pwc-archive/papers-with-abstracts huggingface dataset:
Unified Pragmatic Models for Generating and Following Instructions
Authors: Daniel Fried, Jacob Andreas, Dan Klein
Date: 2017-11-14 00:00:00
Proceeding: NAACL 2018 6
Tasks: Text Generation

We show that explicit pragmatic inference aids in correctly generating and
following natural language instructions for complex, sequential tasks. Our
pragmatics-enabled models reason about why speakers produce certain
instructions, and about how listeners will react upon hearing them. Like
previous pragmatic models, we use learned base listener and speaker models to
build a pragmatic speaker that uses the base listener to simulate the
interpretation of candidate descriptions, and a pragmatic listener that reasons
counterfactually about alternative descriptions. We extend these models to
tasks with sequential structure. Evaluation of language generation and
interpretation shows that pragmatic inference improves state-of-the-art
listener models (at correctly interpreting human instructions) and speaker
models (at producing instructions correctly interpreted by humans) in diverse
settings.
An Expectation Conditional Maximization approach for Gaussian graphical models
Authors: Zehang Richard Li, Tyler H. McCormick
Date: 2017-09-20 00:00:00
Tasks: Variable Selection

Bayesian graphical models are a useful tool for understanding dependence
relationships among many variables, particularly in situations with external
prior information. In high-dimensional settings, the space of possible graphs
becomes enormous, rendering even state-of-the-art Bayesian stochastic search
computationally infeasible. We propose a deterministic alternative to estimate
Gaussian and Gaussian copula graphical models using an Expectation Conditional
Maximization (ECM) algorithm, extending the EM approach from Bayesian variable
selection to graphical model estimation. We show that the ECM approach enables
fast posterior exploration under a sequence of mixture priors, and can
incorporate multiple sources of information.
String Methods for Stochastic Image and Shape Matching
Authors: Alexis Arnaudon, Darryl Holm, Stefan Sommer
Date: 2018-05-15 00:00:00

Matching of images and analysis of shape differences is traditionally pursued
by energy minimization of paths of deformations acting to match the shape
objects. In the Large Deformation Diffeomorphic Metric Mapping (LDDMM)
framework, iterative gradient descents on the matching functional lead to
matching algorithms informally known as Beg algorithms. When stochasticity is
introduced to model stochastic variability of shapes and to provide more
realistic models of observed shape data, the corresponding matching problem can
be solved with a stochastic Beg algorithm, similar to the finite temperature
string method used in rare event sampling. In this paper, we apply a stochastic
model compatible with the geometry of the LDDMM framework to obtain a
stochastic model of images and we derive the stochastic version of the Beg
algorithm which we compare with the string method and an
expectation-maximization optimization of posterior likelihoods. The algorithm
and its use for statistical inference is tested on stochastic LDDMM landmarks
and images.
Unsupervised Learning of Word-Sequence Representations from Scratch via Convolutional Tensor Decomposition
Authors: Furong Huang, Animashree Anandkumar
Date: 2016-06-10 00:00:00
Tasks: Dictionary Learning, Sentence, Tensor Decomposition

Unsupervised text embeddings extraction is crucial for text understanding in
machine learning. Word2Vec and its variants have received substantial success
in mapping words with similar syntactic or semantic meaning to vectors close to
each other. However, extracting context-aware word-sequence embedding remains a
challenging task. Training over large corpus is difficult as labels are
difficult to get. More importantly, it is challenging for pre-trained models to
obtain word-sequence embeddings that are universally good for all downstream
tasks or for any new datasets. We propose a two-phased ConvDic+DeconvDec
framework to solve the problem by combining a word-sequence dictionary learning
model with a word-sequence embedding decode model. We propose a convolutional
tensor decomposition mechanism to learn good word-sequence phrase dictionary in
the learning phase. It is proved to be more accurate and much more efficient
than the popular alternating minimization method. In the decode phase, we
introduce a deconvolution framework that is immune to the problem of varying
sentence lengths. The word-sequence embeddings we extracted using
ConvDic+DeconvDec are universally good for a few downstream tasks we test on.
The framework requires neither pre-training nor prior/outside information.
Core Conflictual Relationship: Text Mining to Discover What and When
Authors: Fionn Murtagh, Giuseppe Iurato
Date: 2018-05-28 00:00:00
Tasks: Computational Efficiency

