pwc-archive/papers-with-abstracts huggingface dataset:
High-frequency market-making with inventory constraints and directional bets
Authors: Pietro Fodra, Mauricio Labadie
Date: 2012-06-21 00:00:00
Tasks: Vocal Bursts Intensity Prediction

In this paper we extend the market-making models with inventory constraints
of Avellaneda and Stoikov ("High-frequency trading in a limit-order book",
Quantitative Finance Vol.8 No.3 2008) and Gueant, Lehalle and Fernandez-Tapia
("Dealing with inventory risk", Preprint 2011) to the case of a rather general
class of mid-price processes, under either exponential or linear PNL utility
functions, and we add an inventory-risk-aversion parameter that penalises the
marker-maker if she finishes her day with a non-zero inventory. This general,
non-martingale framework allows a market-maker to make directional bets on
market trends whilst keeping under control her inventory risk. In order to
achieve this, the marker-maker places non-symmetric limit orders that favour
market orders to hit her bid (resp. ask) quotes if she expects that prices will
go up (resp. down).
  With this inventory-risk-aversion parameter, the market-maker has not only
direct control on her inventory risk but she also has indirect control on the
moments of her PNL distribution. Therefore, this parameter can be seen as a
fine-tuning of the marker-maker's risk-reward profile.
  In the case of a mean-reverting mid-price, we show numerically that the
inventory-risk-aversion parameter gives the market-maker enough room to tailor
her risk-reward profile, depending on her risk budgets in inventory and PNL
distribution (especially variance, skewness, kurtosis and VaR). For example,
when compared to the martingale benchmark, a market can choose to either
increase her average PNL by more than 15% and carry a huge risk, on inventory
and PNL, or either give up 5% of her benchmark PNL to increase her control on
inventory and PNL, as well as increasing her Sharpe ratio by a factor bigger
than 2.
Neural Network Decoders for Large-Distance 2D Toric Codes
Authors: Xiaotong Ni
Date: 2018-09-18 00:00:00
Tasks: Decoder

We still do not have perfect decoders for topological codes that can satisfy all needs of different experimental setups. Recently, a few neural network based decoders have been studied, with the motivation that they can adapt to a wide range of noise models, and can easily run on dedicated chips without a full-fledged computer. The later feature might lead to fast speed and the ability to operate at low temperatures. However, a question which has not been addressed in previous works is whether neural network decoders can handle 2D topological codes with large distances. In this work, we provide a positive answer for the toric code. The structure of our neural network decoder is inspired by the renormalization group decoder. With a fairly strict policy on training time, when the bit-flip error rate is lower than $9\%$ and syndrome extraction is perfect, the neural network decoder performs better when code distance increases. With a less strict policy, we find it is not hard for the neural decoder to achieve a performance close to the minimum-weight perfect matching algorithm. The numerical simulation is done up to code distance $d=64$. Last but not least, we describe and analyze a few failed approaches. They guide us to the final design of our neural decoder, but also serve as a caution when we gauge the versatility of stock deep neural networks. The source code of our neural decoder can be found at https://github.com/XiaotongNi/toric-code-neural-decoder .
Pipe-SGD: A Decentralized Pipelined SGD Framework for Distributed Deep Net Training
Authors: Youjie Li, Mingchao Yu, Songze Li, Salman Avestimehr, Nam Sung Kim, Alexander Schwing
Date: 2018-11-08 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: GPU

Distributed training of deep nets is an important technique to address some
of the present day computing challenges like memory consumption and
computational demands. Classical distributed approaches, synchronous or
asynchronous, are based on the parameter server architecture, i.e., worker
nodes compute gradients which are communicated to the parameter server while
updated parameters are returned. Recently, distributed training with AllReduce
operations gained popularity as well. While many of those operations seem
appealing, little is reported about wall-clock training time improvements. In
this paper, we carefully analyze the AllReduce based setup, propose timing
models which include network latency, bandwidth, cluster size and compute time,
and demonstrate that a pipelined training with a width of two combines the best
of both synchronous and asynchronous training. Specifically, for a setup
consisting of a four-node GPU cluster we show wall-clock time training
improvements of up to 5.4x compared to conventional approaches.
Demonstrating Advantages of Neuromorphic Computation: A Pilot Study
Authors: Timo Wunderlich, Akos F. Kungl, Eric Müller, Andreas Hartel, Yannik Stradmann, Syed Ahmed Aamir, Andreas Grübl, Arthur Heimbrecht, Korbinian Schreiber, David Stöckel, Christian Pehle, Sebastian Billaudelle, Gerd Kiene, Christian Mauch, Johannes Schemmel, Karlheinz Meier, Mihai A. Petrovici
Date: 2018-11-08 00:00:00

