pwc-archive/papers-with-abstracts huggingface dataset:
A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning
Authors: Eric Brochu, Vlad M. Cora, Nando de Freitas
Date: 2010-12-12 00:00:00
Tasks: Bayesian Optimization, Hierarchical Reinforcement Learning, Hyperparameter Optimization, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We present a tutorial on Bayesian optimization, a method of finding the
maximum of expensive cost functions. Bayesian optimization employs the Bayesian
technique of setting a prior over the objective function and combining it with
evidence to get a posterior function. This permits a utility-based selection of
the next observation to make on the objective function, which must take into
account both exploration (sampling from areas of high uncertainty) and
exploitation (sampling areas likely to offer improvement over the current best
observation). We also present two detailed extensions of Bayesian optimization,
with experiments---active user modelling with preferences, and hierarchical
reinforcement learning---and a discussion of the pros and cons of Bayesian
optimization based on our experiences.
Soft Sampling for Robust Object Detection
Authors: Zhe Wu, Navaneeth Bodla, Bharat Singh, Mahyar Najibi, Rama Chellappa, Larry S. Davis
Date: 2018-06-18 00:00:00
Tasks: Object, object-detection, Object Detection, Robust Object Detection

We study the robustness of object detection under the presence of missing annotations. In this setting, the unlabeled object instances will be treated as background, which will generate an incorrect training signal for the detector. Interestingly, we observe that after dropping 30% of the annotations (and labeling them as background), the performance of CNN-based object detectors like Faster-RCNN only drops by 5% on the PASCAL VOC dataset. We provide a detailed explanation for this result. To further bridge the performance gap, we propose a simple yet effective solution, called Soft Sampling. Soft Sampling re-weights the gradients of RoIs as a function of overlap with positive instances. This ensures that the uncertain background regions are given a smaller weight compared to the hardnegatives. Extensive experiments on curated PASCAL VOC datasets demonstrate the effectiveness of the proposed Soft Sampling method at different annotation drop rates. Finally, we show that on OpenImagesV3, which is a real-world dataset with missing annotations, Soft Sampling outperforms standard detection baselines by over 3%.
Vertex-Context Sampling for Weighted Network Embedding
Authors: Chih-Ming Chen, Yi-Hsuan Yang, Yi-An Chen, Ming-Feng Tsai
Date: 2017-11-01 00:00:00
Tasks: Information Retrieval, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Network Embedding, Retrieval, Word Similarity

In recent years, network embedding methods have garnered increasing attention
because of their effectiveness in various information retrieval tasks. The goal
is to learn low-dimensional representations of vertexes in an information
network and simultaneously capture and preserve the network structure. Critical
to the performance of a network embedding method is how the edges/vertexes of
the network is sampled for the learning process. Many existing methods adopt a
uniform sampling method to reduce learning complexity, but when the network is
non-uniform (i.e. a weighted network) such uniform sampling incurs information
loss. The goal of this paper is to present a generalized vertex sampling
framework that works seamlessly with most existing network embedding methods to
support weighted instead of uniform vertex/edge sampling. For efficiency, we
propose a delicate sequential vertex-to-context graph data structure, such that
sampling a training pair for learning takes only constant time. For scalability
and memory efficiency, we design the graph data structure in a way that keeps
space consumption low without requiring additional space. In addition to
implementing existing network embedding methods, the proposed framework can be
used to implement extensions that feature high-order proximity modeling and
weighted relation modeling. Experiments conducted on three datasets, including
a commercial large-scale one, verify the effectiveness and efficiency of the
proposed weighted network embedding methods on a variety of tasks, including
word similarity search, multi-label classification, and item recommendation.
Twitter mood predicts the stock market
Authors: Johan Bollen, Huina Mao, Xiao-jun Zeng
Date: 2010-10-14 00:00:00
Tasks: Decision Making, Stock Market Prediction, Time Series, Time Series Analysis

