pwc-archive/papers-with-abstracts huggingface dataset:
Double Viterbi: Weight Encoding for High Compression Ratio and Fast On-Chip Reconstruction for Deep Neural Network
Authors: Daehyun Ahn, Dongsoo Lee, Taesu Kim, Jae-Joon Kim
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Model Compression, Quantization

Weight pruning has been introduced as an efficient model compression technique. Even though pruning removes significant amount of weights in a network, memory requirement reduction was limited since conventional sparse matrix formats require significant amount of memory to store index-related information. Moreover, computations associated with such sparse matrix formats are slow because sequential sparse matrix decoding process does not utilize highly parallel computing systems efficiently. As an attempt to compress index information while keeping the decoding process parallelizable, Viterbi-based pruning was suggested. Decoding non-zero weights, however, is still sequential in Viterbi-based pruning. In this paper, we propose a new sparse matrix format in order to enable a highly parallel decoding process of the entire sparse matrix. The proposed sparse matrix is constructed by combining pruning and weight quantization. For the latest RNN models on PTB and WikiText-2 corpus, LSTM parameter storage requirement is compressed 19x using the proposed sparse matrix format compared to the baseline model. Compressed weight and indices can be reconstructed into a dense matrix fast using Viterbi encoders. Simulation results show that the proposed scheme can feed parameters to processing elements 20 % to 106 % faster than the case where the dense matrix values directly come from DRAM.
Learning protein constitutive motifs from sequence data
Authors: Jérôme Tubiana, Simona Cocco, Rémi Monasson
Date: 2018-03-23 00:00:00
Tasks: Benchmarking, Specificity

Statistical analysis of evolutionary-related protein sequences provides
insights about their structure, function, and history. We show that Restricted
Boltzmann Machines (RBM), designed to learn complex high-dimensional data and
their statistical features, can efficiently model protein families from
sequence information. We apply RBM to two protein domains, Kunitz and WW, and
to synthetic lattice proteins for benchmarking. The features inferred by the
RBM can be biologically interpreted in terms of structural modes, including
residue-residue tertiary contacts and extended secondary motifs ($\alpha$-helix
and $\beta$-sheet), of functional modes controlling activity and ligand
specificity, or of phylogenetic identity. In addition, we use RBM to design new
protein sequences with putative properties by composing and turning up or down
the different modes at will. Our work therefore shows that RBM are a versatile
and practical tool to unveil and exploit the genotype-phenotype relationship
for protein families.
Can Millimeter Wave Cellular Systems provide High Reliability and Low Latency? An analysis of the impact of Mobile Blockers
Authors: Ish Kumar Jain, Rajeev Kumar, Shivendra Panwar
Date: 2018-07-12 00:00:00

Millimeter Wave (mmWave) communication systems can provide high data rates,
but the system performance may degrade significantly due to interruptions by
mobile blockers such as humans or vehicles. A high frequency of interruptions
and lengthy blockage durations will degrade the quality of the user's
experience. A promising solution is to employ the macrodiversity of Base
Stations (BSs), where the User Equipment (UE) can handover to other available
BSs if the current serving BS gets blocked. However, an analytical model to
evaluate the system performance of dynamic blockage events in this setting is
unknown. In this paper, we develop a Line of Sight (LOS) dynamic blockage model
and evaluate the probability, duration, and frequency of blockage events
considering all the links in the range of the UE which are not blocked by
buildings or the user's own body. For a dense urban area, we also analyze the
impact of non-LOS (NLOS) links on blockage events. Our results indicate that
the minimum density of BS required to satisfy the Quality of Service (QoS)
requirements of Ultra Reliable Low Latency Communication (URLLC) applications
is driven mainly by reliability and latency constraints, rather than coverage
or capacity requirements.
A Geometric Perspective on Optimal Representations for Reinforcement Learning
Authors: Marc G. Bellemare, Will Dabney, Robert Dadashi, Adrien Ali Taiga, Pablo Samuel Castro, Nicolas Le Roux, Dale Schuurmans, Tor Lattimore, Clare Lyle
Date: 2019-01-31 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Representation Learning

