pwc-archive/papers-with-abstracts huggingface dataset:
A Novel Geometric Framework on Gram Matrix Trajectories for Human Behavior Understanding
Authors: Anis Kacem, Mohamed Daoudi, Boulbaba Ben Amor, Stefano Berretti, Juan Carlos Alvarez-Paiva
Date: 2018-06-29 00:00:00
Tasks: Action Recognition, Emotion Recognition, Facial Expression Recognition, Facial Expression Recognition (FER), Temporal Action Localization

In this paper, we propose a novel space-time geometric representation of
human landmark configurations and derive tools for comparison and
classification. We model the temporal evolution of landmarks as parametrized
trajectories on the Riemannian manifold of positive semidefinite matrices of
fixed-rank. Our representation has the benefit to bring naturally a second
desirable quantity when comparing shapes, the spatial covariance, in addition
to the conventional affine-shape representation. We derived then geometric and
computational tools for rate-invariant analysis and adaptive re-sampling of
trajectories, grounding on the Riemannian geometry of the underlying manifold.
Specifically, our approach involves three steps: (1) landmarks are first mapped
into the Riemannian manifold of positive semidefinite matrices of fixed-rank to
build time-parameterized trajectories; (2) a temporal warping is performed on
the trajectories, providing a geometry-aware (dis-)similarity measure between
them; (3) finally, a pairwise proximity function SVM is used to classify them,
incorporating the (dis-)similarity measure into the kernel function. We show
that such representation and metric achieve competitive results in applications
as action recognition and emotion recognition from 3D skeletal data, and facial
expression recognition from videos. Experiments have been conducted on several
publicly available up-to-date benchmarks.
Grapevine: A Wine Prediction Algorithm Using Multi-dimensional Clustering Methods
Authors: Richard Diehl Martinez, Geoffrey Angus, Rooz Mahdavian
Date: 2018-06-29 00:00:00
Tasks: Clustering

We present a method for a wine recommendation system that employs
multidimensional clustering and unsupervised learning methods. Our algorithm
first performs clustering on a large corpus of wine reviews. It then uses the
resulting wine clusters as an approximation of the most common flavor palates,
recommending a user a wine by optimizing over a price-quality ratio within
clusters that they demonstrated a preference for.
YH Technologies at ActivityNet Challenge 2018
Authors: Ting Yao, Xue Li
Date: 2018-06-29 00:00:00
Tasks: Action Localization, Action Recognition, Dense Captioning, Spatio-Temporal Action Localization, Temporal Action Localization

This notebook paper presents an overview and comparative analysis of our
systems designed for the following five tasks in ActivityNet Challenge 2018:
temporal action proposals, temporal action localization, dense-captioning
events in videos, trimmed action recognition, and spatio-temporal action
localization.
Single Index Latent Variable Models for Network Topology Inference
Authors: Jonathan Mei, José M. F. Moura
Date: 2018-06-28 00:00:00
Tasks: regression

A semi-parametric, non-linear regression model in the presence of latent
variables is applied towards learning network graph structure. These latent
variables can correspond to unmodeled phenomena or unmeasured agents in a
complex system of interacting entities. This formulation jointly estimates
non-linearities in the underlying data generation, the direct interactions
between measured entities, and the indirect effects of unmeasured processes on
the observed data. The learning is posed as regularized empirical risk
minimization. Details of the algorithm for learning the model are outlined.
Experiments demonstrate the performance of the learned model on real data.
Task-Driven Convolutional Recurrent Models of the Visual System
Authors: Aran Nayebi, Daniel Bear, Jonas Kubilius, Kohitij Kar, Surya Ganguli, David Sussillo, James J. Dicarlo, Daniel L. K. Yamins
Date: 2018-06-20 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: General Classification, Object Recognition