Neuromorphic devices represent an attempt to mimic aspects of the brain's
architecture and dynamics with the aim of replicating its hallmark functional
capabilities in terms of computational power, robust learning and energy
efficiency. We employ a single-chip prototype of the BrainScaleS 2 neuromorphic
system to implement a proof-of-concept demonstration of reward-modulated
spike-timing-dependent plasticity in a spiking network that learns to play the
Pong video game by smooth pursuit. This system combines an electronic
mixed-signal substrate for emulating neuron and synapse dynamics with an
embedded digital processor for on-chip learning, which in this work also serves
to simulate the virtual environment and learning agent. The analog emulation of
neuronal membrane dynamics enables a 1000-fold acceleration with respect to
biological real-time, with the entire chip operating on a power budget of 57mW.
Compared to an equivalent simulation using state-of-the-art software, the
on-chip emulation is at least one order of magnitude faster and three orders of
magnitude more energy-efficient. We demonstrate how on-chip learning can
mitigate the effects of fixed-pattern noise, which is unavoidable in analog
substrates, while making use of temporal variability for action exploration.
Learning compensates imperfections of the physical substrate, as manifested in
neuronal parameter variability, by adapting synaptic weights to match
respective excitability of individual neurons.
GradiVeQ: Vector Quantization for Bandwidth-Efficient Gradient Aggregation in Distributed CNN Training
Authors: Mingchao Yu, Zhifeng Lin, Krishna Narra, Songze Li, Youjie Li, Nam Sung Kim, Alexander Schwing, Murali Annavaram, Salman Avestimehr
Date: 2018-11-08 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Dimensionality Reduction, Quantization

Data parallelism can boost the training speed of convolutional neural
networks (CNN), but could suffer from significant communication costs caused by
gradient aggregation. To alleviate this problem, several scalar quantization
techniques have been developed to compress the gradients. But these techniques
could perform poorly when used together with decentralized aggregation
protocols like ring all-reduce (RAR), mainly due to their inability to directly
aggregate compressed gradients. In this paper, we empirically demonstrate the
strong linear correlations between CNN gradients, and propose a gradient vector
quantization technique, named GradiVeQ, to exploit these correlations through
principal component analysis (PCA) for substantial gradient dimension
reduction. GradiVeQ enables direct aggregation of compressed gradients, hence
allows us to build a distributed learning system that parallelizes GradiVeQ
gradient compression and RAR communications. Extensive experiments on popular
CNNs demonstrate that applying GradiVeQ slashes the wall-clock gradient
aggregation time of the original RAR by more than 5X without noticeable
accuracy loss, and reduces the end-to-end training time by almost 50%. The
results also show that GradiVeQ is compatible with scalar quantization
techniques such as QSGD (Quantized SGD), and achieves a much higher speed-up
gain under the same compression ratio.
Federated Learning for Mobile Keyboard Prediction
Authors: Andrew Hard, Kanishka Rao, Rajiv Mathews, Swaroop Ramaswamy, Françoise Beaufays, Sean Augenstein, Hubert Eichner, Chloé Kiddon, Daniel Ramage
Date: 2018-11-08 00:00:00
Tasks: Federated Learning, Language Modeling, Language Modelling, Prediction

We train a recurrent neural network language model using a distributed,
on-device learning framework called federated learning for the purpose of
next-word prediction in a virtual keyboard for smartphones. Server-based
training using stochastic gradient descent is compared with training on client
devices using the Federated Averaging algorithm. The federated algorithm, which
enables training on a higher-quality dataset for this use case, is shown to
achieve better prediction recall. This work demonstrates the feasibility and
benefit of training language models on client devices without exporting
sensitive user data to servers. The federated learning environment gives users
greater control over the use of their data and simplifies the task of
incorporating privacy by default with distributed training and aggregation
across a population of client devices.
Measuring the Effects of Data Parallelism on Neural Network Training
Authors: Christopher J. Shallue, Jaehoon Lee, Joseph Antognini, Jascha Sohl-Dickstein, Roy Frostig, George E. Dahl
Date: 2018-11-08 00:00:00