Behavioral economics tells us that emotions can profoundly affect individual
behavior and decision-making. Does this also apply to societies at large, i.e.,
can societies experience mood states that affect their collective decision
making? By extension is the public mood correlated or even predictive of
economic indicators? Here we investigate whether measurements of collective
mood states derived from large-scale Twitter feeds are correlated to the value
of the Dow Jones Industrial Average (DJIA) over time. We analyze the text
content of daily Twitter feeds by two mood tracking tools, namely OpinionFinder
that measures positive vs. negative mood and Google-Profile of Mood States
(GPOMS) that measures mood in terms of 6 dimensions (Calm, Alert, Sure, Vital,
Kind, and Happy). We cross-validate the resulting mood time series by comparing
their ability to detect the public's response to the presidential election and
Thanksgiving day in 2008. A Granger causality analysis and a Self-Organizing
Fuzzy Neural Network are then used to investigate the hypothesis that public
mood states, as measured by the OpinionFinder and GPOMS mood time series, are
predictive of changes in DJIA closing values. Our results indicate that the
accuracy of DJIA predictions can be significantly improved by the inclusion of
specific public mood dimensions but not others. We find an accuracy of 87.6% in
predicting the daily up and down changes in the closing values of the DJIA and
a reduction of the Mean Average Percentage Error by more than 6%.
Stream VByte: Faster Byte-Oriented Integer Compression
Authors: Daniel Lemire, Nathan Kurz, Christoph Rupp
Date: 2017-09-25 00:00:00

Arrays of integers are often compressed in search engines. Though there are
many ways to compress integers, we are interested in the popular byte-oriented
integer compression techniques (e.g., VByte or Google's Varint-GB). They are
appealing due to their simplicity and engineering convenience. Amazon's
varint-G8IU is one of the fastest byte-oriented compression technique published
so far. It makes judicious use of the powerful single-instruction-multiple-data
(SIMD) instructions available in commodity processors. To surpass varint-G8IU,
we present Stream VByte, a novel byte-oriented compression technique that
separates the control stream from the encoded data. Like varint-G8IU, Stream
VByte is well suited for SIMD instructions. We show that Stream VByte decoding
can be up to twice as fast as varint-G8IU decoding over real data sets. In this
sense, Stream VByte establishes new speed records for byte-oriented integer
compression, at times exceeding the speed of the memcpy function. On a 3.4GHz
Haswell processor, it decodes more than 4 billion differentially-coded integers
per second from RAM to L1 cache.
Decoding billions of integers per second through vectorization
Authors: Daniel Lemire, Leonid Boytsov
Date: 2012-09-10 00:00:00
Tasks: CPU

In many important applications -- such as search engines and relational database systems -- data is stored in the form of arrays of integers. Encoding and, most importantly, decoding of these arrays consumes considerable CPU time. Therefore, substantial effort has been made to reduce costs associated with compression and decompression. In particular, researchers have exploited the superscalar nature of modern processors and SIMD instructions. Nevertheless, we introduce a novel vectorized scheme called SIMD-BP128 that improves over previously proposed vectorized approaches. It is nearly twice as fast as the previously fastest schemes on desktop processors (varint-G8IU and PFOR). At the same time, SIMD-BP128 saves up to 2 bits per integer. For even better compression, we propose another new vectorized scheme (SIMD-FastPFOR) that has a compression ratio within 10% of a state-of-the-art scheme (Simple-8b) while being two times faster during decoding.
Vectorized VByte Decoding
Authors: Jeff Plaisance, Nathan Kurz, Daniel Lemire
Date: 2015-02-20 00:00:00
Tasks: Decoder

We consider the ubiquitous technique of VByte compression, which represents
each integer as a variable length sequence of bytes. The low 7 bits of each
byte encode a portion of the integer, and the high bit of each byte is reserved
as a continuation flag. This flag is set to 1 for all bytes except the last,
and the decoding of each integer is complete when a byte with a high bit of 0
is encountered. VByte decoding can be a performance bottleneck especially when
the unpredictable lengths of the encoded integers cause frequent branch
mispredictions. Previous attempts to accelerate VByte decoding using SIMD
vector instructions have been disappointing, prodding search engines such as
Google to use more complicated but faster-to-decode formats for
performance-critical code. Our decoder (Masked VByte) is 2 to 4 times faster
than a conventional scalar VByte decoder, making the format once again
competitive with regard to speed.
A General SIMD-based Approach to Accelerating Compression Algorithms
Authors: Wayne Xin Zhao, Xu-Dong Zhang, Daniel Lemire, Dongdong Shan, Jian-Yun Nie, Hongfei Yan, Ji-Rong Wen
Date: 2015-02-06 00:00:00

