pwc-archive/papers-with-abstracts huggingface dataset:
Communication-efficient distributed SGD with Sketching
Authors: Nikita Ivkin, Daniel Rothchild, Enayat Ullah, Vladimir Braverman, Ion Stoica, Raman Arora
Date: 2019-03-12 00:00:00
Proceeding: NeurIPS 2019 12

Large-scale distributed training of neural networks is often limited by network bandwidth, wherein the communication time overwhelms the local computation time. Motivated by the success of sketching methods in sub-linear/streaming algorithms, we introduce Sketched SGD, an algorithm for carrying out distributed SGD by communicating sketches instead of full gradients. We show that Sketched SGD has favorable convergence rates on several classes of functions. When considering all communication -- both of gradients and of updated model weights -- Sketched SGD reduces the amount of communication required compared to other gradient compression methods from $\mathcal{O}(d)$ or $\mathcal{O}(W)$ to $\mathcal{O}(\log d)$, where $d$ is the number of model parameters and $W$ is the number of workers participating in training. We run experiments on a transformer model, an LSTM, and a residual network, demonstrating up to a 40x reduction in total communication cost with no loss in final model performance. We also show experimentally that Sketched SGD scales to at least 256 workers without increasing communication cost or degrading model performance.
Multi-Agent Deep Reinforcement Learning for Large-scale Traffic Signal Control
Authors: Tianshu Chu, Jie Wang, Lara Codecà, Zhaojian Li
Date: 2019-03-11 00:00:00
Tasks: Deep Reinforcement Learning, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Traffic Signal Control

Reinforcement learning (RL) is a promising data-driven approach for adaptive
traffic signal control (ATSC) in complex urban traffic networks, and deep
neural networks further enhance its learning power. However, centralized RL is
infeasible for large-scale ATSC due to the extremely high dimension of the
joint action space. Multi-agent RL (MARL) overcomes the scalability issue by
distributing the global control to each local RL agent, but it introduces new
challenges: now the environment becomes partially observable from the viewpoint
of each local agent due to limited communication among agents. Most existing
studies in MARL focus on designing efficient communication and coordination
among traditional Q-learning agents. This paper presents, for the first time, a
fully scalable and decentralized MARL algorithm for the state-of-the-art deep
RL agent: advantage actor critic (A2C), within the context of ATSC. In
particular, two methods are proposed to stabilize the learning procedure, by
improving the observability and reducing the learning difficulty of each local
agent. The proposed multi-agent A2C is compared against independent A2C and
independent Q-learning algorithms, in both a large synthetic traffic grid and a
large real-world traffic network of Monaco city, under simulated peak-hour
traffic dynamics. Results demonstrate its optimality, robustness, and sample
efficiency over other state-of-the-art decentralized MARL algorithms.
Embarrassingly parallel MCMC using deep invertible transformations
Authors: Diego Mesquita, Paul Blomstedt, Samuel Kaski
Date: 2019-03-11 00:00:00
Tasks: Bayesian Inference

While MCMC methods have become a main work-horse for Bayesian inference, scaling them to large distributed datasets is still a challenge. Embarrassingly parallel MCMC strategies take a divide-and-conquer stance to achieve this by writing the target posterior as a product of subposteriors, running MCMC for each of them in parallel and subsequently combining the results. The challenge then lies in devising efficient aggregation strategies. Current strategies trade-off between approximation quality, and costs of communication and computation. In this work, we introduce a novel method that addresses these issues simultaneously. Our key insight is to introduce a deep invertible transformation to approximate each of the subposteriors. These approximations can be made accurate even for complex distributions and serve as intermediate representations, keeping the total communication cost limited. Moreover, they enable us to sample from the product of the subposteriors using an efficient and stable importance sampling scheme. We demonstrate the approach outperforms available state-of-the-art methods in a range of challenging scenarios, including high-dimensional and heterogeneous subposteriors.
Nuanced Metrics for Measuring Unintended Bias with Real Data for Text Classification
Authors: Daniel Borkan, Lucas Dixon, Jeffrey Sorensen, Nithum Thain, Lucy Vasserman
Date: 2019-03-11 00:00:00
Tasks: BIG-bench Machine Learning, Fairness, General Classification, Text Classification