Following detailed presentation of the Core Conflictual Relationship Theme
(CCRT), there is the objective of relevant methods for what has been described
as verbalization and visualization of data. Such is also termed data mining and
text mining, and knowledge discovery in data. The Correspondence Analysis
methodology, also termed Geometric Data Analysis, is shown in a case study to
be comprehensive and revealing. Computational efficiency depends on how the
analysis process is structured. For both illustrative and revealing aspects of
the case study here, relatively extensive dream reports are used. This
Geometric Data Analysis confirms the validity of CCRT method.
Prioritizing network communities
Authors: Marinka Zitnik, Rok Sosic, Jure Leskovec
Date: 2018-05-07 00:00:00
Tasks: Community Detection

Uncovering modular structure in networks is fundamental for systems in
biology, physics, and engineering. Community detection identifies candidate
modules as hypotheses, which then need to be validated through experiments,
such as mutagenesis in a biological laboratory. Only a few communities can
typically be validated, and it is thus important to prioritize which
communities to select for downstream experimentation. Here we develop CRank, a
mathematically principled approach for prioritizing network communities. CRank
efficiently evaluates robustness and magnitude of structural features of each
community and then combines these features into the community prioritization.
CRank can be used with any community detection method. It needs only
information provided by the network structure and does not require any
additional metadata or labels. However, when available, CRank can incorporate
domain-specific information to further boost performance. Experiments on many
large networks show that CRank effectively prioritizes communities, yielding a
nearly 50-fold improvement in community prioritization.
Global Sum Pooling: A Generalization Trick for Object Counting with Small Datasets of Large Images
Authors: Shubhra Aich, Ian Stavness
Date: 2018-05-28 00:00:00
Tasks: Crowd Counting, Object Counting

In this paper, we explore the problem of training one-look regression models for counting objects in datasets comprising a small number of high-resolution, variable-shaped images. We illustrate that conventional global average pooling (GAP) based models are unreliable due to the patchwise cancellation of true overestimates and underestimates for patchwise inference. To overcome this limitation and reduce overfitting caused by the training on full-resolution images, we propose to employ global sum pooling (GSP) instead of GAP or fully connected (FC) layers at the backend of a convolutional network. Although computationally equivalent to GAP, we show through comprehensive experimentation that GSP allows convolutional networks to learn the counting task as a simple linear mapping problem generalized over the input shape and the number of objects present. This generalization capability allows GSP to avoid both patchwise cancellation and overfitting by training on small patches and inference on full-resolution images as a whole. We evaluate our approach on four different aerial image datasets - two car counting datasets (CARPK and COWC), one crowd counting dataset (ShanghaiTech; parts A and B) and one new challenging dataset for wheat spike counting. Our GSP models improve upon the state-of-the-art approaches on all four datasets with a simple architecture. Also, GSP architectures trained with smaller-sized image patches exhibit better localization property due to their focus on learning from smaller regions while training.
A Crowd-Annotated Spanish Corpus for Humor Analysis
Authors: Santiago Castro, Luis Chiruzzo, Aiala Rosá, Diego Garat, Guillermo Moncecchi
Date: 2017-10-02 00:00:00
Proceeding: WS 2018 7
Tasks: Humor Detection

Computational Humor involves several tasks, such as humor recognition, humor
generation, and humor scoring, for which it is useful to have human-curated
data. In this work we present a corpus of 27,000 tweets written in Spanish and
crowd-annotated by their humor value and funniness score, with about four
annotations per tweet, tagged by 1,300 people over the Internet. It is equally
divided between tweets coming from humorous and non-humorous accounts. The
inter-annotator agreement Krippendorff's alpha value is 0.5710. The dataset is
available for general use and can serve as a basis for humor detection and as a
first step to tackle subjectivity.
More Than a Feeling: Learning to Grasp and Regrasp using Vision and Touch
Authors: Roberto Calandra, Andrew Owens, Dinesh Jayaraman, Justin Lin, Wenzhen Yuan, Jitendra Malik, Edward H. Adelson, Sergey Levine
Date: 2018-05-28 00:00:00
Tasks: Robotic Grasping