Compression algorithms are important for data oriented tasks, especially in
the era of Big Data. Modern processors equipped with powerful SIMD instruction
sets, provide us an opportunity for achieving better compression performance.
Previous research has shown that SIMD-based optimizations can multiply decoding
speeds. Following these pioneering studies, we propose a general approach to
accelerate compression algorithms. By instantiating the approach, we have
developed several novel integer compression algorithms, called Group-Simple,
Group-Scheme, Group-AFOR, and Group-PFD, and implemented their corresponding
vectorized versions. We evaluate the proposed algorithms on two public TREC
datasets, a Wikipedia dataset and a Twitter dataset. With competitive
compression ratios and encoding speeds, our SIMD-based algorithms outperform
state-of-the-art non-vectorized algorithms with respect to decoding speeds.
OpenGM: A C++ Library for Discrete Graphical Models
Authors: Bjoern Andres, Thorsten Beier, Joerg H. Kappes
Date: 2012-06-01 00:00:00
Tasks: C++ code

OpenGM is a C++ template library for defining discrete graphical models and
performing inference on these models, using a wide range of state-of-the-art
algorithms. No restrictions are imposed on the factor graph to allow for
higher-order factors and arbitrary neighborhood structures. Large models with
repetitive structure are handled efficiently because (i) functions that occur
repeatedly need to be stored only once, and (ii) distinct functions can be
implemented differently, using different encodings alongside each other in the
same model. Several parametric functions (e.g. metrics), sparse and dense value
tables are provided and so is an interface for custom C++ code. Algorithms are
separated by design from the representation of graphical models and are easily
exchangeable. OpenGM, its algorithms, HDF5 file format and command line tools
are modular and extendible.
Neural Information Retrieval: A Literature Review
Authors: Ye Zhang, Md Mustafizur Rahman, Alex Braylan, Brandon Dang, Heng-Lu Chang, Henna Kim, Quinten McNamara, Aaron Angert, Edward Banner, Vivek Khetan, Tyler McDonnell, An Thanh Nguyen, Dan Xu, Byron C. Wallace, Matthew Lease
Date: 2016-11-18 00:00:00
Tasks: Information Retrieval, Retrieval, speech-recognition, Speech Recognition

A recent "third wave" of Neural Network (NN) approaches now delivers
state-of-the-art performance in many machine learning tasks, spanning speech
recognition, computer vision, and natural language processing. Because these
modern NNs often comprise multiple interconnected layers, this new NN research
is often referred to as deep learning. Stemming from this tide of NN work, a
number of researchers have recently begun to investigate NN approaches to
Information Retrieval (IR). While deep NNs have yet to achieve the same level
of success in IR as seen in other areas, the recent surge of interest and work
in NNs for IR suggest that this state of affairs may be quickly changing. In
this work, we survey the current landscape of Neural IR research, paying
special attention to the use of learned representations of queries and
documents (i.e., neural embeddings). We highlight the successes of neural IR
thus far, catalog obstacles to its wider adoption, and suggest potentially
promising directions for future research.
Modeling Temporal Dependencies in High-Dimensional Sequences: Application to Polyphonic Music Generation and Transcription
Authors: Nicolas Boulanger-Lewandowski, Yoshua Bengio, Pascal Vincent
Date: 2012-06-27 00:00:00
Tasks: Language Modeling, Language Modelling, Music Generation, Music Modeling