We propose a new perspective on representation learning in reinforcement learning based on geometric properties of the space of value functions. We leverage this perspective to provide formal evidence regarding the usefulness of value functions as auxiliary tasks. Our formulation considers adapting the representation to minimize the (linear) approximation of the value function of all stationary policies for a given environment. We show that this optimization reduces to making accurate predictions regarding a special class of value functions which we call adversarial value functions (AVFs). We demonstrate that using value functions as auxiliary tasks corresponds to an expected-error relaxation of our formulation, with AVFs a natural candidate, and identify a close relationship with proto-value functions (Mahadevan, 2005). We highlight characteristics of AVFs and their usefulness as auxiliary tasks in a series of experiments on the four-room domain.
Addressing Sample Complexity in Visual Tasks Using HER and Hallucinatory GANs
Authors: Himanshu Sahni, Toby Buckley, Pieter Abbeel, Ilya Kuzovkin
Date: 2019-01-31 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement Learning (RL) algorithms typically require millions of environment interactions to learn successful policies in sparse reward settings. Hindsight Experience Replay (HER) was introduced as a technique to increase sample efficiency by reimagining unsuccessful trajectories as successful ones by altering the originally intended goals. However, it cannot be directly applied to visual environments where goal states are often characterized by the presence of distinct visual features. In this work, we show how visual trajectories can be hallucinated to appear successful by altering agent observations using a generative model trained on relatively few snapshots of the goal. We then use this model in combination with HER to train RL agents in visual settings. We validate our approach on 3D navigation tasks and a simulated robotics application and show marked improvement over baselines derived from previous work.
Shaping the Narrative Arc: An Information-Theoretic Approach to Collaborative Dialogue
Authors: Kory W. Mathewson, Pablo Samuel Castro, Colin Cherry, George Foster, Marc G. Bellemare
Date: 2019-01-31 00:00:00
Tasks: ARC, Specificity

We consider the problem of designing an artificial agent capable of
interacting with humans in collaborative dialogue to produce creative, engaging
narratives. In this task, the goal is to establish universe details, and to
collaborate on an interesting story in that universe, through a series of
natural dialogue exchanges. Our model can augment any probabilistic
conversational agent by allowing it to reason about universe information
established and what potential next utterances might reveal. Ideally, with each
utterance, agents would reveal just enough information to add specificity and
reduce ambiguity without limiting the conversation. We empirically show that
our model allows control over the rate at which the agent reveals information
and that doing so significantly improves accuracy in predicting the next line
of dialogues from movies. We close with a case-study with four professional
theatre performers, who preferred interactions with our model-augmented agent
over an unaugmented agent.
The Value Function Polytope in Reinforcement Learning
Authors: Robert Dadashi, Adrien Ali Taïga, Nicolas Le Roux, Dale Schuurmans, Marc G. Bellemare
Date: 2019-01-31 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We establish geometric and topological properties of the space of value functions in finite state-action Markov decision processes. Our main contribution is the characterization of the nature of its shape: a general polytope (Aigner et al., 2010). To demonstrate this result, we exhibit several properties of the structural relationship between policies and value functions including the line theorem, which shows that the value functions of policies constrained on all but one state describe a line segment. Finally, we use this novel perspective to introduce visualizations to enhance the understanding of the dynamics of reinforcement learning algorithms.
Stochastic Recursive Variance-Reduced Cubic Regularization Methods
Authors: Dongruo Zhou, Quanquan Gu
Date: 2019-01-31 00:00:00

