered in problems such as climate or rare event modelling. Conventional
machine learning approaches tend to overestimate the non-zeros leading to poor
performance. We propose a novel model family of zero-inflated Gaussian
processes (ZiGP) for such zero-inflated datasets, produced by sparse kernels
through learning a latent probit Gaussian process that can zero out kernel rows
and columns whenever the signal is absent. The ZiGPs are particularly useful
for making the powerful Gaussian process networks more interpretable. We
introduce sparse GP networks where variable-order latent modelling is achieved
through sparse mixing signals. We derive the non-trivial stochastic variational
inference tractably for scalable learning of the sparse kernels in both models.
The novel output-sparse approach improves both prediction of zero-inflated data
and interpretability of latent mixing models.
A Weighted Sparse Sampling and Smoothing Frame Transition Approach for Semantic Fast-Forward First-Person Videos
Authors: Michel Melo Silva, Washington Luis Souza Ramos, Joao Klock Ferreira, Felipe Cadar Chamone, Mario Fernando Montenegro Campos, Erickson Rangel Nascimento
Date: 2018-02-23 00:00:00
Proceeding: CVPR 2018
Tasks: Cultural Vocal Bursts Intensity Prediction

Thanks to the advances in the technology of low-cost digital cameras and the
popularity of the self-recording culture, the amount of visual data on the
Internet is going to the opposite side of the available time and patience of
the users. Thus, most of the uploaded videos are doomed to be forgotten and
unwatched in a computer folder or website. In this work, we address the problem
of creating smooth fast-forward videos without losing the relevant content. We
present a new adaptive frame selection formulated as a weighted minimum
reconstruction problem, which combined with a smoothing frame transition method
accelerates first-person videos emphasizing the relevant segments and avoids
visual discontinuities. The experiments show that our method is able to
fast-forward videos to retain as much relevant information and smoothness as
the state-of-the-art techniques in less time. We also present a new 80-hour
multimodal (RGB-D, IMU, and GPS) dataset of first-person videos with
annotations for recorder profile, frame scene, activities, interaction, and
attention.
Early stopping for kernel boosting algorithms: A general analysis with localized complexities
Authors: Yuting Wei, Fanny Yang, Martin J. Wainwright
Date: 2017-07-05 00:00:00
Proceeding: NeurIPS 2017 12

Early stopping of iterative algorithms is a widely-used form of
regularization in statistics, commonly used in conjunction with boosting and
related gradient-type algorithms. Although consistency results have been
established in some settings, such estimators are less well-understood than
their analogues based on penalized regularization. In this paper, for a
relatively broad class of loss functions and boosting algorithms (including
L2-boost, LogitBoost and AdaBoost, among others), we exhibit a direct
connection between the performance of a stopped iterate and the localized
Gaussian complexity of the associated function class. This connection allows us
to show that local fixed point analysis of Gaussian or Rademacher complexities,
now standard in the analysis of penalized estimators, can be used to derive
optimal stopping rules. We derive such stopping rules in detail for various
kernel classes, and illustrate the correspondence of our theory with practice
for Sobolev kernel classes.
Principal Component Analysis with Tensor Train Subspace
Authors: Wenqi Wang, Vaneet Aggarwal, Shuchin Aeron
Date: 2018-03-13 00:00:00

Tensor train is a hierarchical tensor network structure that helps alleviate
the curse of dimensionality by parameterizing large-scale multidimensional data
via a set of network of low-rank tensors. Associated with such a construction
is a notion of Tensor Train subspace and in this paper we propose a TT-PCA
algorithm for estimating this structured subspace from the given data. By
maintaining low rank tensor structure, TT-PCA is more robust to noise comparing
with PCA or Tucker-PCA. This is borne out numerically by testing the proposed
approach on the Extended YaleFace Dataset B.
Efficient and principled score estimation with Nyström kernel exponential families
Authors: Danica J. Sutherland, Heiko Strathmann, Michael Arbel, Arthur Gretton
Date: 2017-05-23 00:00:00
Tasks: Computational Efficiency, Denoising, Density Estimation