Unintended bias in Machine Learning can manifest as systemic differences in performance for different demographic groups, potentially compounding existing challenges to fairness in society at large. In this paper, we introduce a suite of threshold-agnostic metrics that provide a nuanced view of this unintended bias, by considering the various ways that a classifier's score distribution can vary across designated groups. We also introduce a large new test set of online comments with crowd-sourced annotations for identity references. We use this to show how our metrics can be used to find new and potentially subtle unintended bias in existing public models.
Complementary Learning for Overcoming Catastrophic Forgetting Using Experience Replay
Authors: Mohammad Rostami, Soheil Kolouri, Praveen K. Pilly
Date: 2019-03-11 00:00:00

Despite huge success, deep networks are unable to learn effectively in sequential multitask learning settings as they forget the past learned tasks after learning new tasks. Inspired from complementary learning systems theory, we address this challenge by learning a generative model that couples the current task to the past learned tasks through a discriminative embedding space. We learn an abstract level generative distribution in the embedding that allows the generation of data points to represent the experience. We sample from this distribution and utilize experience replay to avoid forgetting and simultaneously accumulate new knowledge to the abstract distribution in order to couple the current task with past experience. We demonstrate theoretically and empirically that our framework learns a distribution in the embedding that is shared across all task and as a result tackles catastrophic forgetting.
Detecting drug-drug interactions using artificial neural networks and classic graph similarity measures
Authors: Guy Shtar, Lior Rokach, Bracha Shapira
Date: 2019-03-11 00:00:00
Tasks: Graph Similarity, Link Prediction, Prediction

Drug-drug interactions are preventable causes of medical injuries and often result in doctor and emergency room visits. Computational techniques can be used to predict potential drug-drug interactions. We approach the drug-drug interaction prediction problem as a link prediction problem and present two novel methods for drug-drug interaction prediction based on artificial neural networks and factor propagation over graph nodes: adjacency matrix factorization (AMF) and adjacency matrix factorization with propagation (AMFP). We conduct a retrospective analysis by training our models on a previous release of the DrugBank database with 1,141 drugs and 45,296 drug-drug interactions and evaluate the results on a later version of DrugBank with 1,440 drugs and 248,146 drug-drug interactions. Additionally, we perform a holdout analysis using DrugBank. We report an area under the receiver operating characteristic curve score of 0.807 and 0.990 for the retrospective and holdout analyses respectively. Finally, we create an ensemble-based classifier using AMF, AMFP, and existing link prediction methods and obtain an area under the receiver operating characteristic curve of 0.814 and 0.991 for the retrospective and the holdout analyses. We demonstrate that AMF and AMFP provide state of the art results compared to existing methods and that the ensemble-based classifier improves the performance by combining various predictors. These results suggest that AMF, AMFP, and the proposed ensemble-based classifier can provide important information during drug development and regarding drug prescription given only partial or noisy data. These methods can also be used to solve other link prediction problems. Drug embeddings (compressed representations) created when training our models using the interaction network have been made public.
Graph Colouring Meets Deep Learning: Effective Graph Neural Network Models for Combinatorial Problems
Authors: Henrique Lemos, Marcelo Prates, Pedro Avelar, Luis Lamb
Date: 2019-03-11 00:00:00
Tasks: Deep Learning, Graph Neural Network