Stochastic Variance-Reduced Cubic regularization (SVRC) algorithms have received increasing attention due to its improved gradient/Hessian complexities (i.e., number of queries to stochastic gradient/Hessian oracles) to find local minima for nonconvex finite-sum optimization. However, it is unclear whether existing SVRC algorithms can be further improved. Moreover, the semi-stochastic Hessian estimator adopted in existing SVRC algorithms prevents the use of Hessian-vector product-based fast cubic subproblem solvers, which makes SVRC algorithms computationally intractable for high-dimensional problems. In this paper, we first present a Stochastic Recursive Variance-Reduced Cubic regularization method (SRVRC) using a recursively updated semi-stochastic gradient and Hessian estimators. It enjoys improved gradient and Hessian complexities to find an $(\epsilon, \sqrt{\epsilon})$-approximate local minimum, and outperforms the state-of-the-art SVRC algorithms. Built upon SRVRC, we further propose a Hessian-free SRVRC algorithm, namely SRVRC$_{\text{free}}$, which only requires stochastic gradient and Hessian-vector product computations, and achieves $\tilde O(dn\epsilon^{-2} \land d\epsilon^{-3})$ runtime complexity, where $n$ is the number of component functions in the finite-sum structure, $d$ is the problem dimension, and $\epsilon$ is the optimization precision. This outperforms the best-known runtime complexity $\tilde O(d\epsilon^{-3.5})$ achieved by stochastic cubic regularization algorithm proposed in Tripuraneni et al. 2018.
ProBO: Versatile Bayesian Optimization Using Any Probabilistic Programming Language
Authors: Willie Neiswanger, Kirthevasan Kandasamy, Barnabas Poczos, Jeff Schneider, Eric Xing
Date: 2019-01-31 00:00:00
Tasks: Bayesian Optimization, Gaussian Processes, Probabilistic Programming

Optimizing an expensive-to-query function is a common task in science and engineering, where it is beneficial to keep the number of queries to a minimum. A popular strategy is Bayesian optimization (BO), which leverages probabilistic models for this task. Most BO today uses Gaussian processes (GPs), or a few other surrogate models. However, there is a broad set of Bayesian modeling techniques that could be used to capture complex systems and reduce the number of queries in BO. Probabilistic programming languages (PPLs) are modern tools that allow for flexible model definition, prior specification, model composition, and automatic inference. In this paper, we develop ProBO, a BO procedure that uses only standard operations common to most PPLs. This allows a user to drop in a model built with an arbitrary PPL and use it directly in BO. We describe acquisition functions for ProBO, and strategies for efficiently optimizing these functions given complex models or costly inference procedures. Using existing PPLs, we implement new models to aid in a few challenging optimization settings, and demonstrate these on model hyperparameter and architecture search tasks.
Minimizing Negative Transfer of Knowledge in Multivariate Gaussian Processes: A Scalable and Regularized Approach
Authors: Raed Kontar, Garvesh Raskutti, Shiyu Zhou
Date: 2019-01-31 00:00:00
Tasks: Gaussian Processes

Recently there has been an increasing interest in the multivariate Gaussian
process (MGP) which extends the Gaussian process (GP) to deal with multiple
outputs. One approach to construct the MGP and account for non-trivial
commonalities amongst outputs employs a convolution process (CP). The CP is
based on the idea of sharing latent functions across several convolutions.
Despite the elegance of the CP construction, it provides new challenges that
need yet to be tackled. First, even with a moderate number of outputs, model
building is extremely prohibitive due to the huge increase in computational
demands and number of parameters to be estimated. Second, the negative transfer
of knowledge may occur when some outputs do not share commonalities. In this
paper we address these issues. We propose a regularized pairwise modeling
approach for the MGP established using CP. The key feature of our approach is
to distribute the estimation of the full multivariate model into a group of
bivariate GPs which are individually built. Interestingly pairwise modeling
turns out to possess unique characteristics, which allows us to tackle the
challenge of negative transfer through penalizing the latent function that
facilitates information sharing in each bivariate model. Predictions are then
made through combining predictions from the bivariate models within a Bayesian
framework. The proposed method has excellent scalability when the number of
outputs is large and minimizes the negative transfer of knowledge between
uncorrelated outputs. Statistical guarantees for the proposed method are
studied and its advantageous features are demonstrated through numerical
studies.
Multi-Task Deep Neural Networks for Natural Language Understanding
Authors: Xiaodong Liu, Pengcheng He, Weizhu Chen, Jianfeng Gao
Date: 2019-01-31 00:00:00
Proceeding: ACL 2019 7
Tasks: Domain Adaptation, Language Modeling, Language Modelling, Linguistic Acceptability, Natural Language Inference, Natural Language Understanding, Paraphrase Identification, Sentiment Analysis

