pwc-archive/papers-with-abstracts huggingface dataset:
Constrained Deep Networks: Lagrangian Optimization via Log-Barrier Extensions
Authors: Hoel Kervadec, Jose Dolz, Jing Yuan, Christian Desrosiers, Eric Granger, Ismail Ben Ayed
Date: 2019-04-08 00:00:00
Tasks: Image Segmentation, Semantic Segmentation, Stochastic Optimization, Weakly supervised segmentation

This study investigates imposing hard inequality constraints on the outputs of convolutional neural networks (CNN) during training. Several recent works showed that the theoretical and practical advantages of Lagrangian optimization over simple penalties do not materialize in practice when dealing with modern CNNs involving millions of parameters. Therefore, constrained CNNs are typically handled with penalties. We propose *log-barrier extensions*, which approximate Lagrangian optimization of constrained-CNN problems with a sequence of unconstrained losses. Unlike standard interior-point and log-barrier methods, our formulation does not need an initial feasible solution. The proposed extension yields an upper bound on the duality gap -- generalizing the result of standard log-barriers -- and yielding sub-optimality certificates for feasible solutions. While sub-optimality is not guaranteed for non-convex problems, this result shows that log-barrier extensions are a principled way to approximate Lagrangian optimization for constrained CNNs via implicit dual variables. We report weakly supervised image segmentation experiments, with various constraints, showing that our formulation outperforms substantially the existing constrained-CNN methods, in terms of accuracy, constraint satisfaction and training stability, more so when dealing with a large number of constraints.
Classification of pulsars with Dirichlet process Gaussian mixture model
Authors: F. Ay, G. İnce, M. E. Kamaşak, K. Y. Ekşi
Date: 2019-04-08 00:00:00
Tasks: Classification, General Classification

Young isolated neutron stars (INS) most commonly manifest themselves as rotationally powered pulsars (RPPs) which involve conventional radio pulsars as well as gamma-ray pulsars (GRPs) and rotating radio transients (RRATs). Some other young INS families manifest themselves as anomalous X-ray pulsars (AXPs) and soft gamma-ray repeaters (SGRs) which are commonly accepted as magnetars, i.e. magnetically powered neutron stars with decaying superstrong fields. Yet some other young INS are identified as central compact objects (CCOs) and X-ray dim isolated neutron stars (XDINSs) which are cooling objects powered by their thermal energy. Older pulsars, as a result of a previous long episode of accretion from a companion, manifest themselves as millisecond pulsars and more commonly appear in binary systems. We use Dirichlet process Gaussian mixture model (DPGMM), an unsupervised machine learning algorithm, for analyzing the distribution of these pulsar families in the parameter space of period and period derivative. We compare the average values of the characteristic age, magnetic dipole field strength, surface temperature and transverse velocity of all discovered clusters. We verify that DPGMM is robust and provides hints for inferring relations between different classes of pulsars. We discuss the implications of our findings for the magneto-thermal spin evolution models and fallback discs.
Characterizing the Social Interactions in the Artificial Bee Colony Algorithm
Authors: Lydia Taw, Nishant Gurrapadi, Mariana Macedo, Marcos Oliveira, Diego Pinheiro, Carmelo Bastos-Filho, Ronaldo Menezes
Date: 2019-04-08 00:00:00
Tasks: Diversity

Computational swarm intelligence consists of multiple artificial simple
agents exchanging information while exploring a search space. Despite a rich
literature in the field, with works improving old approaches and proposing new
ones, the mechanism by which complex behavior emerges in these systems is still
not well understood. This literature gap hinders the researchers' ability to
deal with known problems in swarms intelligence such as premature convergence,
and the balance of coordination and diversity among agents. Recent advances in
the literature, however, have proposed to study these systems via the network
that emerges from the social interactions within the swarm (i.e., the
interaction network). In our work, we propose a definition of the interaction
network for the Artificial Bee Colony (ABC) algorithm. With our approach, we
captured striking idiosyncrasies of the algorithm. We uncovered the different
patterns of social interactions that emerge from each type of bee, revealing
the importance of the bees variations throughout the iterations of the
algorithm. We found that ABC exhibits a dynamic information flow through the
use of different bees but lacks continuous coordination between the agents.
Dynamics of Pedestrian Crossing Decisions Based on Vehicle Trajectories in Large-Scale Simulated and Real-World Data
Authors: Jack Terwilliger, Michael Glazer, Henri Schmidt, Josh Domeyer, Heishiro Toyoda, Bruce Mehler, Bryan Reimer, Lex Fridman
Date: 2019-04-08 00:00:00
Tasks: Navigate