Feed-forward convolutional neural networks (CNNs) are currently
state-of-the-art for object classification tasks such as ImageNet. Further,
they are quantitatively accurate models of temporally-averaged responses of
neurons in the primate brain's visual system. However, biological visual
systems have two ubiquitous architectural features not shared with typical
CNNs: local recurrence within cortical areas, and long-range feedback from
downstream areas to upstream areas. Here we explored the role of recurrence in
improving classification performance. We found that standard forms of
recurrence (vanilla RNNs and LSTMs) do not perform well within deep CNNs on the
ImageNet task. In contrast, novel cells that incorporated two structural
features, bypassing and gating, were able to boost task accuracy substantially.
We extended these design principles in an automated search over thousands of
model architectures, which identified novel local recurrent cells and
long-range feedback connections useful for object recognition. Moreover, these
task-optimized ConvRNNs matched the dynamics of neural activity in the primate
visual system better than feedforward networks, suggesting a role for the
brain's recurrent connections in performing difficult visual behaviors.
Model-based Hand Pose Estimation for Generalized Hand Shape with Appearance Normalization
Authors: Jan Wöhlke, Shile Li, Dongheui Lee
Date: 2018-07-02 00:00:00
Tasks: Hand Pose Estimation, Pose Estimation

Since the emergence of large annotated datasets, state-of-the-art hand pose
estimation methods have been mostly based on discriminative learning. Recently,
a hybrid approach has embedded a kinematic layer into the deep learning
structure in such a way that the pose estimates obey the physical constraints
of human hand kinematics. However, the existing approach relies on a single
person's hand shape parameters, which are fixed constants. Therefore, the
existing hybrid method has problems to generalize to new, unseen hands. In this
work, we extend the kinematic layer to make the hand shape parameters
learnable. In this way, the learnt network can generalize towards arbitrary
hand shapes. Furthermore, inspired by the idea of Spatial Transformer Networks,
we apply a cascade of appearance normalization networks to decrease the
variance in the input data. The input images are shifted, rotated, and globally
scaled to a similar appearance. The effectiveness and limitations of our
proposed approach are extensively evaluated on the Hands 2017 challenge dataset
and the NYU dataset.
Reaching Human-level Performance in Automatic Grammatical Error Correction: An Empirical Study
Authors: Tao Ge, Furu Wei, Ming Zhou
Date: 2018-07-03 00:00:00
Tasks: Grammatical Error Correction, Sentence

Neural sequence-to-sequence (seq2seq) approaches have proven to be successful
in grammatical error correction (GEC). Based on the seq2seq framework, we
propose a novel fluency boost learning and inference mechanism. Fluency
boosting learning generates diverse error-corrected sentence pairs during
training, enabling the error correction model to learn how to improve a
sentence's fluency from more instances, while fluency boosting inference allows
the model to correct a sentence incrementally with multiple inference steps.
Combining fluency boost learning and inference with convolutional seq2seq
models, our approach achieves the state-of-the-art performance: 75.72 (F_{0.5})
on CoNLL-2014 10 annotation dataset and 62.42 (GLEU) on JFLEG test set
respectively, becoming the first GEC system that reaches human-level
performance (72.58 for CoNLL and 62.37 for JFLEG) on both of the benchmarks.
Viewpoint Estimation-Insights & Model
Authors: Gilad Divon, Ayellet Tal
Date: 2018-07-03 00:00:00
Tasks: General Classification, model, Viewpoint Estimation

This paper addresses the problem of viewpoint estimation of an object in a
given image. It presents five key insights that should be taken into
consideration when designing a CNN that solves the problem. Based on these
insights, the paper proposes a network in which (i) The architecture jointly
solves detection, classification, and viewpoint estimation. (ii) New types of
data are added and trained on. (iii) A novel loss function, which takes into
account both the geometry of the problem and the new types of data, is propose.
Our network improves the state-of-the-art results for this problem by 9.8%.
Proceedings of the 2018 ICML Workshop on Human Interpretability in Machine Learning (WHI 2018)
Authors: Been Kim, Kush R. Varshney, Adrian Weller
Date: 2018-07-03 00:00:00
Tasks: BIG-bench Machine Learning

This is the Proceedings of the 2018 ICML Workshop on Human Interpretability
in Machine Learning (WHI 2018), which was held in Stockholm, Sweden, July 14,
2018. Invited speakers were Barbara Engelhardt, Cynthia Rudin, Fernanda
Vi\'egas, and Martin Wattenberg.
Generalized Bilinear Deep Convolutional Neural Networks for Multimodal Biometric Identification
Authors: Sobhan Soleymani, Amirsina Torfi, Jeremy Dawson, Nasser M. Nasrabadi
Date: 2018-07-03 00:00:00
Tasks: Person Identification