For humans, the process of grasping an object relies heavily on rich tactile
feedback. Most recent robotic grasping work, however, has been based only on
visual input, and thus cannot easily benefit from feedback after initiating
contact. In this paper, we investigate how a robot can learn to use tactile
information to iteratively and efficiently adjust its grasp. To this end, we
propose an end-to-end action-conditional model that learns regrasping policies
from raw visuo-tactile data. This model -- a deep, multimodal convolutional
network -- predicts the outcome of a candidate grasp adjustment, and then
executes a grasp by iteratively selecting the most promising actions. Our
approach requires neither calibration of the tactile sensors, nor any
analytical modeling of contact forces, thus reducing the engineering effort
required to obtain efficient grasping policies. We train our model with data
from about 6,450 grasping trials on a two-finger gripper equipped with GelSight
high-resolution tactile sensors on each finger. Across extensive experiments,
our approach outperforms a variety of baselines at (i) estimating grasp
adjustment outcomes, (ii) selecting efficient grasp adjustments for quick
grasping, and (iii) reducing the amount of force applied at the fingers, while
maintaining competitive performance. Finally, we study the choices made by our
model and show that it has successfully acquired useful and interpretable
grasping behaviors.
Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting
Authors: Yen-Chun Chen, Mohit Bansal
Date: 2018-05-28 00:00:00
Proceeding: ACL 2018 7
Tasks: Abstractive Text Summarization, Decoder, Sentence, Sentence ReWriting, Text Summarization

Inspired by how humans summarize long documents, we propose an accurate and
fast summarization model that first selects salient sentences and then rewrites
them abstractively (i.e., compresses and paraphrases) to generate a concise
overall summary. We use a novel sentence-level policy gradient method to bridge
the non-differentiable computation between these two neural networks in a
hierarchical way, while maintaining language fluency. Empirically, we achieve
the new state-of-the-art on all metrics (including human evaluation) on the
CNN/Daily Mail dataset, as well as significantly higher abstractiveness scores.
Moreover, by first operating at the sentence-level and then the word-level, we
enable parallel decoding of our neural generative model that results in
substantially faster (10-20x) inference speed as well as 4x faster training
convergence than previous long-paragraph encoder-decoder models. We also
demonstrate the generalization of our model on the test-only DUC-2002 dataset,
where we achieve higher scores than a state-of-the-art model.
Training DNNs with Hybrid Block Floating Point
Authors: Mario Drumond, Tao Lin, Martin Jaggi, Babak Falsafi
Date: 2018-04-04 00:00:00
Proceeding: NeurIPS 2018 12

The wide adoption of DNNs has given birth to unrelenting computing
requirements, forcing datacenter operators to adopt domain-specific
accelerators to train them. These accelerators typically employ densely packed
full precision floating-point arithmetic to maximize performance per area.
Ongoing research efforts seek to further increase that performance density by
replacing floating-point with fixed-point arithmetic. However, a significant
roadblock for these attempts has been fixed point's narrow dynamic range, which
is insufficient for DNN training convergence. We identify block floating point
(BFP) as a promising alternative representation since it exhibits wide dynamic
range and enables the majority of DNN operations to be performed with
fixed-point logic. Unfortunately, BFP alone introduces several limitations that
preclude its direct applicability. In this work, we introduce HBFP, a hybrid
BFP-FP approach, which performs all dot products in BFP and other operations in
floating point. HBFP delivers the best of both worlds: the high accuracy of
floating point at the superior hardware density of fixed point. For a wide
variety of models, we show that HBFP matches floating point's accuracy while
enabling hardware implementations that deliver up to 8.5x higher throughput.
Dataflow Matrix Machines as a Generalization of Recurrent Neural Networks
Authors: Michael Bukatin, Steve Matthews, Andrey Radul
Date: 2016-03-29 00:00:00
Tasks: BIG-bench Machine Learning, Probabilistic Programming