Humans, as both pedestrians and drivers, generally skillfully navigate
traffic intersections. Despite the uncertainty, danger, and the non-verbal
nature of communication commonly found in these interactions, there are
surprisingly few collisions considering the total number of interactions. As
the role of automation technology in vehicles grows, it becomes increasingly
critical to understand the relationship between pedestrian and driver behavior:
how pedestrians perceive the actions of a vehicle/driver and how pedestrians
make crossing decisions. The relationship between time-to-arrival (TTA) and
pedestrian gap acceptance (i.e., whether a pedestrian chooses to cross under a
given window of time to cross) has been extensively investigated. However, the
dynamic nature of vehicle trajectories in the context of non-verbal
communication has not been systematically explored. Our work provides evidence
that trajectory dynamics, such as changes in TTA, can be powerful signals in
the non-verbal communication between drivers and pedestrians. Moreover, we
investigate these effects in both simulated and real-world datasets, both
larger than have previously been considered in literature to the best of our
knowledge.
Pushing the Envelope for RGB-based Dense 3D Hand Pose Estimation via Neural Rendering
Authors: Seungryul Baek, Kwang In Kim, Tae-Kyun Kim
Date: 2019-04-08 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D Hand Pose Estimation, 3D Pose Estimation, Data Augmentation, Hand Pose Estimation, Neural Rendering, Pose Estimation

Estimating 3D hand meshes from single RGB images is challenging, due to
intrinsic 2D-3D mapping ambiguities and limited training data. We adopt a
compact parametric 3D hand model that represents deformable and articulated
hand meshes. To achieve the model fitting to RGB images, we investigate and
contribute in three ways: 1) Neural rendering: inspired by recent work on human
body, our hand mesh estimator (HME) is implemented by a neural network and a
differentiable renderer, supervised by 2D segmentation masks and 3D skeletons.
HME demonstrates good performance for estimating diverse hand shapes and
improves pose estimation accuracies. 2) Iterative testing refinement: Our
fitting function is differentiable. We iteratively refine the initial estimate
using the gradients, in the spirit of iterative model fitting methods like ICP.
The idea is supported by the latest research on human body. 3) Self-data
augmentation: collecting sized RGB-mesh (or segmentation mask)-skeleton
triplets for training is a big hurdle. Once the model is successfully fitted to
input RGB images, its meshes i.e. shapes and articulations, are realistic, and
we augment view-points on top of estimated dense hand poses. Experiments using
three RGB-based benchmarks show that our framework offers beyond
state-of-the-art accuracy in 3D pose estimation, as well as recovers dense 3D
hand shapes. Each technical component above meaningfully improves the accuracy
in the ablation study.
Learning to Navigate Unseen Environments: Back Translation with Environmental Dropout
Authors: Hao Tan, Licheng Yu, Mohit Bansal
Date: 2019-04-08 00:00:00
Proceeding: NAACL 2019 6
Tasks: Navigate, Reinforcement Learning, Translation, Vision-Language Navigation

