pwc-archive/papers-with-abstracts huggingface dataset:
Stance-In-Depth Deep Neural Approach to Stance Classification
Authors: Gayathri Rajendran, Bhadrachalam Chitturi, Prabaharan Poornachandran
Date: 2018-06-08 00:00:00
Proceeding: International Conference on Computational Intelligence and Data Science (ICCIDS 2018) 2018 6
Tasks: Classification, Fake News Detection, General Classification, Satire Detection, Stance Classification, Stance Detection

Understanding the user intention from text is a problem of growing interest. The social media like Twitter, Facebook etc. extract
user intention to analyze the behaviour of a user which in turn is employed for bot recognition, satire detection, fake news
detection etc.. The process of identifying stance of a user from the text is called stance detection. This article compares the
headline and body pair of a news article and classifies the pair as related or unrelated. The related pair is further classified into
agree, disagree, discuss. We call related as detailed classification and unrelated as broad classification. We employ deep neural
nets for feature extraction and stance classification. RNN models and its extensions showed significant variations in the
classification of detailed class. Bidirectional LSTM model achieved the best accuracy for broad as well as detailed classification
Focal Visual-Text Attention for Memex Question Answering
Authors: Junwei Liang, Lu Jiang, Liangliang Cao, Yannis Kalantidis, Li-Jia Li, and Alexander Hauptmann
Date: 2018-12-14 00:00:00
Proceeding: IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2018 12
Tasks: Memex Question Answering, Question Answering, Visual Question Answering, Visual Question Answering (VQA)

Recent insights on language and vision with neural networks have been successfully applied to simple single-image visual question answering. However, to tackle real-life question answering problems on multimedia collections such as personal photo albums, we have to look at whole collections with sequences of photos. This paper proposes a new multimodal MemexQA task: given a sequence of photos from a user, the goal is to automatically answer questions that help users recover their memory about an event captured in these photos. In addition to a text answer, a few grounding photos are also given to justify the answer. The grounding photos are necessary as they help users quickly verifying the answer. Towards solving the task, we 1) present the MemexQA dataset, the first publicly available multimodal question answering dataset consisting of real personal photo albums; 2) propose an end-to-end trainable network that makes use of a hierarchical process to dynamically determine what media and what time to focus on in the sequential data to answer the question. Experimental results on the MemexQA dataset demonstrate that our model outperforms strong baselines and yields the most relevant grounding photos on this challenging task.
Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples
Authors: Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin, Pascal Lamblin, Utku Evci, Kelvin Xu, Ross Goroshin, Carles Gelada, Kevin Swersky, Pierre-Antoine Manzagol, Hugo Larochelle
Date: 2019-03-07 00:00:00
Proceeding: ICLR 2020 1
Tasks: Few-Shot Image Classification, General Classification, Meta-Learning

Few-shot classification refers to learning a classifier for new classes given only a few examples. While a plethora of models have emerged to tackle it, we find the procedure and datasets that are used to assess their progress lacking. To address this limitation, we propose Meta-Dataset: a new benchmark for training and evaluating models that is large-scale, consists of diverse datasets, and presents more realistic tasks. We experiment with popular baselines and meta-learners on Meta-Dataset, along with a competitive method that we propose. We analyze performance as a function of various characteristics of test tasks and examine the models' ability to leverage diverse training sources for improving their generalization. We also propose a new set of baselines for quantifying the benefit of meta-learning in Meta-Dataset. Our extensive experimentation has uncovered important research challenges and we hope to inspire work in these directions.
RAVEN: A Dataset for Relational and Analogical Visual rEasoNing
Authors: Chi Zhang, Feng Gao, Baoxiong Jia, Yixin Zhu, Song-Chun Zhu
Date: 2019-03-07 00:00:00
Proceeding: CVPR 2019 6
Tasks: Object Recognition, Question Answering, Visual Question Answering, Visual Question Answering (VQA), Visual Reasoning