Deep learning has consistently defied state-of-the-art techniques in many fields over the last decade. However, we are just beginning to understand the capabilities of neural learning in symbolic domains. Deep learning architectures that employ parameter sharing over graphs can produce models which can be trained on complex properties of relational data. These include highly relevant NP-Complete problems, such as SAT and TSP. In this work, we showcase how Graph Neural Networks (GNN) can be engineered -- with a very simple architecture -- to solve the fundamental combinatorial problem of graph colouring. Our results show that the model, which achieves high accuracy upon training on random instances, is able to generalise to graph distributions different from those seen at training time. Further, it performs better than the Neurosat, Tabucol and greedy baselines for some distributions. In addition, we show how vertex embeddings can be clustered in multidimensional spaces to yield constructive solutions even though our model is only trained as a binary classifier. In summary, our results contribute to shorten the gap in our understanding of the algorithms learned by GNNs, as well as hoarding empirical evidence for their capability on hard combinatorial problems. Our results thus contribute to the standing challenge of integrating robust learning and symbolic reasoning in Deep Learning systems.
Financial Trading Model with Stock Bar Chart Image Time Series with Deep Convolutional Neural Networks
Authors: Omer Berat Sezer, Ahmet Murat Ozbayoglu
Date: 2019-03-11 00:00:00
Tasks: Algorithmic Trading, Time Series, Time Series Analysis

Even though computational intelligence techniques have been extensively
utilized in financial trading systems, almost all developed models use the time
series data for price prediction or identifying buy-sell points. However, in
this study we decided to use 2-D stock bar chart images directly without
introducing any additional time series associated with the underlying stock. We
propose a novel algorithmic trading model CNN-BI (Convolutional Neural Network
with Bar Images) using a 2-D Convolutional Neural Network. We generated 2-D
images of sliding windows of 30-day bar charts for Dow 30 stocks and trained a
deep Convolutional Neural Network (CNN) model for our algorithmic trading
model. We tested our model separately between 2007-2012 and 2012-2017 for
representing different market conditions. The results indicate that the model
was able to outperform Buy and Hold strategy, especially in trendless or bear
markets. Since this is a preliminary study and probably one of the first
attempts using such an unconventional approach, there is always potential for
improvement. Overall, the results are promising and the model might be
integrated as part of an ensemble trading model combined with different
strategies.
Learning Edge Properties in Graphs from Path Aggregations
Authors: Rakshit Agrawal, Luca de Alfaro
Date: 2019-03-11 00:00:00
Tasks: Fairness, Link Prediction, Prediction

Graph edges, along with their labels, can represent information of
fundamental importance, such as links between web pages, friendship between
users, the rating given by users to other users or items, and much more. We
introduce LEAP, a trainable, general framework for predicting the presence and
properties of edges on the basis of the local structure, topology, and labels
of the graph. The LEAP framework is based on the exploration and
machine-learning aggregation of the paths connecting nodes in a graph. We
provide several methods for performing the aggregation phase by training path
aggregators, and we demonstrate the flexibility and generality of the framework
by applying it to the prediction of links and user ratings in social networks.
  We validate the LEAP framework on two problems: link prediction, and user
rating prediction. On eight large datasets, among which the arXiv collaboration
network, the Yeast protein-protein interaction, and the US airlines routes
network, we show that the link prediction performance of LEAP is at least as
good as the current state of the art methods, such as SEAL and WLNM. Next, we
consider the problem of predicting user ratings on other users: this problem is
known as the edge-weight prediction problem in weighted signed networks (WSN).
On Bitcoin networks, and Wikipedia RfA, we show that LEAP performs consistently
better than the Fairness & Goodness based regression models, varying the amount
of training edges between 10 to 90%. These examples demonstrate that LEAP, in
spite of its generality, can match or best the performance of approaches that
have been especially crafted to solve very specific edge prediction problems.
Deep Log-Likelihood Ratio Quantization
Authors: Marius Arvinte, Ahmed H. Tewfik, Sriram Vishwanath
Date: 2019-03-11 00:00:00
Tasks: Decoder, Quantization