A grand goal in AI is to build a robot that can accurately navigate based on
natural language instructions, which requires the agent to perceive the scene,
understand and ground language, and act in the real-world environment. One key
challenge here is to learn to navigate in new environments that are unseen
during training. Most of the existing approaches perform dramatically worse in
unseen environments as compared to seen ones. In this paper, we present a
generalizable navigational agent. Our agent is trained in two stages. The first
stage is training via mixed imitation and reinforcement learning, combining the
benefits from both off-policy and on-policy optimization. The second stage is
fine-tuning via newly-introduced 'unseen' triplets (environment, path,
instruction). To generate these unseen triplets, we propose a simple but
effective 'environmental dropout' method to mimic unseen environments, which
overcomes the problem of limited seen environment variability. Next, we apply
semi-supervised learning (via back-translation) on these dropped-out
environments to generate new paths and instructions. Empirically, we show that
our agent is substantially better at generalizability when fine-tuned with
these triplets, outperforming the state-of-art approaches by a large margin on
the private unseen test set of the Room-to-Room task, and achieving the top
rank on the leaderboard.
Unsupervised learning of action classes with continuous temporal embedding
Authors: Anna Kukleva, Hilde Kuehne, Fadime Sener, Juergen Gall
Date: 2019-04-08 00:00:00
Proceeding: CVPR 2019 6
Tasks: Action Segmentation, Unsupervised Action Segmentation

The task of temporally detecting and segmenting actions in untrimmed videos
has seen an increased attention recently. One problem in this context arises
from the need to define and label action boundaries to create annotations for
training which is very time and cost intensive. To address this issue, we
propose an unsupervised approach for learning action classes from untrimmed
video sequences. To this end, we use a continuous temporal embedding of
framewise features to benefit from the sequential nature of activities. Based
on the latent space created by the embedding, we identify clusters of temporal
segments across all videos that correspond to semantic meaningful action
classes. The approach is evaluated on three challenging datasets, namely the
Breakfast dataset, YouTube Instructions, and the 50Salads dataset. While
previous works assumed that the videos contain the same high level activity, we
furthermore show that the proposed approach can also be applied to a more
general setting where the content of the videos is unknown.
Data-Driven Design for Fourier Ptychographic Microscopy
Authors: Michael Kellman, Emrah Bostan, Michael Chen, Laura Waller
Date: 2019-04-08 00:00:00
Tasks: Experimental Design, Retrieval, Super-Resolution

Fourier Ptychographic Microscopy (FPM) is a computational imaging method that
is able to super-resolve features beyond the diffraction-limit set by the
objective lens of a traditional microscope. This is accomplished by using
synthetic aperture and phase retrieval algorithms to combine many measurements
captured by an LED array microscope with programmable source patterns. FPM
provides simultaneous large field-of-view and high resolution imaging, but at
the cost of reduced temporal resolution, thereby limiting live cell
applications. In this work, we learn LED source pattern designs that compress
the many required measurements into only a few, with negligible loss in
reconstruction quality or resolution. This is accomplished by recasting the
super-resolution reconstruction as a Physics-based Neural Network and learning
the experimental design to optimize the network's overall performance.
Specifically, we learn LED patterns for different applications (e.g. amplitude
contrast and quantitative phase imaging) and show that the designs we learn
through simulation generalize well in the experimental setting. Further, we
discuss a context-specific loss function, practical memory limitations, and
interpretability of our learned designs.
Accelerated Neural Networks on OpenCL Devices Using SYCL-DNN
Authors: Rod Burns, John Lawson, Duncan McBain, Daniel Soutar
Date: 2019-04-08 00:00:00
Tasks: CPU, GPU

Over the past few years machine learning has seen a renewed explosion of
interest, following a number of studies showing the effectiveness of neural
networks in a range of tasks which had previously been considered incredibly
hard. Neural networks' effectiveness in the fields of image recognition and
natural language processing stems primarily from the vast amounts of data
available to companies and researchers, coupled with the huge amounts of
compute power available in modern accelerators such as GPUs, FPGAs and ASICs.
There are a number of approaches available to developers for utilizing GPGPU
technologies such as SYCL, OpenCL and CUDA, however many applications require
the same low level mathematical routines. Libraries dedicated to accelerating
these common routines allow developers to easily make full use of the available
hardware without requiring low level knowledge of the hardware themselves,
however such libraries are often provided by hardware manufacturers for
specific hardware such as cuDNN for Nvidia hardware or MIOpen for AMD hardware.
  SYCL-DNN is a new open-source library dedicated to providing accelerated
routines for neural network operations which are hardware and vendor agnostic.
Built on top of the SYCL open standard and written entirely in standard C++,
SYCL-DNN allows a user to easily accelerate neural network code for a wide
range of hardware using a modern C++ interface. The library is tested on AMD's
OpenCL for GPU, Intel's OpenCL for CPU and GPU, ARM's OpenCL for Mali GPUs as
well as ComputeAorta's OpenCL for R-Car CV engine and host CPU. In this talk we
will present performance figures for SYCL-DNN on this range of hardware, and
discuss how high performance was achieved on such a varied set of accelerators
with such different hardware features.
Revisiting EmbodiedQA: A Simple Baseline and Beyond
Authors: Yu Wu, Lu Jiang, Yi Yang
Date: 2019-04-08 00:00:00
Tasks: Embodied Question Answering, Question Answering