We propose a fast method with statistical guarantees for learning an exponential family density model where the natural parameter is in a reproducing kernel Hilbert space, and may be infinite-dimensional. The model is learned by fitting the derivative of the log density, the score, thus avoiding the need to compute a normalization constant. Our approach improves the computational efficiency of an earlier solution by using a low-rank, Nystr\"om-like solution. The new solution retains the consistency and convergence rates of the full-rank solution (exactly in Fisher distance, and nearly in other distances), with guarantees on the degree of cost and storage reduction. We evaluate the method in experiments on density estimation and in the construction of an adaptive Hamiltonian Monte Carlo sampler. Compared to an existing score learning approach using a denoising autoencoder, our estimator is empirically more data-efficient when estimating the score, runs faster, and has fewer parameters (which can be tuned in a principled and interpretable way), in addition to providing statistical guarantees.
A Probabilistic Disease Progression Model for Predicting Future Clinical Outcome
Authors: Yingying Zhu, Mert R. Sabuncu
Date: 2018-03-13 00:00:00
Tasks: Bayesian Inference

In this work, we consider the problem of predicting the course of a
progressive disease, such as cancer or Alzheimer's. Progressive diseases often
start with mild symptoms that might precede a diagnosis, and each patient
follows their own trajectory. Patient trajectories exhibit wild variability,
which can be associated with many factors such as genotype, age, or sex. An
additional layer of complexity is that, in real life, the amount and type of
data available for each patient can differ significantly. For example, for one
patient we might have no prior history, whereas for another patient we might
have detailed clinical assessments obtained at multiple prior time-points. This
paper presents a probabilistic model that can handle multiple modalities
(including images and clinical assessments) and variable patient histories with
irregular timings and missing entries, to predict clinical scores at future
time-points. We use a sigmoidal function to model latent disease progression,
which gives rise to clinical observations in our generative model. We
implemented an approximate Bayesian inference strategy on the proposed model to
estimate the parameters on data from a large population of subjects.
Furthermore, the Bayesian framework enables the model to automatically
fine-tune its predictions based on historical observations that might be
available on the test subject. We applied our method to a longitudinal
Alzheimer's disease dataset with more than 3000 subjects [23] and present a
detailed empirical analysis of prediction performance under different
scenarios, with comparisons against several benchmarks. We also demonstrate how
the proposed model can be interrogated to glean insights about temporal
dynamics in Alzheimer's disease.
Conditional Activation for Diverse Neurons in Heterogeneous Networks
Authors: Albert Lee, Bonnie Lam, Wenyuan Li, Hochul Lee, Wei-Hao Chen, Meng-Fan Chang, Kang. -L. Wang
Date: 2018-03-13 00:00:00

In this paper, we propose a new scheme for modelling the diverse behavior of
neurons. We introduce the conditional activation, in which a neurons activation
function is dynamically modified by a control signal. We apply this method to
recreate behavior of special neurons existing in the human auditory and visual
system. A heterogeneous multilayered perceptron (MLP) incorporating the
developed models demonstrates simultaneous improvement in learning speed and
performance across a various number of hidden units and layers, compared to a
homogeneous network composed of the conventional neuron model. For similar
performance, the proposed model lowers the memory for storing network
parameters significantly.
On the Algebra in Boole's Laws of Thought
Authors: Subhash Kak
Date: 2018-03-13 00:00:00

This article explores the ideas that went into George Boole's development of
an algebra for logical inference in his book The Laws of Thought. We explore in
particular his wife Mary Boole's claim that he was deeply influenced by Indian
logic and argue that his work was more than a framework for processing
propositions. By exploring parallels between his work and Indian logic, we are
able to explain several peculiarities of this work.
LCANet: End-to-End Lipreading with Cascaded Attention-CTC
Authors: Kai Xu, Dawei Li, Nick Cassimatis, Xiaolong Wang
Date: 2018-03-13 00:00:00

Machine lipreading is a special type of automatic speech recognition (ASR)
which transcribes human speech by visually interpreting the movement of related
face regions including lips, face, and tongue. Recently, deep neural network
based lipreading methods show great potential and have exceeded the accuracy of
experienced human lipreaders in some benchmark datasets. However, lipreading is
still far from being solved, and existing methods tend to have high error rates
on the wild data. In this paper, we propose LCANet, an end-to-end deep neural
network based lipreading system. LCANet encodes input video frames using a
stacked 3D convolutional neural network (CNN), highway network and
bidirectional GRU network. The encoder effectively captures both short-term and
long-term spatio-temporal information. More importantly, LCANet incorporates a
cascaded attention-CTC decoder to generate output texts. By cascading CTC with
attention, it partially eliminates the defect of the conditional independence
assumption of CTC within the hidden neural layers, and this yields notably
performance improvement as well as faster convergence. The experimental results
show the proposed system achieves a 1.3% CER and 3.0% WER on the GRID corpus
database, leading to a 12.3% improvement compared to the state-of-the-art
methods.
Being Robust (in High Dimensions) Can Be Practical
Authors: Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, Alistair Stewart
Date: 2017-03-02 00:00:00
Proceeding: ICML 2017 8
Tasks: Vocal Bursts Intensity Prediction