Recent hardware developments have dramatically increased the scale of data parallelism available for neural network training. Among the simplest ways to harness next-generation hardware is to increase the batch size in standard mini-batch neural network training algorithms. In this work, we aim to experimentally characterize the effects of increasing the batch size on training time, as measured by the number of steps necessary to reach a goal out-of-sample error. We study how this relationship varies with the training algorithm, model, and data set, and find extremely large variation between workloads. Along the way, we show that disagreements in the literature on how batch size affects model quality can largely be explained by differences in metaparameter tuning and compute budgets at different batch sizes. We find no evidence that larger batch sizes degrade out-of-sample performance. Finally, we discuss the implications of our results on efforts to train neural networks much faster in the future. Our experimental data is publicly available as a database of 71,638,836 loss measurements taken over the course of training for 168,160 individual models across 35 workloads.
Nonlinear Dimension Reduction via Outer Bi-Lipschitz Extensions
Authors: Sepideh Mahabadi, Konstantin Makarychev, Yury Makarychev, Ilya Razenshteyn
Date: 2018-11-08 00:00:00
Tasks: Dimensionality Reduction, LEMMA, Open-Ended Question Answering

We introduce and study the notion of an outer bi-Lipschitz extension of a map
between Euclidean spaces. The notion is a natural analogue of the notion of a
Lipschitz extension of a Lipschitz map. We show that for every map $f$ there
exists an outer bi-Lipschitz extension $f'$ whose distortion is greater than
that of $f$ by at most a constant factor. This result can be seen as a
counterpart of the classic Kirszbraun theorem for outer bi-Lipschitz
extensions. We also study outer bi-Lipschitz extensions of near-isometric maps
and show upper and lower bounds for them. Then, we present applications of our
results to prioritized and terminal dimension reduction problems.
  * We prove a prioritized variant of the Johnson-Lindenstrauss lemma: given a
set of points $X\subset \mathbb{R}^d$ of size $N$ and a permutation ("priority
ranking") of $X$, there exists an embedding $f$ of $X$ into $\mathbb{R}^{O(\log
N)}$ with distortion $O(\log \log N)$ such that the point of rank $j$ has only
$O(\log^{3 + \varepsilon} j)$ non-zero coordinates - more specifically, all but
the first $O(\log^{3+\varepsilon} j)$ coordinates are equal to $0$; the
distortion of $f$ restricted to the first $j$ points (according to the ranking)
is at most $O(\log\log j)$. The result makes a progress towards answering an
open question by Elkin, Filtser, and Neiman about prioritized dimension
reductions.
  * We prove that given a set $X$ of $N$ points in $\mathbb{R}^d$, there exists
a terminal dimension reduction embedding of $\mathbb{R}^d$ into
$\mathbb{R}^{d'}$, where $d' = O\left(\frac{\log N}{\varepsilon^4}\right)$,
which preserves distances $\|x-y\|$ between points $x\in X$ and $y \in
\mathbb{R}^{d}$, up to a multiplicative factor of $1 \pm \varepsilon$. This
improves a recent result by Elkin, Filtser, and Neiman.
  The dimension reductions that we obtain are nonlinear, and this nonlinearity
is necessary.
Large-Scale Visual Active Learning with Deep Probabilistic Ensembles
Authors: Kashyap Chitta, Jose M. Alvarez, Adam Lesnikowski
Date: 2018-11-08 00:00:00
Tasks: Active Learning, General Classification, Semantic Segmentation

Annotating the right data for training deep neural networks is an important
challenge. Active learning using uncertainty estimates from Bayesian Neural
Networks (BNNs) could provide an effective solution to this. Despite being
theoretically principled, BNNs require approximations to be applied to
large-scale problems, where both performance and uncertainty estimation are
crucial. In this paper, we introduce Deep Probabilistic Ensembles (DPEs), a
scalable technique that uses a regularized ensemble to approximate a deep BNN.
We conduct a series of large-scale visual active learning experiments to
evaluate DPEs on classification with the CIFAR-10, CIFAR-100 and ImageNet
datasets, and semantic segmentation with the BDD100k dataset. Our models
require significantly less training data to achieve competitive performances,
and steadily improve upon strong active learning baselines as the annotation
budget is increased.
Intrinsic Geometric Vulnerability of High-Dimensional Artificial Intelligence
Authors: Luca Bortolussi, Guido Sanguinetti
Date: 2018-11-08 00:00:00
Tasks: Vocal Bursts Intensity Prediction