In Embodied Question Answering (EmbodiedQA), an agent interacts with an environment to gather necessary information for answering user questions. Existing works have laid a solid foundation towards solving this interesting problem. But the current performance, especially in navigation, suggests that EmbodiedQA might be too challenging for the contemporary approaches. In this paper, we empirically study this problem and introduce 1) a simple yet effective baseline that achieves promising performance; 2) an easier and practical setting for EmbodiedQA where an agent has a chance to adapt the trained model to a new environment before it actually answers users questions. In this new setting, we randomly place a few objects in new environments, and upgrade the agent policy by a distillation network to retain the generalization ability from the trained model. On the EmbodiedQA v1 benchmark, under the standard setting, our simple baseline achieves very competitive results to the-state-of-the-art; in the new setting, we found the introduced small change in settings yields a notable gain in navigation.
Knowledge Distillation For Recurrent Neural Network Language Modeling With Trust Regularization
Authors: Yangyang Shi, Mei-Yuh Hwang, Xin Lei, Haoyu Sheng
Date: 2019-04-08 00:00:00
Tasks: Knowledge Distillation, Language Modeling, Language Modelling, speech-recognition, Speech Recognition

Recurrent Neural Networks (RNNs) have dominated language modeling because of
their superior performance over traditional N-gram based models. In many
applications, a large Recurrent Neural Network language model (RNNLM) or an
ensemble of several RNNLMs is used. These models have large memory footprints
and require heavy computation. In this paper, we examine the effect of applying
knowledge distillation in reducing the model size for RNNLMs. In addition, we
propose a trust regularization method to improve the knowledge distillation
training for RNNLMs. Using knowledge distillation with trust regularization, we
reduce the parameter size to a third of that of the previously published best
model while maintaining the state-of-the-art perplexity result on Penn Treebank
data. In a speech recognition N-bestrescoring task, we reduce the RNNLM model
size to 18.5% of the baseline system, with no degradation in word error
rate(WER) performance on Wall Street Journal data set.
Audio Source Separation via Multi-Scale Learning with Dilated Dense U-Nets
Authors: Vivek Sivaraman Narayanaswamy, Sameeksha Katoch, Jayaraman J. Thiagarajan, Huan Song, Andreas Spanias
Date: 2019-04-08 00:00:00
Tasks: Audio Source Separation

Modern audio source separation techniques rely on optimizing sequence model
architectures such as, 1D-CNNs, on mixture recordings to generalize well to
unseen mixtures. Specifically, recent focus is on time-domain based
architectures such as Wave-U-Net which exploit temporal context by extracting
multi-scale features. However, the optimality of the feature extraction process
in these architectures has not been well investigated. In this paper, we
examine and recommend critical architectural changes that forge an optimal
multi-scale feature extraction process. To this end, we replace regular $1-$D
convolutions with adaptive dilated convolutions that have innate capability of
capturing increased context by using large temporal receptive fields. We also
investigate the impact of dense connections on the extraction process that
encourage feature reuse and better gradient flow. The dense connections between
the downsampling and upsampling paths of a U-Net architecture capture
multi-resolution information leading to improved temporal modelling. We
evaluate the proposed approaches on the MUSDB test dataset. In addition to
providing an improved performance over the state-of-the-art, we also provide
insights on the impact of different architectural choices on complex
data-driven solutions for source separation.
Bayesian Neural Networks at Finite Temperature
Authors: Robert J. N. Baldock, Nicola Marzari
Date: 2019-04-08 00:00:00
Tasks: Bayesian Inference, Model Selection