Robust estimation is much more challenging in high dimensions than it is in
one dimension: Most techniques either lead to intractable optimization problems
or estimators that can tolerate only a tiny fraction of errors. Recent work in
theoretical computer science has shown that, in appropriate distributional
models, it is possible to robustly estimate the mean and covariance with
polynomial time algorithms that can tolerate a constant fraction of
corruptions, independent of the dimension. However, the sample and time
complexity of these algorithms is prohibitively large for high-dimensional
applications. In this work, we address both of these issues by establishing
sample complexity bounds that are optimal, up to logarithmic factors, as well
as giving various refinements that allow the algorithms to tolerate a much
larger fraction of corruptions. Finally, we show on both synthetic and real
data that our algorithms have state-of-the-art performance and suddenly make
high-dimensional robust estimation a realistic possibility.
Automatic Pixelwise Object Labeling for Aerial Imagery Using Stacked U-Nets
Authors: Andrew Khalel, Motaz El-Saban
Date: 2018-03-13 00:00:00

Automation of objects labeling in aerial imagery is a computer vision task
with numerous practical applications. Fields like energy exploration require an
automated method to process a continuous stream of imagery on a daily basis. In
this paper we propose a pipeline to tackle this problem using a stack of
convolutional neural networks (U-Net architecture) arranged end-to-end. Each
network works as post-processor to the previous one. Our model outperforms
current state-of-the-art on two different datasets: Inria Aerial Image Labeling
dataset and Massachusetts Buildings dataset each with different characteristics
such as spatial resolution, object shapes and scales. Moreover, we
experimentally validate computation time savings by processing sub-sampled
images and later upsampling pixelwise labeling. These savings come at a
negligible degradation in segmentation quality. Though the conducted
experiments in this paper cover only aerial imagery, the technique presented is
general and can handle other types of images.
Revisiting Classifier Two-Sample Tests
Authors: David Lopez-Paz, Maxime Oquab
Date: 2016-10-20 00:00:00
Tasks: Causal Discovery, Vocal Bursts Valence Prediction

The goal of two-sample tests is to assess whether two samples, $S_P \sim P^n$
and $S_Q \sim Q^m$, are drawn from the same distribution. Perhaps intriguingly,
one relatively unexplored method to build two-sample tests is the use of binary
classifiers. In particular, construct a dataset by pairing the $n$ examples in
$S_P$ with a positive label, and by pairing the $m$ examples in $S_Q$ with a
negative label. If the null hypothesis "$P = Q$" is true, then the
classification accuracy of a binary classifier on a held-out subset of this
dataset should remain near chance-level. As we will show, such Classifier
Two-Sample Tests (C2ST) learn a suitable representation of the data on the fly,
return test statistics in interpretable units, have a simple null distribution,
and their predictive uncertainty allow to interpret where $P$ and $Q$ differ.
The goal of this paper is to establish the properties, performance, and uses of
C2ST. First, we analyze their main theoretical properties. Second, we compare
their performance against a variety of state-of-the-art alternatives. Third, we
propose their use to evaluate the sample quality of generative models with
intractable likelihoods, such as Generative Adversarial Networks (GANs).
Fourth, we showcase the novel application of GANs together with C2ST for causal
discovery.
A Framework for Video-Driven Crowd Synthesis
Authors: Jordan Stadler, Faisal Z. Qureshi
Date: 2018-03-13 00:00:00

We present a framework for video-driven crowd synthesis. Motion vectors
extracted from input crowd video are processed to compute global motion paths.
These paths encode the dominant motions observed in the input video. These
paths are then fed into a behavior-based crowd simulation framework, which is
responsible for synthesizing crowd animations that respect the motion patterns
observed in the video. Our system synthesizes 3D virtual crowds by animating
virtual humans along the trajectories returned by the crowd simulation
framework. We also propose a new metric for comparing the "visual similarity"
between the synthesized crowd and exemplar crowd. We demonstrate the proposed
approach on crowd videos collected under different settings.