Dramatic progress has been witnessed in basic vision tasks involving
low-level perception, such as object recognition, detection, and tracking.
Unfortunately, there is still an enormous performance gap between artificial
vision systems and human intelligence in terms of higher-level vision problems,
especially ones involving reasoning. Earlier attempts in equipping machines
with high-level reasoning have hovered around Visual Question Answering (VQA),
one typical task associating vision and language understanding. In this work,
we propose a new dataset, built in the context of Raven's Progressive Matrices
(RPM) and aimed at lifting machine intelligence by associating vision with
structural, relational, and analogical reasoning in a hierarchical
representation. Unlike previous works in measuring abstract reasoning using
RPM, we establish a semantic link between vision and reasoning by providing
structure representation. This addition enables a new type of abstract
reasoning by jointly operating on the structure representation. Machine
reasoning ability using modern computer vision is evaluated in this newly
proposed dataset. Additionally, we also provide human performance as a
reference. Finally, we show consistent improvement across all models by
incorporating a simple neural module that combines visual understanding and
structure reasoning.
SR-LSTM: State Refinement for LSTM towards Pedestrian Trajectory Prediction
Authors: Pu Zhang, Wanli Ouyang, Pengfei Zhang, Jianru Xue, Nanning Zheng
Date: 2019-03-07 00:00:00
Proceeding: CVPR 2019 6
Tasks: Pedestrian Trajectory Prediction, Trajectory Prediction

In crowd scenarios, reliable trajectory prediction of pedestrians requires
insightful understanding of their social behaviors. These behaviors have been
well investigated by plenty of studies, while it is hard to be fully expressed
by hand-craft rules. Recent studies based on LSTM networks have shown great
ability to learn social behaviors. However, many of these methods rely on
previous neighboring hidden states but ignore the important current intention
of the neighbors. In order to address this issue, we propose a data-driven
state refinement module for LSTM network (SR-LSTM), which activates the
utilization of the current intention of neighbors, and jointly and iteratively
refines the current states of all participants in the crowd through a message
passing mechanism. To effectively extract the social effect of neighbors, we
further introduce a social-aware information selection mechanism consisting of
an element-wise motion gate and a pedestrian-wise attention to select useful
message from neighboring pedestrians. Experimental results on two public
datasets, i.e. ETH and UCY, demonstrate the effectiveness of our proposed
SR-LSTM and we achieves state-of-the-art results.
Weakly Supervised Complementary Parts Models for Fine-Grained Image Classification from the Bottom Up
Authors: Weifeng Ge, Xiangru Lin, Yizhou Yu
Date: 2019-03-07 00:00:00
Proceeding: CVPR 2019 6
Tasks: Fine-Grained Image Classification, General Classification, image-classification, Image Classification, Instance Segmentation, Object, object-detection, Object Detection, Semantic Segmentation, Weakly Supervised Object Detection

Given a training dataset composed of images and corresponding category
labels, deep convolutional neural networks show a strong ability in mining
discriminative parts for image classification. However, deep convolutional
neural networks trained with image level labels only tend to focus on the most
discriminative parts while missing other object parts, which could provide
complementary information. In this paper, we approach this problem from a
different perspective. We build complementary parts models in a weakly
supervised manner to retrieve information suppressed by dominant object parts
detected by convolutional neural networks. Given image level labels only, we
first extract rough object instances by performing weakly supervised object
detection and instance segmentation using Mask R-CNN and CRF-based
segmentation. Then we estimate and search for the best parts model for each
object instance under the principle of preserving as much diversity as
possible. In the last stage, we build a bi-directional long short-term memory
(LSTM) network to fuze and encode the partial information of these
complementary parts into a comprehensive feature for image classification.
Experimental results indicate that the proposed method not only achieves
significant improvement over our baseline models, but also outperforms
state-of-the-art algorithms by a large margin (6.7%, 2.8%, 5.2% respectively)
on Stanford Dogs 120, Caltech-UCSD Birds 2011-200 and Caltech 256.
COIN: A Large-scale Dataset for Comprehensive Instructional Video Analysis
Authors: Yansong Tang, Dajun Ding, Yongming Rao, Yu Zheng, Danyang Zhang, Lili Zhao, Jiwen Lu, Jie zhou
Date: 2019-03-07 00:00:00
Proceeding: CVPR 2019 6
Tasks: Action Detection