In this paper, we propose to employ a bank of modality-dedicated
Convolutional Neural Networks (CNNs), fuse, train, and optimize them together
for person classification tasks. A modality-dedicated CNN is used for each
modality to extract modality-specific features. We demonstrate that, rather
than spatial fusion at the convolutional layers, the fusion can be performed on
the outputs of the fully-connected layers of the modality-specific CNNs without
any loss of performance and with significant reduction in the number of
parameters. We show that, using multiple CNNs with multimodal fusion at the
feature-level, we significantly outperform systems that use unimodal
representation. We study weighted feature, bilinear, and compact bilinear
feature-level fusion algorithms for multimodal biometric person identification.
Finally, We propose generalized compact bilinear fusion algorithm to deploy
both the weighted feature fusion and compact bilinear schemes. We provide the
results for the proposed algorithms on three challenging databases: CMU
Multi-PIE, BioCop, and BIOMDATA.
End-to-End Learning on 3D Protein Structure for Interface Prediction
Authors: Raphael J. L. Townshend, Rishi Bedi, Patricia A. Suriana, Ron O. Dror
Date: 2018-07-03 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Open-Ended Question Answering, Protein Interface Prediction, Transfer Learning

Despite an explosion in the number of experimentally determined, atomically detailed structures of biomolecules, many critical tasks in structural biology remain data-limited. Whether performance in such tasks can be improved by using large repositories of tangentially related structural data remains an open question. To address this question, we focused on a central problem in biology: predicting how proteins interact with one another---that is, which surfaces of one protein bind to those of another protein. We built a training dataset, the Database of Interacting Protein Structures (DIPS), that contains biases but is two orders of magnitude larger than those used previously. We found that these biases significantly degrade the performance of existing methods on gold-standard data. Hypothesizing that assumptions baked into the hand-crafted features on which these methods depend were the source of the problem, we developed the first end-to-end learning model for protein interface prediction, the Siamese Atomic Surfacelet Network (SASNet). Using only spatial coordinates and identities of atoms, SASNet outperforms state-of-the-art methods trained on gold-standard structural data, even when trained on only 3% of our new dataset. Code and data available at https://github.com/drorlab/DIPS.
Intent Generation for Goal-Oriented Dialogue Systems based on Schema.org Annotations
Authors: Umutcan Şimşek, Dieter Fensel
Date: 2018-07-03 00:00:00
Tasks: Goal-Oriented Dialogue Systems, Natural Language Understanding

Goal-oriented dialogue systems typically communicate with a backend (e.g.
database, Web API) to complete certain tasks to reach a goal. The intents that
a dialogue system can recognize are mostly included to the system by the
developer statically. For an open dialogue system that can work on more than a
small set of well curated data and APIs, this manual intent creation will not
scalable. In this paper, we introduce a straightforward methodology for intent
creation based on semantic annotation of data and services on the web. With
this method, the Natural Language Understanding (NLU) module of a goal-oriented
dialogue system can adapt to newly introduced APIs without requiring heavy
developer involvement. We were able to extract intents and necessary slots to
be filled from schema.org annotations. We were also able to create a set of
initial training sentences for classifying user utterances into the generated
intents. We demonstrate our approach on the NLU module of a state-of-the art
dialogue system development framework.
New Losses for Generative Adversarial Learning
Authors: Victor Berger, Michèle Sebag
Date: 2018-07-03 00:00:00

Generative Adversarial Networks (Goodfellow et al., 2014), a major
breakthrough in the field of generative modeling, learn a discriminator to
estimate some distance between the target and the candidate distributions.
  This paper examines mathematical issues regarding the way the gradients for
the generative model are computed in this context, and notably how to take into
account how the discriminator itself depends on the generator parameters.
  A unifying methodology is presented to define mathematically sound training
objectives for generative models taking this dependency into account in a
robust way, covering both GAN, VAE and some GAN variants as particular cases.
Human-level performance in first-person multiplayer games with population-based deep reinforcement learning
Authors: Max Jaderberg, Wojciech M. Czarnecki, Iain Dunning, Luke Marris, Guy Lever, Antonio Garcia Castaneda, Charles Beattie, Neil C. Rabinowitz, Ari S. Morcos, Avraham Ruderman, Nicolas Sonnerat, Tim Green, Louise Deason, Joel Z. Leibo, David Silver, Demis Hassabis, Koray Kavukcuoglu, Thore Graepel
Date: 2018-07-03 00:00:00
Tasks: Deep Reinforcement Learning, Reinforcement Learning, Reinforcement Learning (RL)