Dataflow matrix machines are a powerful generalization of recurrent neural
networks. They work with multiple types of arbitrary linear streams, multiple
types of powerful neurons, and allow to incorporate higher-order constructions.
We expect them to be useful in machine learning and probabilistic programming,
and in the synthesis of dynamic systems and of deterministic and probabilistic
programs.
Reward Constrained Policy Optimization
Authors: Chen Tessler, Daniel J. Mankowitz, Shie Mannor
Date: 2018-05-28 00:00:00
Proceeding: ICLR 2019 5
Tasks: Reinforcement Learning, Reinforcement Learning (RL), Safe Reinforcement Learning

Solving tasks in Reinforcement Learning is no easy feat. As the goal of the
agent is to maximize the accumulated reward, it often learns to exploit
loopholes and misspecifications in the reward signal resulting in unwanted
behavior. While constraints may solve this issue, there is no closed form
solution for general constraints. In this work we present a novel
multi-timescale approach for constrained policy optimization, called `Reward
Constrained Policy Optimization' (RCPO), which uses an alternative penalty
signal to guide the policy towards a constraint satisfying one. We prove the
convergence of our approach and provide empirical evidence of its ability to
train constraint satisfying policies.
Non-bifurcating phylogenetic tree inference via the adaptive LASSO
Authors: Cheng Zhang, Vu Dinh, Frederick A. Matsen IV
Date: 2018-05-28 00:00:00

Phylogenetic tree inference using deep DNA sequencing is reshaping our understanding of rapidly evolving systems, such as the within-host battle between viruses and the immune system. Densely sampled phylogenetic trees can contain special features, including "sampled ancestors" in which we sequence a genotype along with its direct descendants, and "polytomies" in which multiple descendants arise simultaneously. These features are apparent after identifying zero-length branches in the tree. However, current maximum-likelihood based approaches are not capable of revealing such zero-length branches. In this paper, we find these zero-length branches by introducing adaptive-LASSO-type regularization estimators to phylogenetics, deriving their properties, and showing regularization to be a practically useful approach for phylogenetics.
BlockCNN: A Deep Network for Artifact Removal and Image Compression
Authors: Danial Maleki, Soheila Nadalian, Mohammad Mahdi Derakhshani, Mohammad Amin Sadeghi
Date: 2018-05-28 00:00:00
Tasks: Image Compression

We present a general technique that performs both artifact removal and image
compression. For artifact removal, we input a JPEG image and try to remove its
compression artifacts. For compression, we input an image and process its 8 by
8 blocks in a sequence. For each block, we first try to predict its intensities
based on previous blocks; then, we store a residual with respect to the input
image. Our technique reuses JPEG's legacy compression and decompression
routines. Both our artifact removal and our image compression techniques use
the same deep network, but with different training weights. Our technique is
simple and fast and it significantly improves the performance of artifact
removal and image compression.
State-Denoised Recurrent Neural Networks
Authors: Michael C. Mozer, Denis Kazakov, Robert V. Lindsey
Date: 2018-05-22 00:00:00
Proceeding: ICLR 2019 5
Tasks: Denoising

Recurrent neural networks (RNNs) are difficult to train on sequence
processing tasks, not only because input noise may be amplified through
feedback, but also because any inaccuracy in the weights has similar
consequences as input noise. We describe a method for denoising the hidden
state during training to achieve more robust representations thereby improving
generalization performance. Attractor dynamics are incorporated into the hidden
state to `clean up' representations at each step of a sequence. The attractor
dynamics are trained through an auxillary denoising loss to recover previously
experienced hidden states from noisy versions of those states. This
state-denoised recurrent neural network {SDRNN} performs multiple steps of
internal processing for each external sequence step. On a range of tasks, we
show that the SDRNN outperforms a generic RNN as well as a variant of the SDRNN
with attractor dynamics on the hidden state but without the auxillary loss. We
argue that attractor dynamics---and corresponding connectivity
constraints---are an essential component of the deep learning arsenal and
should be invoked not only for recurrent networks but also for improving deep
feedforward nets and intertask transfer.
Theory and Experiments on Vector Quantized Autoencoders
Authors: Aurko Roy, Ashish Vaswani, Arvind Neelakantan, Niki Parmar
Date: 2018-05-28 00:00:00
Tasks: Image Generation, Knowledge Distillation, Machine Translation, Translation