The success of modern Artificial Intelligence (AI) technologies depends
critically on the ability to learn non-linear functional dependencies from
large, high dimensional data sets. Despite recent high-profile successes,
empirical evidence indicates that the high predictive performance is often
paired with low robustness, making AI systems potentially vulnerable to
adversarial attacks. In this report, we provide a simple intuitive argument
suggesting that high performance and vulnerability are intrinsically coupled,
and largely dependent on the geometry of typical, high-dimensional data sets.
Our work highlights a major potential pitfall of modern AI systems, and
suggests practical research directions to ameliorate the problem.
A Geometric Approach of Gradient Descent Algorithms in Linear Neural Networks
Authors: Yacine Chitour, Zhenyu Liao, Romain Couillet
Date: 2018-11-08 00:00:00

In this paper, we propose a geometric framework to analyze the convergence properties of gradient descent trajectories in the context of linear neural networks. We translate a well-known empirical observation of linear neural nets into a conjecture that we call the \emph{overfitting conjecture} which states that, for almost all training data and initial conditions, the trajectory of the corresponding gradient descent system converges to a global minimum. This would imply that the solution achieved by vanilla gradient descent algorithms is equivalent to that of the least-squares estimation, for linear neural networks of an arbitrary number of hidden layers. Built upon a key invariance property induced by the network structure, we first establish convergence of gradient descent trajectories to critical points of the square loss function in the case of linear networks of arbitrary depth. Our second result is the proof of the \emph{overfitting conjecture} in the case of single-hidden-layer linear networks with an argument based on the notion of normal hyperbolicity and under a generic property on the training data (i.e., holding for almost all training data).
Biologically-plausible learning algorithms can scale to large datasets
Authors: Will Xiao, Honglin Chen, Qianli Liao, Tomaso Poggio
Date: 2018-11-08 00:00:00
Proceeding: ICLR 2019 5
Tasks: Biologically-plausible Training

The backpropagation (BP) algorithm is often thought to be biologically
implausible in the brain. One of the main reasons is that BP requires symmetric
weight matrices in the feedforward and feedback pathways. To address this
"weight transport problem" (Grossberg, 1987), two more biologically plausible
algorithms, proposed by Liao et al. (2016) and Lillicrap et al. (2016), relax
BP's weight symmetry requirements and demonstrate comparable learning
capabilities to that of BP on small datasets. However, a recent study by
Bartunov et al. (2018) evaluate variants of target-propagation (TP) and
feedback alignment (FA) on MINIST, CIFAR, and ImageNet datasets, and find that
although many of the proposed algorithms perform well on MNIST and CIFAR, they
perform significantly worse than BP on ImageNet. Here, we additionally evaluate
the sign-symmetry algorithm (Liao et al., 2016), which differs from both BP and
FA in that the feedback and feedforward weights share signs but not magnitudes.
We examine the performance of sign-symmetry and feedback alignment on ImageNet
and MS COCO datasets using different network architectures (ResNet-18 and
AlexNet for ImageNet, RetinaNet for MS COCO). Surprisingly, networks trained
with sign-symmetry can attain classification performance approaching that of
BP-trained networks. These results complement the study by Bartunov et al.
(2018), and establish a new benchmark for future biologically plausible
learning algorithms on more difficult datasets and more complex architectures.
Manifold Learning of Four-dimensional Scanning Transmission Electron Microscopy
Authors: Xin Li, Ondrej E. Dyck, Mark P. Oxley, Andrew R. Lupini, Leland McInnes, John Healy, Stephen Jesse, Sergei V. Kalinin
Date: 2018-10-18 00:00:00