In this work, a deep learning-based method for log-likelihood ratio (LLR) lossy compression and quantization is proposed, with emphasis on a single-input single-output uncorrelated fading communication setting. A deep autoencoder network is trained to compress, quantize and reconstruct the bit log-likelihood ratios corresponding to a single transmitted symbol. Specifically, the encoder maps to a latent space with dimension equal to the number of sufficient statistics required to recover the inputs - equal to three in this case - while the decoder aims to reconstruct a noisy version of the latent representation with the purpose of modeling quantization effects in a differentiable way. Simulation results show that, when applied to a standard rate-1/2 low-density parity-check (LDPC) code, a finite precision compression factor of nearly three times is achieved when storing an entire codeword, with an incurred loss of performance lower than 0.1 dB compared to straightforward scalar quantization of the log-likelihood ratios.
Practical Multi-fidelity Bayesian Optimization for Hyperparameter Tuning
Authors: Jian Wu, Saul Toscano-Palmerin, Peter I. Frazier, Andrew Gordon Wilson
Date: 2019-03-12 00:00:00
Tasks: Bayesian Optimization

Bayesian optimization is popular for optimizing time-consuming black-box
objectives. Nonetheless, for hyperparameter tuning in deep neural networks, the
time required to evaluate the validation error for even a few hyperparameter
settings remains a bottleneck. Multi-fidelity optimization promises relief
using cheaper proxies to such objectives --- for example, validation error for
a network trained using a subset of the training points or fewer iterations
than required for convergence. We propose a highly flexible and practical
approach to multi-fidelity Bayesian optimization, focused on efficiently
optimizing hyperparameters for iteratively trained supervised learning models.
We introduce a new acquisition function, the trace-aware knowledge-gradient,
which efficiently leverages both multiple continuous fidelity controls and
trace observations --- values of the objective at a sequence of fidelities,
available when varying fidelity using training iterations. We provide a
provably convergent method for optimizing our acquisition function and show it
outperforms state-of-the-art alternatives for hyperparameter tuning of deep
neural networks and large-scale kernel learning.
Imitation Learning of Factored Multi-agent Reactive Models
Authors: Michael Teng, Tuan Anh Le, Adam Scibior, Frank Wood
Date: 2019-03-12 00:00:00
Tasks: Imitation Learning

We apply recent advances in deep generative modeling to the task of imitation learning from biological agents. Specifically, we apply variations of the variational recurrent neural network model to a multi-agent setting where we learn policies of individual uncoordinated agents acting based on their perceptual inputs and their hidden belief state. We learn stochastic policies for these agents directly from observational data, without constructing a reward function. An inference network learned jointly with the policy allows for efficient inference over the agent's belief state given a sequence of its current perceptual inputs and the prior actions it performed, which lets us extrapolate observed sequences of behavior into the future while maintaining uncertainty estimates over future trajectories. We test our approach on a dataset of flies interacting in a 2D environment, where we demonstrate better predictive performance than existing approaches which learn deterministic policies with recurrent neural networks. We further show that the uncertainty estimates over future trajectories we obtain are well calibrated, which makes them useful for a variety of downstream processing tasks.
Activation Analysis of a Byte-Based Deep Neural Network for Malware Classification
Authors: Scott E. Coull, Christopher Gardner
Date: 2019-03-12 00:00:00
Tasks: BIG-bench Machine Learning, Feature Engineering, General Classification, Malware Classification