There are substantial instructional videos on the Internet, which enables us
to acquire knowledge for completing various tasks. However, most existing
datasets for instructional video analysis have the limitations in diversity and
scale,which makes them far from many real-world applications where more diverse
activities occur. Moreover, it still remains a great challenge to organize and
harness such data. To address these problems, we introduce a large-scale
dataset called "COIN" for COmprehensive INstructional video analysis. Organized
with a hierarchical structure, the COIN dataset contains 11,827 videos of 180
tasks in 12 domains (e.g., vehicles, gadgets, etc.) related to our daily life.
With a new developed toolbox, all the videos are annotated effectively with a
series of step descriptions and the corresponding temporal boundaries.
Furthermore, we propose a simple yet effective method to capture the
dependencies among different steps, which can be easily plugged into
conventional proposal-based action detection methods for localizing important
steps in instructional videos. In order to provide a benchmark for
instructional video analysis, we evaluate plenty of approaches on the COIN
dataset under different evaluation criteria. We expect the introduction of the
COIN dataset will promote the future in-depth research on instructional video
analysis for the community.
Focused Quantization for Sparse CNNs
Authors: Yiren Zhao, Xitong Gao, Daniel Bates, Robert Mullins, Cheng-Zhong Xu
Date: 2019-03-07 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Model Compression, Neural Network Compression, Quantization

Deep convolutional neural networks (CNNs) are powerful tools for a wide range of vision tasks, but the enormous amount of memory and compute resources required by CNNs pose a challenge in deploying them on constrained devices. Existing compression techniques, while excelling at reducing model sizes, struggle to be computationally friendly. In this paper, we attend to the statistical properties of sparse CNNs and present focused quantization, a novel quantization strategy based on power-of-two values, which exploits the weight distributions after fine-grained pruning. The proposed method dynamically discovers the most effective numerical representation for weights in layers with varying sparsities, significantly reducing model sizes. Multiplications in quantized CNNs are replaced with much cheaper bit-shift operations for efficient inference. Coupled with lossless encoding, we built a compression pipeline that provides CNNs with high compression ratios (CR), low computation cost and minimal loss in accuracy. In ResNet-50, we achieved a 18.08x CR with only 0.24% loss in top-5 accuracy, outperforming existing compression methods. We fully compressed a ResNet-18 and found that it is not only higher in CR and top-5 accuracy, but also more hardware efficient as it requires fewer logic gates to implement when compared to other state-of-the-art quantization methods assuming the same throughput.
Accurate inference of crowdsourcing properties when using efficient allocation strategies
Authors: Abigail Hotaling, James Bagrow
Date: 2019-03-07 00:00:00

Allocation strategies improve the efficiency of crowdsourcing by decreasing the work needed to complete individual tasks accurately. However, these algorithms introduce bias by preferentially allocating workers onto easy tasks, leading to sets of completed tasks that are no longer representative of all tasks. This bias challenges inference of problem-wide properties such as typical task difficulty or crowd properties such as worker completion times, important information that goes beyond the crowd responses themselves. Here we study inference about problem properties when using an allocation algorithm to improve crowd efficiency. We introduce Decision-Explicit Probability Sampling (DEPS), a novel method to perform inference of problem properties while accounting for the potential bias introduced by an allocation strategy. Experiments on real and synthetic crowdsourcing data show that DEPS outperforms baseline inference methods while still leveraging the efficiency gains of the allocation method. The ability to perform accurate inference of general properties when using non-representative data allows crowdsourcers to extract more knowledge out of a given crowdsourced dataset.
Lifted Weight Learning of Markov Logic Networks Revisited
Authors: Ondrej Kuzelka, Vyacheslav Kungurtsev
Date: 2019-03-07 00:00:00