We recapitulate the Bayesian formulation of neural network based classifiers
and show that, while sampling from the posterior does indeed lead to better
generalisation than is obtained by standard optimisation of the cost function,
even better performance can in general be achieved by sampling finite
temperature ($T$) distributions derived from the posterior. Taking the example
of two different deep (3 hidden layers) classifiers for MNIST data, we find
quite different $T$ values to be appropriate in each case. In particular, for a
typical neural network classifier a clear minimum of the test error is observed
at $T>0$. This suggests an early stopping criterion for full batch simulated
annealing: cool until the average validation error starts to increase, then
revert to the parameters with the lowest validation error. As $T$ is increased
classifiers transition from accurate classifiers to classifiers that have
higher training error than assigning equal probability to each class. Efficient
studies of these temperature-induced effects are enabled using a
replica-exchange Hamiltonian Monte Carlo simulation technique. Finally, we show
how thermodynamic integration can be used to perform model selection for deep
neural networks. Similar to the Laplace approximation, this approach assumes
that the posterior is dominated by a single mode. Crucially, however, no
assumption is made about the shape of that mode and it is not required to
precisely compute and invert the Hessian.
AutoSeM: Automatic Task Selection and Mixing in Multi-Task Learning
Authors: Han Guo, Ramakanth Pasunuru, Mohit Bansal
Date: 2019-04-08 00:00:00
Proceeding: NAACL 2019 6
Tasks: Bayesian Optimization, Inductive Bias, Multi-Task Learning, Thompson Sampling

Multi-task learning (MTL) has achieved success over a wide range of problems,
where the goal is to improve the performance of a primary task using a set of
relevant auxiliary tasks. However, when the usefulness of the auxiliary tasks
w.r.t. the primary task is not known a priori, the success of MTL models
depends on the correct choice of these auxiliary tasks and also a balanced
mixing ratio of these tasks during alternate training. These two problems could
be resolved via manual intuition or hyper-parameter tuning over all
combinatorial task choices, but this introduces inductive bias or is not
scalable when the number of candidate auxiliary tasks is very large. To address
these issues, we present AutoSeM, a two-stage MTL pipeline, where the first
stage automatically selects the most useful auxiliary tasks via a
Beta-Bernoulli multi-armed bandit with Thompson Sampling, and the second stage
learns the training mixing ratio of these selected auxiliary tasks via a
Gaussian Process based Bayesian optimization framework. We conduct several MTL
experiments on the GLUE language understanding tasks, and show that our AutoSeM
framework can successfully find relevant auxiliary tasks and automatically
learn their mixing ratio, achieving significant performance boosts on several
primary tasks. Finally, we present ablations for each stage of AutoSeM and
analyze the learned auxiliary task choices.
Learning monocular depth estimation infusing traditional stereo knowledge
Authors: Fabio Tosi, Filippo Aleotti, Matteo Poggi, Stefano Mattoccia
Date: 2019-04-08 00:00:00
Proceeding: CVPR 2019 6
Tasks: Depth Estimation, Image Generation, Monocular Depth Estimation, Stereo Matching, Stereo Matching Hand

Depth estimation from a single image represents a fascinating, yet
challenging problem with countless applications. Recent works proved that this
task could be learned without direct supervision from ground truth labels
leveraging image synthesis on sequences or stereo pairs. Focusing on this
second case, in this paper we leverage stereo matching in order to improve
monocular depth estimation. To this aim we propose monoResMatch, a novel deep
architecture designed to infer depth from a single input image by synthesizing
features from a different point of view, horizontally aligned with the input
image, performing stereo matching between the two cues. In contrast to previous
works sharing this rationale, our network is the first trained end-to-end from
scratch. Moreover, we show how obtaining proxy ground truth annotation through
traditional stereo algorithms, such as Semi-Global Matching, enables more
accurate monocular depth estimation still countering the need for expensive
depth labels by keeping a self-supervised approach. Exhaustive experimental
results prove how the synergy between i) the proposed monoResMatch architecture
and ii) proxy-supervision attains state-of-the-art for self-supervised
monocular depth estimation. The code is publicly available at
https://github.com/fabiotosi92/monoResMatch-Tensorflow.