Feature engineering is one of the most costly aspects of developing effective
machine learning models, and that cost is even greater in specialized problem
domains, like malware classification, where expert skills are necessary to
identify useful features. Recent work, however, has shown that deep learning
models can be used to automatically learn feature representations directly from
the raw, unstructured bytes of the binaries themselves. In this paper, we
explore what these models are learning about malware. To do so, we examine the
learned features at multiple levels of resolution, from individual byte
embeddings to end-to-end analysis of the model. At each step, we connect these
byte-oriented activations to their original semantics through parsing and
disassembly of the binary to arrive at human-understandable features. Through
our results, we identify several interesting features learned by the model and
their connection to manually-derived features typically used by traditional
machine learning models. Additionally, we explore the impact of training data
volume and regularization on the quality of the learned features and the
efficacy of the classifiers, revealing the somewhat paradoxical insight that
better generalization does not necessarily result in better performance for
byte-based malware classifiers.
Low-rank Tensor Grid for Image Completion
Authors: Huyan Huang, Yipeng Liu, Ce Zhu
Date: 2019-03-12 00:00:00
Tasks: Computational Efficiency

Tensor completion estimates missing components by exploiting the low-rank structure of multi-way data. The recently proposed methods based on tensor train (TT) and tensor ring (TR) show better performance in image recovery than classical ones. Compared with TT and TR, the projected entangled pair state (PEPS), which is also called tensor grid (TG), allows more interactions between different dimensions, and may lead to more compact representation. In this paper, we propose to perform image completion based on low-rank tensor grid. A two-stage density matrix renormalization group algorithm is used for initialization of TG decomposition, which consists of multiple TT decompositions. The latent TG factors can be alternatively obtained by solving alternating least squares problems. To further improve the computational efficiency, a multi-linear matrix factorization for low rank TG completion is developed by using parallel matrix factorization. Experimental results on synthetic data and real-world images show the proposed methods outperform the existing ones in terms of recovery accuracy.
Open-Set Recognition Using Intra-Class Splitting
Authors: Patrick Schlachter, Yiwen Liao, Bin Yang
Date: 2019-03-12 00:00:00
Tasks: General Classification, Open Set Learning

This paper proposes a method to use deep neural networks as end-to-end open-set classifiers. It is based on intra-class data splitting. In open-set recognition, only samples from a limited number of known classes are available for training. During inference, an open-set classifier must reject samples from unknown classes while correctly classifying samples from known classes. The proposed method splits given data into typical and atypical normal subsets by using a closed-set classifier. This enables to model the abnormal classes by atypical normal samples. Accordingly, the open-set recognition problem is reformulated into a traditional classification problem. In addition, a closed-set regularization is proposed to guarantee a high closed-set classification performance. Intensive experiments on five well-known image datasets showed the effectiveness of the proposed method which outperformed the baselines and achieved a distinct improvement over the state-of-the-art methods.
Online Human Activity Recognition Employing Hierarchical Hidden Markov Models
Authors: Parviz Asghari, Elnaz Soelimani, Ehsan Nazerfard
Date: 2019-03-12 00:00:00
Tasks: Activity Recognition, Human Activity Recognition, Privacy Preserving

In the last few years there has been a growing interest in Human Activity
Recognition~(HAR) topic. Sensor-based HAR approaches, in particular, has been
gaining more popularity owing to their privacy preserving nature. Furthermore,
due to the widespread accessibility of the internet, a broad range of
streaming-based applications such as online HAR, has emerged over the past
decades. However, proposing sufficiently robust online activity recognition
approach in smart environment setting is still considered as a remarkable
challenge. This paper presents a novel online application of Hierarchical
Hidden Markov Model in order to detect the current activity on the live
streaming of sensor events. Our method consists of two phases. In the first
phase, data stream is segmented based on the beginning and ending of the
activity patterns. Also, on-going activity is reported with every receiving
observation. This phase is implemented using Hierarchical Hidden Markov models.
The second phase is devoted to the correction of the provided label for the
segmented data stream based on statistical features. The proposed model can
also discover the activities that happen during another activity - so-called
interrupted activities. After detecting the activity pane, the predicted label
will be corrected utilizing statistical features such as time of day at which
the activity happened and the duration of the activity. We validated our
proposed method by testing it against two different smart home datasets and
demonstrated its effectiveness, which is competing with the state-of-the-art
methods.