We investigate the problem of modeling symbolic sequences of polyphonic music
in a completely general piano-roll representation. We introduce a probabilistic
model based on distribution estimators conditioned on a recurrent neural
network that is able to discover temporal dependencies in high-dimensional
sequences. Our approach outperforms many traditional models of polyphonic music
on a variety of realistic datasets. We show how our musical language model can
serve as a symbolic prior to improve the accuracy of polyphonic transcription.
GRIM-Filter: Fast Seed Location Filtering in DNA Read Mapping Using Processing-in-Memory Technologies
Authors: Jeremie S. Kim, Damla Senol Cali, Hongyi Xin, Donghyuk Lee, Saugata Ghose, Mohammed Alser, Hasan Hassan, Oguz Ergin, Can Alkan, Onur Mutlu
Date: 2017-11-02 00:00:00

Motivation: Seed location filtering is critical in DNA read mapping, a
process where billions of DNA fragments (reads) sampled from a donor are mapped
onto a reference genome to identify genomic variants of the donor.
State-of-the-art read mappers 1) quickly generate possible mapping locations
for seeds (i.e., smaller segments) within each read, 2) extract reference
sequences at each of the mapping locations, and 3) check similarity between
each read and its associated reference sequences with a
computationally-expensive algorithm (i.e., sequence alignment) to determine the
origin of the read. A seed location filter comes into play before alignment,
discarding seed locations that alignment would deem a poor match. The ideal
seed location filter would discard all poor match locations prior to alignment
such that there is no wasted computation on unnecessary alignments.
  Results: We propose a novel seed location filtering algorithm, GRIM-Filter,
optimized to exploit 3D-stacked memory systems that integrate computation
within a logic layer stacked under memory layers, to perform
processing-in-memory (PIM). GRIM-Filter quickly filters seed locations by 1)
introducing a new representation of coarse-grained segments of the reference
genome, and 2) using massively-parallel in-memory operations to identify read
presence within each coarse-grained segment. Our evaluations show that for a
sequence alignment error tolerance of 0.05, GRIM-Filter 1) reduces the false
negative rate of filtering by 5.59x--6.41x, and 2) provides an end-to-end read
mapper speedup of 1.81x--3.65x, compared to a state-of-the-art read mapper
employing the best previous seed location filtering algorithm.
  Availability: The code is available online at:
https://github.com/CMU-SAFARI/GRIM
NGS Based Haplotype Assembly Using Matrix Completion
Authors: Sina Majidian, MH Kahaei
Date: 2018-01-30 00:00:00
Tasks: Matrix Completion

We use matrix completion methods for haplotype assembly from NGS reads to
develop the new HapSVT, HapNuc, and HapOPT algorithms. This is performed by
applying a mathematical model to convert the reads to an incomplete matrix and
estimating unknown components. This is followed by quantizing and decoding the
completed matrix in order to generate haplotypes. These algorithms are compared
to the recently addressed SDhaP algorithm for the real fosmid data. It is shown
that reconstruction rate and the MSE of these algorithms outperform the SDhaP.
Also, the MEC score of the HapOPT is lower than that of the SDhaP with almost
the same running time.
Practical Bayesian Optimization of Machine Learning Algorithms
Authors: Jasper Snoek, Hugo Larochelle, Ryan P. Adams
Date: 2012-06-13 00:00:00
Proceeding: NeurIPS 2012 12
Tasks: Bayesian Optimization, BIG-bench Machine Learning, Hyperparameter Optimization

Machine learning algorithms frequently require careful tuning of model
hyperparameters, regularization terms, and optimization parameters.
Unfortunately, this tuning is often a "black art" that requires expert
experience, unwritten rules of thumb, or sometimes brute-force search. Much
more appealing is the idea of developing automatic approaches which can
optimize the performance of a given learning algorithm to the task at hand. In
this work, we consider the automatic tuning problem within the framework of
Bayesian optimization, in which a learning algorithm's generalization
performance is modeled as a sample from a Gaussian process (GP). The tractable
posterior distribution induced by the GP leads to efficient use of the
information gathered by previous experiments, enabling optimal choices about
what parameters to try next. Here we show how the effects of the Gaussian
process prior and the associated inference procedure can have a large impact on
the success or failure of Bayesian optimization. We show that thoughtful
choices can lead to results that exceed expert-level performance in tuning
machine learning algorithms. We also describe new algorithms that take into
account the variable cost (duration) of learning experiments and that can
leverage the presence of multiple cores for parallel experimentation. We show
that these proposed algorithms improve on previous automatic procedures and can
reach or surpass human expert-level optimization on a diverse set of
contemporary algorithms including latent Dirichlet allocation, structured SVMs
and convolutional neural networks.
Efficient and accurate extraction of in vivo calcium signals from microendoscopic video data
Authors: Pengcheng Zhou, Shanna L. Resendez, Jose Rodriguez-Romaguera, Jessica C. Jimenez, Shay Q. Neufeld, Garret D. Stuber, Rene Hen, Mazen A. Kheirbek, Bernardo L. Sabatini, Robert E. Kass, Liam Paninski
Date: 2016-05-24 00:00:00