Deep neural networks with discrete latent variables offer the promise of
better symbolic reasoning, and learning abstractions that are more useful to
new tasks. There has been a surge in interest in discrete latent variable
models, however, despite several recent improvements, the training of discrete
latent variable models has remained challenging and their performance has
mostly failed to match their continuous counterparts. Recent work on vector
quantized autoencoders (VQ-VAE) has made substantial progress in this
direction, with its perplexity almost matching that of a VAE on datasets such
as CIFAR-10. In this work, we investigate an alternate training technique for
VQ-VAE, inspired by its connection to the Expectation Maximization (EM)
algorithm. Training the discrete bottleneck with EM helps us achieve better
image generation results on CIFAR-10, and together with knowledge distillation,
allows us to develop a non-autoregressive machine translation model whose
accuracy almost matches a strong greedy autoregressive baseline Transformer,
while being 3.3 times faster at inference.
A Study on Passage Re-ranking in Embedding based Unsupervised Semantic Search
Authors: Md. Faisal Mahbub Chowdhury, Vijil Chenthamarakshan, Rishav Chakravarti, Alfio M. Gliozzo
Date: 2018-04-22 00:00:00
Tasks: Passage Re-Ranking, Re-Ranking, Sentence, Sentence Embedding, Sentence-Embedding

State of the art approaches for (embedding based) unsupervised semantic
search exploits either compositional similarity (of a query and a passage) or
pair-wise word (or term) similarity (from the query and the passage). By
design, word based approaches do not incorporate similarity in the larger
context (query/passage), while compositional similarity based approaches are
usually unable to take advantage of the most important cues in the context. In
this paper we propose a new compositional similarity based approach, called
variable centroid vector (VCVB), that tries to address both of these
limitations. We also presents results using a different type of compositional
similarity based approach by exploiting universal sentence embedding. We
provide empirical evaluation on two different benchmarks.
Deep Generative Models for Distribution-Preserving Lossy Compression
Authors: Michael Tschannen, Eirikur Agustsson, Mario Lucic
Date: 2018-05-28 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Image Compression, Image Generation

We propose and study the problem of distribution-preserving lossy
compression. Motivated by recent advances in extreme image compression which
allow to maintain artifact-free reconstructions even at very low bitrates, we
propose to optimize the rate-distortion tradeoff under the constraint that the
reconstructed samples follow the distribution of the training data. The
resulting compression system recovers both ends of the spectrum: On one hand,
at zero bitrate it learns a generative model of the data, and at high enough
bitrates it achieves perfect reconstruction. Furthermore, for intermediate
bitrates it smoothly interpolates between learning a generative model of the
training data and perfectly reconstructing the training samples. We study
several methods to approximately solve the proposed optimization problem,
including a novel combination of Wasserstein GAN and Wasserstein Autoencoder,
and present an extensive theoretical and empirical characterization of the
proposed compression systems.
Flexible and accurate inference and learning for deep generative models
Authors: Eszter Vertes, Maneesh Sahani
Date: 2018-05-28 00:00:00
Proceeding: NeurIPS 2018 12

We introduce a new approach to learning in hierarchical latent-variable
generative models called the "distributed distributional code Helmholtz
machine", which emphasises flexibility and accuracy in the inferential process.
In common with the original Helmholtz machine and later variational autoencoder
algorithms (but unlike adverserial methods) our approach learns an explicit
inference or "recognition" model to approximate the posterior distribution over
the latent variables. Unlike in these earlier methods, the posterior
representation is not limited to a narrow tractable parameterised form (nor is
it represented by samples). To train the generative and recognition models we
develop an extended wake-sleep algorithm inspired by the original Helmholtz
Machine. This makes it possible to learn hierarchical latent models with both
discrete and continuous variables, where an accurate posterior representation
is essential. We demonstrate that the new algorithm outperforms current
state-of-the-art methods on synthetic, natural image patch and the MNIST data
sets.