pwc-archive/papers-with-abstracts huggingface dataset:
RADMPC: A Fast Decentralized Approach for Chance-Constrained Multi-Vehicle Path-Planning
Authors: Aaron Huang, Benjamin J. Ayton, Brian C. Williams
Date: 2018-11-25 00:00:00
Tasks: Model Predictive Control

Robust multi-vehicle path-planning is important for ensuring the safety of
multi-vehicle systems in applications like transportation, search and rescue,
and robotic exploration. Chance-constrained methods like Iterative Risk
Allocation (IRA)\cite{IRA} have been developed for situations where
environmental disturbances are unbounded. However, chance-constrained methods
for the multi-vehicle case generally use centralized strategies where the
vehicle set is planned with couplings between all vehicle pairs. This approach
is intractable as fleet size increases because computation time is exponential
with respect to the number of vehicles being planned over due to a polynomial
increase in coupling constraints between vehicle pairs. We present a faster
approach for chance-constrained multi-vehicle path-planning that relies upon a
decentralized path-planning method called Risk-Aware Decentralized Model
Predictive Control (RADMPC) to rapidly approximate a centralized IRA approach.
The RADMPC approximation is evaluated for vehicle interactions to determine the
vehicle sets that should be planned in a coupled manner. Applying IRA to the
smaller vehicle sets determined from the RADMPC approximation rapidly plans
safe paths for the entire fleet. A Monte Carlo simulation analysis demonstrates
the correctness of our approach and a significant improvement in computation
time compared to a centralized IRA approach.
Steady-state Non-Line-of-Sight Imaging
Authors: Wenzheng Chen, Simon Daneau, Fahim Mannan, Felix Heide
Date: 2018-11-24 00:00:00
Proceeding: CVPR 2019 6

Conventional intensity cameras recover objects in the direct line-of-sight of
the camera, whereas occluded scene parts are considered lost in this process.
Non-line-of-sight imaging (NLOS) aims at recovering these occluded objects by
analyzing their indirect reflections on visible scene surfaces. Existing NLOS
methods temporally probe the indirect light transport to unmix light paths
based on their travel time, which mandates specialized instrumentation that
suffers from low photon efficiency, high cost, and mechanical scanning. We
depart from temporal probing and demonstrate steady-state NLOS imaging using
conventional intensity sensors and continuous illumination. Instead of assuming
perfectly isotropic scattering, the proposed method exploits directionality in
the hidden surface reflectance, resulting in (small) spatial variation of their
indirect reflections for varying illumination. To tackle the shape-dependence
of these variations, we propose a trainable architecture which learns to map
diffuse indirect reflections to scene reflectance using only synthetic training
data. Relying on consumer color image sensors, with high fill factor, high
quantum efficiency and low read-out noise, we demonstrate high-fidelity color
NLOS imaging for scene configurations tackled before with picosecond time
resolution.
RGB-D Based Action Recognition with Light-weight 3D Convolutional Networks
Authors: Haokui Zhang, Ying Li, Peng Wang, Yu Liu, Chunhua Shen
Date: 2018-11-24 00:00:00
Tasks: Action Recognition, Temporal Action Localization

Different from RGB videos, depth data in RGB-D videos provide key
complementary information for tristimulus visual data which potentially could
achieve accuracy improvement for action recognition. However, most of the
existing action recognition models solely using RGB videos limit the
performance capacity. Additionally, the state-of-the-art action recognition
models, namely 3D convolutional neural networks (3D-CNNs) contain tremendous
parameters suffering from computational inefficiency. In this paper, we propose
a series of 3D light-weight architectures for action recognition based on RGB-D
data. Compared with conventional 3D-CNN models, the proposed light-weight
3D-CNNs have considerably less parameters involving lower computation cost,
while it results in favorable recognition performance. Experimental results on
two public benchmark datasets show that our models can approximate or
outperform the state-of-the-art approaches. Specifically, on the RGB+D-NTU
(NTU) dataset, we achieve 93.2% and 97.6% for cross-subject and cross-view
measurement, and on the Northwestern-UCLA Multiview Action 3D (N-UCLA) dataset,
we achieve 95.5% accuracy of cross-view.
Biscotti: A Ledger for Private and Secure Peer-to-Peer Machine Learning
Authors: Muhammad Shayan, Clement Fung, Chris J. M. Yoon, Ivan Beschastnikh
Date: 2018-11-24 00:00:00
Tasks: BIG-bench Machine Learning, Federated Learning, Privacy Preserving

Federated Learning is the current state of the art in supporting secure multi-party machine learning (ML): data is maintained on the owner's device and the updates to the model are aggregated through a secure protocol. However, this process assumes a trusted centralized infrastructure for coordination, and clients must trust that the central service does not use the byproducts of client data. In addition to this, a group of malicious clients could also harm the performance of the model by carrying out a poisoning attack. As a response, we propose Biscotti: a fully decentralized peer to peer (P2P) approach to multi-party ML, which uses blockchain and cryptographic primitives to coordinate a privacy-preserving ML process between peering clients. Our evaluation demonstrates that Biscotti is scalable, fault tolerant, and defends against known attacks. For example, Biscotti is able to protect the privacy of an individual client's update and the performance of the global model at scale when 30% of adversaries are trying to poison the model. The implementation can be found at: https://github.com/DistributedML/Biscotti
TGE-viz : Transition Graph Embedding for Visualization of Plan Traces and Domains
Authors: Sriram Gopalakrishnan, Subbarao Kambhampati
Date: 2018-11-24 00:00:00
Tasks: Graph Embedding