Recent progress in artificial intelligence through reinforcement learning
(RL) has shown great success on increasingly complex single-agent environments
and two-player turn-based games. However, the real-world contains multiple
agents, each learning and acting independently to cooperate and compete with
other agents, and environments reflecting this degree of complexity remain an
open challenge. In this work, we demonstrate for the first time that an agent
can achieve human-level in a popular 3D multiplayer first-person video game,
Quake III Arena Capture the Flag, using only pixels and game points as input.
These results were achieved by a novel two-tier optimisation process in which a
population of independent RL agents are trained concurrently from thousands of
parallel matches with agents playing in teams together and against each other
on randomly generated environments. Each agent in the population learns its own
internal reward signal to complement the sparse delayed reward from winning,
and selects actions using a novel temporally hierarchical representation that
enables the agent to reason at multiple timescales. During game-play, these
agents display human-like behaviours such as navigating, following, and
defending based on a rich learned representation that is shown to encode
high-level game knowledge. In an extensive tournament-style evaluation the
trained agents exceeded the win-rate of strong human players both as teammates
and opponents, and proved far stronger than existing state-of-the-art agents.
These results demonstrate a significant jump in the capabilities of artificial
agents, bringing us closer to the goal of human-level intelligence.
On the Computational Power of Online Gradient Descent
Authors: Vaggos Chatziafratis, Tim Roughgarden, Joshua R. Wang
Date: 2018-07-03 00:00:00

We prove that the evolution of weight vectors in online gradient descent can
encode arbitrary polynomial-space computations, even in very simple learning
settings. Our results imply that, under weak complexity-theoretic assumptions,
it is impossible to reason efficiently about the fine-grained behavior of
online gradient descent.
Dynamic Control of Explore/Exploit Trade-Off In Bayesian Optimization
Authors: Dipti Jasrasaria, Edward O. Pyzer-Knapp
Date: 2018-07-03 00:00:00
Tasks: Bayesian Optimization

Bayesian optimization offers the possibility of optimizing black-box
operations not accessible through traditional techniques. The success of
Bayesian optimization methods such as Expected Improvement (EI) are
significantly affected by the degree of trade-off between exploration and
exploitation. Too much exploration can lead to inefficient optimization
protocols, whilst too much exploitation leaves the protocol open to strong
initial biases, and a high chance of getting stuck in a local minimum.
Typically, a constant margin is used to control this trade-off, which results
in yet another hyper-parameter to be optimized. We propose contextual
improvement as a simple, yet effective heuristic to counter this - achieving a
one-shot optimization strategy. Our proposed heuristic can be swiftly
calculated and improves both the speed and robustness of discovery of optimal
solutions. We demonstrate its effectiveness on both synthetic and real world
problems and explore the unaccounted for uncertainty in the pre-determination
of search hyperparameters controlling explore-exploit trade-off.
Playing against Nature: causal discovery for decision making under uncertainty
Authors: M. Gonzalez-Soto, L. E. Sucar, H. J. Escalante
Date: 2018-07-03 00:00:00
Tasks: Causal Discovery, Decision Making, Decision Making Under Uncertainty, Q-Learning, Sequential Decision Making

We consider decision problems under uncertainty where the options available
to a decision maker and the resulting outcome are related through a causal
mechanism which is unknown to the decision maker. We ask how a decision maker
can learn about this causal mechanism through sequential decision making as
well as using current causal knowledge inside each round in order to make
better choices had she not considered causal knowledge and propose a decision
making procedure in which an agent holds \textit{beliefs} about her environment
which are used to make a choice and are updated using the observed outcome. As
proof of concept, we present an implementation of this causal decision making
model and apply it in a simple scenario. We show that the model achieves a
performance similar to the classic Q-learning while it also acquires a causal
model of the environment.
A Weakly Supervised Adaptive DenseNet for Classifying Thoracic Diseases and Identifying Abnormalities
Authors: Bo Zhou, Yuemeng Li, Jiangcong Wang
Date: 2018-07-03 00:00:00
Tasks: Classification, General Classification