We study lifted weight learning of Markov logic networks. We show that there
is an algorithm for maximum-likelihood learning of 2-variable Markov logic
networks which runs in time polynomial in the domain size. Our results are
based on existing lifted-inference algorithms and recent algorithmic results on
computing maximum entropy distributions.
Learning to Speak and Act in a Fantasy Text Adventure Game
Authors: Jack Urbanek, Angela Fan, Siddharth Karamcheti, Saachi Jain, Samuel Humeau, Emily Dinan, Tim Rocktäschel, Douwe Kiela, Arthur Szlam, Jason Weston
Date: 2019-03-07 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Retrieval

We introduce a large scale crowdsourced text adventure game as a research
platform for studying grounded dialogue. In it, agents can perceive, emote, and
act whilst conducting dialogue with other agents. Models and humans can both
act as characters within the game. We describe the results of training
state-of-the-art generative and retrieval models in this setting. We show that
in addition to using past dialogue, these models are able to effectively use
the state of the underlying world to condition their predictions. In
particular, we show that grounding on the details of the local environment,
including location descriptions, and the objects (and their affordances) and
characters (and their previous actions) present within it allows better
predictions of agent behavior and dialogue. We analyze the ingredients
necessary for successful grounding in this setting, and how each of these
factors relate to agents that can talk and act successfully.
Self-Tuning Networks: Bilevel Optimization of Hyperparameters using Structured Best-Response Functions
Authors: Matthew MacKay, Paul Vicol, Jon Lorraine, David Duvenaud, Roger Grosse
Date: 2019-03-07 00:00:00
Proceeding: ICLR 2019 5
Tasks: Bilevel Optimization, Data Augmentation, Hyperparameter Optimization

Hyperparameter optimization can be formulated as a bilevel optimization
problem, where the optimal parameters on the training set depend on the
hyperparameters. We aim to adapt regularization hyperparameters for neural
networks by fitting compact approximations to the best-response function, which
maps hyperparameters to optimal weights and biases. We show how to construct
scalable best-response approximations for neural networks by modeling the
best-response as a single network whose hidden units are gated conditionally on
the regularizer. We justify this approximation by showing the exact
best-response for a shallow linear network with L2-regularized Jacobian can be
represented by a similar gating mechanism. We fit this model using a
gradient-based hyperparameter optimization algorithm which alternates between
approximating the best-response around the current hyperparameters and
optimizing the hyperparameters using the approximate best-response function.
Unlike other gradient-based approaches, we do not require differentiating the
training loss with respect to the hyperparameters, allowing us to tune discrete
hyperparameters, data augmentation hyperparameters, and dropout probabilities.
Because the hyperparameters are adapted online, our approach discovers
hyperparameter schedules that can outperform fixed hyperparameter values.
Empirically, our approach outperforms competing hyperparameter optimization
methods on large-scale deep learning problems. We call our networks, which
update their own hyperparameters online during training, Self-Tuning Networks
(STNs).
Label Embedded Dictionary Learning for Image Classification
Authors: Shuai Shao, Yan-Jiang Wang, Bao-Di Liu, Weifeng Liu, Rui Xu
Date: 2019-03-07 00:00:00
Tasks: Classification, Dictionary Learning, General Classification, image-classification, Image Classification

Recently, label consistent k-svd (LC-KSVD) algorithm has been successfully
applied in image classification. The objective function of LC-KSVD is consisted
of reconstruction error, classification error and discriminative sparse codes
error with L0-norm sparse regularization term. The L0-norm, however, leads to
NP-hard problem. Despite some methods such as orthogonal matching pursuit can
help solve this problem to some extent, it is quite difficult to find the
optimum sparse solution. To overcome this limitation, we propose a label
embedded dictionary learning (LEDL) method to utilise the L1-norm as the sparse
regularization term so that we can avoid the hard-to-optimize problem by
solving the convex optimization problem. Alternating direction method of
multipliers and blockwise coordinate descent algorithm are then exploited to
optimize the corresponding objective function. Extensive experimental results
on six benchmark datasets illustrate that the proposed algorithm has achieved
superior performance compared to some conventional classification algorithms.
Quantum Latent Semantic Analysis
Authors: Fabio A. González, Juan C. Caicedo
Date: 2019-03-07 00:00:00
Tasks: Information Retrieval, Retrieval