In this paper, we present a Multi-Task Deep Neural Network (MT-DNN) for learning representations across multiple natural language understanding (NLU) tasks. MT-DNN not only leverages large amounts of cross-task data, but also benefits from a regularization effect that leads to more general representations in order to adapt to new tasks and domains. MT-DNN extends the model proposed in Liu et al. (2015) by incorporating a pre-trained bidirectional transformer language model, known as BERT (Devlin et al., 2018). MT-DNN obtains new state-of-the-art results on ten NLU tasks, including SNLI, SciTail, and eight out of nine GLUE tasks, pushing the GLUE benchmark to 82.7% (2.2% absolute improvement). We also demonstrate using the SNLI and SciTail datasets that the representations learned by MT-DNN allow domain adaptation with substantially fewer in-domain labels than the pre-trained BERT representations. The code and pre-trained models are publicly available at https://github.com/namisan/mt-dnn.
Contrasting Exploration in Parameter and Action Space: A Zeroth-Order Optimization Perspective
Authors: Anirudh Vemula, Wen Sun, J. Andrew Bagnell
Date: 2019-01-31 00:00:00
Tasks: continuous-control, Continuous Control, regression, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Black-box optimizers that explore in parameter space have often been shown to
outperform more sophisticated action space exploration methods developed
specifically for the reinforcement learning problem. We examine these black-box
methods closely to identify situations in which they are worse than action
space exploration methods and those in which they are superior. Through simple
theoretical analyses, we prove that complexity of exploration in parameter
space depends on the dimensionality of parameter space, while complexity of
exploration in action space depends on both the dimensionality of action space
and horizon length. This is also demonstrated empirically by comparing simple
exploration methods on several model problems, including Contextual Bandit,
Linear Regression and Reinforcement Learning in continuous control.
Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks
Authors: Jason W. Wei, Laura J. Tafe, Yevgeniy A. Linnik, Louis J. Vaickus, Naofumi Tomita, Saeed Hassanpour
Date: 2019-01-31 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Lung Cancer Diagnosis, whole slide images

Classification of histologic patterns in lung adenocarcinoma is critical for
determining tumor grade and treatment for patients. However, this task is often
challenging due to the heterogeneous nature of lung adenocarcinoma and the
subjective criteria for evaluation. In this study, we propose a deep learning
model that automatically classifies the histologic patterns of lung
adenocarcinoma on surgical resection slides. Our model uses a convolutional
neural network to identify regions of neoplastic cells, then aggregates those
classifications to infer predominant and minor histologic patterns for any
given whole-slide image. We evaluated our model on an independent set of 143
whole-slide images. It achieved a kappa score of 0.525 and an agreement of
66.6% with three pathologists for classifying the predominant patterns,
slightly higher than the inter-pathologist kappa score of 0.485 and agreement
of 62.7% on this test set. All evaluation metrics for our model and the three
pathologists were within 95% confidence intervals of agreement. If confirmed in
clinical practice, our model can assist pathologists in improving
classification of lung adenocarcinoma patterns by automatically pre-screening
and highlighting cancerous regions prior to review. Our approach can be
generalized to any whole-slide image classification task, and code is made
publicly available at https://github.com/BMIRDS/deepslide.
An Optimization Framework for Task Sequencing in Curriculum Learning
Authors: Francesco Foglino, Christiano Coletto Christakou, Matteo Leonetti
Date: 2019-01-31 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Curriculum learning in reinforcement learning is used to shape exploration by presenting the agent with increasingly complex tasks. The idea of curriculum learning has been largely applied in both animal training and pedagogy. In reinforcement learning, all previous task sequencing methods have shaped exploration with the objective of reducing the time to reach a given performance level. We propose novel uses of curriculum learning, which arise from choosing different objective functions. Furthermore, we define a general optimization framework for task sequencing and evaluate the performance of popular metaheuristic search methods on several tasks. We show that curriculum learning can be successfully used to: improve the initial performance, take fewer suboptimal actions during exploration, and discover better policies.
Towards Controlled Transformation of Sentiment in Sentences
Authors: Wouter Leeftink, Gerasimos Spanakis
Date: 2019-01-31 00:00:00
Tasks: Sentence