In vivo calcium imaging through microscopes has enabled deep brain imaging of
previously inaccessible neuronal populations within the brains of freely moving
subjects. However, microendoscopic data suffer from high levels of background
fluorescence as well as an increased potential for overlapping neuronal
signals. Previous methods fail in identifying neurons and demixing their
temporal activity because the cellular signals are often submerged in the large
fluctuating background. Here we develop an efficient method to extract cellular
signals with minimal influence from the background. We model the background
with two realistic components: (1) one models the constant baseline and slow
trends of each pixel, and (2) the other models the fast fluctuations from
out-of-focus signals and is therefore constrained to have low spatial-frequency
structure. This decomposition avoids cellular signals being absorbed into the
background term. After subtracting the background approximated with this model,
we use Constrained Nonnegative Matrix Factorization (CNMF, Pnevmatikakis et al.
(2016)) to better demix neural signals and get their denoised and deconvolved
temporal activity. We validate our method on simulated and experimental data,
where it shows fast, reliable, and high quality signal extraction under a wide
variety of imaging parameters.
Online Regret Bounds for Undiscounted Continuous Reinforcement Learning
Authors: Ronald Ortner, Daniil Ryabko
Date: 2013-02-11 00:00:00
Proceeding: NeurIPS 2012 12
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We derive sublinear regret bounds for undiscounted reinforcement learning in
continuous state space. The proposed algorithm combines state aggregation with
the use of upper confidence bounds for implementing optimism in the face of
uncertainty. Beside the existence of an optimal policy which satisfies the
Poisson equation, the only assumptions made are Holder continuity of rewards
and transition probabilities.
Approximate Modified Policy Iteration
Authors: Bruno Scherrer, Victor Gabillon, Mohammad Ghavamzadeh, Matthieu Geist
Date: 2012-05-14 00:00:00
Tasks: General Classification

Modified policy iteration (MPI) is a dynamic programming (DP) algorithm that
contains the two celebrated policy and value iteration methods. Despite its
generality, MPI has not been thoroughly studied, especially its approximation
form which is used when the state and/or action spaces are large or infinite.
In this paper, we propose three implementations of approximate MPI (AMPI) that
are extensions of well-known approximate DP algorithms: fitted-value iteration,
fitted-Q iteration, and classification-based policy iteration. We provide error
propagation analyses that unify those for approximate policy and value
iteration. On the last classification-based implementation, we develop a
finite-sample analysis that shows that MPI's main parameter allows to control
the balance between the estimation error of the classifier and the overall
value function approximation.
Neural Ordinary Differential Equations
Authors: Ricky T. Q. Chen, Yulia Rubanova, Jesse Bettencourt, David Duvenaud
Date: 2018-06-19 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Multivariate Time Series Forecasting, Multivariate Time Series Imputation, Pose Estimation

We introduce a new family of deep neural network models. Instead of specifying a discrete sequence of hidden layers, we parameterize the derivative of the hidden state using a neural network. The output of the network is computed using a black-box differential equation solver. These continuous-depth models have constant memory cost, adapt their evaluation strategy to each input, and can explicitly trade numerical precision for speed. We demonstrate these properties in continuous-depth residual networks and continuous-time latent variable models. We also construct continuous normalizing flows, a generative model that can train by maximum likelihood, without partitioning or ordering the data dimensions. For training, we show how to scalably backpropagate through any ODE solver, without access to its internal operations. This allows end-to-end training of ODEs within larger models.