The main goal of this paper is to explore latent topic analysis (LTA), in the
context of quantum information retrieval. LTA is a valuable technique for
document analysis and representation, which has been extensively used in
information retrieval and machine learning. Different LTA techniques have been
proposed, some based on geometrical modeling (such as latent semantic analysis,
LSA) and others based on a strong statistical foundation. However, these two
different approaches are not usually mixed. Quantum information retrieval has
the remarkable virtue of combining both geometry and probability in a common
principled framework. We built on this quantum framework to propose a new LTA
method, which has a clear geometrical motivation but also supports a
well-founded probabilistic interpretation. An initial exploratory
experimentation was performed on three standard data sets. The results show
that the proposed method outperforms LSA on two of the three datasets. These
results suggests that the quantum-motivated representation is an alternative
for geometrical latent topic modeling worthy of further exploration.
Composite Event Recognition for Maritime Monitoring
Authors: Manolis Pitsikalis, Alexander Artikis, Richard Dreo, Cyril Ray, Elena Camossi, Anne-Laure Jousselme
Date: 2019-03-07 00:00:00
Tasks: Computational Efficiency, Position

Maritime monitoring systems support safe shipping as they allow for the real-time detection of dangerous, suspicious and illegal vessel activities. We present such a system using the Run-Time Event Calculus, a composite event recognition system with formal, declarative semantics. For effective recognition, we developed a library of maritime patterns in close collaboration with domain experts. We present a thorough evaluation of the system and the patterns both in terms of predictive accuracy and computational efficiency, using real-world datasets of vessel position streams and contextual geographical information.
RLOC: Neurobiologically Inspired Hierarchical Reinforcement Learning Algorithm for Continuous Control of Nonlinear Dynamical Systems
Authors: Ekaterina Abramova, Luke Dickens, Daniel Kuhn, Aldo Faisal
Date: 2019-03-07 00:00:00
Tasks: continuous-control, Continuous Control, Hierarchical Reinforcement Learning, Model-based Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Nonlinear optimal control problems are often solved with numerical methods
that require knowledge of system's dynamics which may be difficult to infer,
and that carry a large computational cost associated with iterative
calculations. We present a novel neurobiologically inspired hierarchical
learning framework, Reinforcement Learning Optimal Control, which operates on
two levels of abstraction and utilises a reduced number of controllers to solve
nonlinear systems with unknown dynamics in continuous state and action spaces.
Our approach is inspired by research at two levels of abstraction: first, at
the level of limb coordination human behaviour is explained by linear optimal
feedback control theory. Second, in cognitive tasks involving learning symbolic
level action selection, humans learn such problems using model-free and
model-based reinforcement learning algorithms. We propose that combining these
two levels of abstraction leads to a fast global solution of nonlinear control
problems using reduced number of controllers. Our framework learns the local
task dynamics from naive experience and forms locally optimal infinite horizon
Linear Quadratic Regulators which produce continuous low-level control. A
top-level reinforcement learner uses the controllers as actions and learns how
to best combine them in state space while maximising a long-term reward. A
single optimal control objective function drives high-level symbolic learning
by providing training signals on desirability of each selected controller. We
show that a small number of locally optimal linear controllers are able to
solve global nonlinear control problems with unknown dynamics when combined
with a reinforcement learner in this hierarchical framework. Our algorithm
competes in terms of computational cost and solution quality with sophisticated
control algorithms and we illustrate this with solutions to benchmark problems.