An obstacle to the development of many natural language processing products
is the vast amount of training examples necessary to get satisfactory results.
The generation of these examples is often a tedious and time-consuming task.
This paper this paper proposes a method to transform the sentiment of sentences
in order to limit the work necessary to generate more training data. This means
that one sentence can be transformed to an opposite sentiment sentence and
should reduce by half the work required in the generation of text. The proposed
pipeline consists of a sentiment classifier with an attention mechanism to
highlight the short phrases that determine the sentiment of a sentence. Then,
these phrases are changed to phrases of the opposite sentiment using a baseline
model and an autoencoder approach. Experiments are run on both the separate
parts of the pipeline as well as on the end-to-end model. The sentiment
classifier is tested on its accuracy and is found to perform adequately. The
autoencoder is tested on how well it is able to change the sentiment of an
encoded phrase and it was found that such a task is possible. We use human
evaluation to judge the performance of the full (end-to-end) pipeline and that
reveals that a model using word vectors outperforms the encoder model.
Numerical evaluation shows that a success rate of 54.7% is achieved on the
sentiment change.
Distributionally Robust Removal of Malicious Nodes from Networks
Authors: Sixie Yu, Yevgeniy Vorobeychik
Date: 2019-01-31 00:00:00

An important problem in networked systems is detection and removal of
suspected malicious nodes. A crucial consideration in such settings is the
uncertainty endemic in detection, coupled with considerations of network
connectivity, which impose indirect costs from mistakely removing benign nodes
as well as failing to remove malicious nodes. A recent approach proposed to
address this problem directly tackles these considerations, but has a
significant limitation: it assumes that the decision maker has accurate
knowledge of the joint maliciousness probability of the nodes on the network.
This is clearly not the case in practice, where such a distribution is at best
an estimate from limited evidence. To address this problem, we propose a
distributionally robust framework for optimal node removal. While the problem
is NP-Hard, we propose a principled algorithmic technique for solving it
approximately based on duality combined with Semidefinite Programming
relaxation. A combination of both theoretical and empirical analysis, the
latter using both synthetic and real data, provide strong evidence that our
algorithmic approach is highly effective and, in particular, is significantly
more robust than the state of the art.
Exploring the context of recurrent neural network based conversational agents
Authors: Raffaele Piccini, Gerasimos Spanakis
Date: 2019-01-31 00:00:00
Tasks: Decoder

Conversational agents have begun to rise both in the academic (in terms of
research) and commercial (in terms of applications) world. This paper
investigates the task of building a non-goal driven conversational agent, using
neural network generative models and analyzes how the conversation context is
handled. It compares a simpler Encoder-Decoder with a Hierarchical Recurrent
Encoder-Decoder architecture, which includes an additional module to model the
context of the conversation using previous utterances information. We found
that the hierarchical model was able to extract relevant context information
and include them in the generation of the output. However, it performed worse
(35-40%) than the simple Encoder-Decoder model regarding both grammatically
correct output and meaningful response. Despite these results, experiments
demonstrate how conversations about similar topics appear close to each other
in the context space due to the increased frequency of specific topic-related
words, thus leaving promising directions for future research and how the
context of a conversation can be exploited.