Four-dimensional scanning transmission electron microscopy (4D-STEM) of local
atomic diffraction patterns is emerging as a powerful technique for probing
intricate details of atomic structure and atomic electric fields. However,
efficient processing and interpretation of large volumes of data remain
challenging, especially for two-dimensional or light materials because the
diffraction signal recorded on the pixelated arrays is weak. Here we employ
data-driven manifold leaning approaches for straightforward visualization and
exploration analysis of the 4D-STEM datasets, distilling real-space neighboring
effects on atomically resolved deflection patterns from single-layer graphene,
with single dopant atoms, as recorded on a pixelated detector. These extracted
patterns relate to both individual atom sites and sublattice structures,
effectively discriminating single dopant anomalies via multi-mode views. We
believe manifold learning analysis will accelerate physics discoveries coupled
between data-rich imaging mechanisms and materials such as ferroelectric,
topological spin and van der Waals heterostructures.
Real time Traffic Flow Parameters Prediction with Basic Safety Messages at Low Penetration of Connected Vehicles
Authors: Mizanur Rahman, Mashrur Chowdhury, Jerome McClendon
Date: 2018-11-08 00:00:00
Tasks: Prediction

The expected low market penetration of connected vehicles (CVs) in the near future could be a constraint in estimating traffic flow parameters, such as average travel speed of a roadway segment and average space headway between vehicles from the CV broadcasted data. This estimated traffic flow parameters from low penetration of connected vehicles become noisy compared to 100 percent penetration of CVs, and such noise reduces the real time prediction accuracy of a machine learning model, such as the accuracy of long short term memory (LSTM) model in terms of predicting traffic flow parameters. The accurate prediction of the parameters is important for future traffic condition assessment. To improve the prediction accuracy using noisy traffic flow parameters, which is constrained by limited CV market penetration and limited CV data, we developed a real time traffic data prediction model that combines LSTM with Kalman filter based Rauch Tung Striebel (RTS) noise reduction model. We conducted a case study using the Enhanced Next Generation Simulation (NGSIM) dataset, which contains vehicle trajectory data for every one tenth of a second, to evaluate the performance of this prediction model. Compared to a baseline LSTM model performance, for only 5 percent penetration of CVs, the analyses revealed that combined LSTM and RTS model reduced the mean absolute percentage error (MAPE) from 19 percent to 5 percent for speed prediction and from 27 percent to 9 percent for space-headway prediction. The statistical significance test with a 95 percent confidence interval confirmed no significant difference in predicted average speed and average space headway using this LSTM and RTS combination with only 5 percent CV penetration rate.
Modular Architecture for StarCraft II with Deep Reinforcement Learning
Authors: Dennis Lee, Haoran Tang, Jeffrey O. Zhang, Huazhe Xu, Trevor Darrell, Pieter Abbeel
Date: 2018-11-08 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Starcraft, Starcraft II

We present a novel modular architecture for StarCraft II AI. The architecture
splits responsibilities between multiple modules that each control one aspect
of the game, such as build-order selection or tactics. A centralized scheduler
reviews macros suggested by all modules and decides their order of execution.
An updater keeps track of environment changes and instantiates macros into
series of executable actions. Modules in this framework can be optimized
independently or jointly via human design, planning, or reinforcement learning.
We apply deep reinforcement learning techniques to training two out of six
modules of a modular agent with self-play, achieving 94% or 87% win rates
against the "Harder" (level 5) built-in Blizzard bot in Zerg vs. Zerg matches,
with or without fog-of-war.
Implicit Argument Prediction as Reading Comprehension
Authors: Pengxiang Cheng, Katrin Erk
Date: 2018-11-08 00:00:00
Tasks: Prediction, Reading Comprehension

Implicit arguments, which cannot be detected solely through syntactic cues,
make it harder to extract predicate-argument tuples. We present a new model for
implicit argument prediction that draws on reading comprehension, casting the
predicate-argument tuple with the missing argument as a query. We also draw on
pointer networks and multi-hop computation. Our model shows good performance on
an argument cloze task as well as on a nominal implicit argument prediction
task.
An End-to-end Approach to Semantic Segmentation with 3D CNN and Posterior-CRF in Medical Images
Authors: Shuai Chen, Marleen de Bruijne
Date: 2018-11-08 00:00:00
Tasks: General Classification, Semantic Segmentation

Fully-connected Conditional Random Field (CRF) is often used as
post-processing to refine voxel classification results by encouraging spatial
coherence. In this paper, we propose a new end-to-end training method called
Posterior-CRF. In contrast with previous approaches which use the original
image intensity in the CRF, our approach applies 3D, fully connected CRF to the
posterior probabilities from a CNN and optimizes both CNN and CRF together. The
experiments on white matter hyperintensities segmentation demonstrate that our
method outperforms CNN, post-processing CRF and different end-to-end training
CRF approaches.