We present a weakly supervised deep learning model for classifying thoracic
diseases and identifying abnormalities in chest radiography. In this work,
instead of learning from medical imaging data with region-level annotations,
our model was merely trained on imaging data with image-level labels to
classify diseases, and is able to identify abnormal image regions
simultaneously. Our model consists of a customized pooling structure and an
adaptive DenseNet front-end, which can effectively recognize possible disease
features for classification and localization tasks. Our method has been
validated on the publicly available ChestX-ray14 dataset. Experimental results
have demonstrated that our classification and localization prediction
performance achieved significant improvement over the previous models on the
ChestX-ray14 dataset. In summary, our network can produce accurate disease
classification and localization, which can potentially support clinical
decisions.
Who did What at Where and When: Simultaneous Multi-Person Tracking and Activity Recognition
Authors: Wenbo Li, Ming-Ching Chang, Siwei Lyu
Date: 2018-07-03 00:00:00
Tasks: Activity Recognition, Visual Tracking

We present a bootstrapping framework to simultaneously improve multi-person
tracking and activity recognition at individual, interaction and social group
activity levels. The inference consists of identifying trajectories of all
pedestrian actors, individual activities, pairwise interactions, and collective
activities, given the observed pedestrian detections. Our method uses a
graphical model to represent and solve the joint tracking and recognition
problems via multi-stages: (1) activity-aware tracking, (2) joint interaction
recognition and occlusion recovery, and (3) collective activity recognition. We
solve the where and when problem with visual tracking, as well as the who and
what problem with recognition. High-order correlations among the visible and
occluded individuals, pairwise interactions, groups, and activities are then
solved using a hypergraph formulation within the Bayesian framework.
Experiments on several benchmarks show the advantages of our approach over
state-of-art methods.
Training behavior of deep neural network in frequency domain
Authors: Zhi-Qin John Xu, Yaoyu Zhang, Yanyang Xiao
Date: 2018-07-03 00:00:00

Why deep neural networks (DNNs) capable of overfitting often generalize well in practice is a mystery [#zhang2016understanding]. To find a potential mechanism, we focus on the study of implicit biases underlying the training process of DNNs. In this work, for both real and synthetic datasets, we empirically find that a DNN with common settings first quickly captures the dominant low-frequency components, and then relatively slowly captures the high-frequency ones. We call this phenomenon Frequency Principle (F-Principle). The F-Principle can be observed over DNNs of various structures, activation functions, and training algorithms in our experiments. We also illustrate how the F-Principle help understand the effect of early-stopping as well as the generalization of DNNs. This F-Principle potentially provides insights into a general principle underlying DNN optimization and generalization.
SpaceNet: A Remote Sensing Dataset and Challenge Series
Authors: Adam Van Etten, Dave Lindenbaum, Todd M. Bacastow
Date: 2018-07-03 00:00:00
Tasks: BIG-bench Machine Learning, Object Detection

Foundational mapping remains a challenge in many parts of the world, particularly in dynamic scenarios such as natural disasters when timely updates are critical. Updating maps is currently a highly manual process requiring a large number of human labelers to either create features or rigorously validate automated outputs. We propose that the frequent revisits of earth imaging satellite constellations may accelerate existing efforts to quickly update foundational maps when combined with advanced machine learning techniques. Accordingly, the SpaceNet partners (CosmiQ Works, Radiant Solutions, and NVIDIA), released a large corpus of labeled satellite imagery on Amazon Web Services (AWS) called SpaceNet. The SpaceNet partners also launched a series of public prize competitions to encourage improvement of remote sensing machine learning algorithms. The first two of these competitions focused on automated building footprint extraction, and the most recent challenge focused on road network extraction. In this paper we discuss the SpaceNet imagery, labels, evaluation metrics, prize challenge results to date, and future plans for the SpaceNet challenge series.