Existing work for plan trace visualization in automated planning uses
pipeline-style visualizations, similar to plans in Gantt charts. Such
visualization do not capture the domain structure or dependencies between the
various fluents and actions. Additionally, plan traces in such visualizations
cannot be easily compared with one another without parsing the details of
individual actions, which imposes a higher cognitive load. We introduce
TGE-viz, a technique to visualize plan traces within an embedding of the entire
transition graph of a domain in low dimensional space. TGE-viz allows users to
visualize and criticize plans more intuitively for mixed-initiative planning.
It also allows users to visually appraise the structure of domains and the
dependencies in it.
Conditional Recurrent Flow: Conditional Generation of Longitudinal Samples with Applications to Neuroimaging
Authors: Seong Jae Hwang, Zirui Tao, Won Hwa Kim, Vikas Singh
Date: 2018-11-24 00:00:00
Proceeding: ICCV 2019 10

Generative models using neural network have opened a door to large-scale
studies for various application domains, especially for studies that suffer
from lack of real samples to obtain statistically robust inference. Typically,
these generative models would train on existing data to learn the underlying
distribution of the measurements (e.g., images) in latent spaces conditioned on
covariates (e.g., image labels), and generate independent samples that are
identically distributed in the latent space. Such models may work for
cross-sectional studies, however, they are not suitable to generate data for
longitudinal studies that focus on "progressive" behavior in a sequence of
data. In practice, this is a quite common case in various neuroimaging studies
whose goal is to characterize a trajectory of pathologies of a specific disease
even from early stages. This may be too ambitious especially when the sample
size is small (e.g., up to a few hundreds). Motivated from the setup above, we
seek to develop a conditional generative model for longitudinal data generation
by designing an invertable neural network. Inspired by recurrent nature of
longitudinal data, we propose a novel neural network that incorporates
recurrent subnetwork and context gating to include smooth transition in a
sequence of generated data. Our model is validated on a video sequence dataset
and a longitudinal AD dataset with various experimental settings for
qualitative and quantitative evaluations of the generated samples. The results
with the AD dataset captures AD specific group differences with sufficiently
generated longitudinal samples that are consistent with existing literature,
which implies a great potential to be applicable to other disease studies.
Matching Disparate Image Pairs Using Shape-Aware ConvNets
Authors: Shefali Srivastava, Abhimanyu Chopra, Arun CS Kumar, Suchendra M. Bhandarkar, Deepak Sharma
Date: 2018-11-24 00:00:00
Tasks: Graph Matching, Matching Disparate Images

An end-to-end trainable ConvNet architecture, that learns to harness the
power of shape representation for matching disparate image pairs, is proposed.
Disparate image pairs are deemed those that exhibit strong affine variations in
scale, viewpoint and projection parameters accompanied by the presence of
partial or complete occlusion of objects and extreme variations in ambient
illumination. Under these challenging conditions, neither local nor global
feature-based image matching methods, when used in isolation, have been
observed to be effective. The proposed correspondence determination scheme for
matching disparate images exploits high-level shape cues that are derived from
low-level local feature descriptors, thus combining the best of both worlds. A
graph-based representation for the disparate image pair is generated by
constructing an affinity matrix that embeds the distances between feature
points in two images, thus modeling the correspondence determination problem as
one of graph matching. The eigenspectrum of the affinity matrix, i.e., the
learned global shape representation, is then used to further regress the
transformation or homography that defines the correspondence between the source
image and target image. The proposed scheme is shown to yield state-of-the-art
results for both, coarse-level shape matching as well as fine point-wise
correspondence determination.
Deep Learning Inference in Facebook Data Centers: Characterization, Performance Optimizations and Hardware Implications
Authors: Jongsoo Park, Maxim Naumov, Protonu Basu, Summer Deng, Aravind Kalaiah, Daya Khudia, James Law, Parth Malani, Andrey Malevich, Satish Nadathur, Juan Pino, Martin Schatz, Alexander Sidorov, Viswanath Sivakumar, Andrew Tulloch, Xiaodong Wang, Yiming Wu, Hector Yuen, Utku Diril, Dmytro Dzhulgakov, Kim Hazelwood, Bill Jia, Yangqing Jia, Lin Qiao, Vijay Rao, Nadav Rotem, Sungjoo Yoo, Mikhail Smelyanskiy
Date: 2018-11-24 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning

The application of deep learning techniques resulted in remarkable
improvement of machine learning models. In this paper provides detailed
characterizations of deep learning models used in many Facebook social network
services. We present computational characteristics of our models, describe high
performance optimizations targeting existing systems, point out their
limitations and make suggestions for the future general-purpose/accelerated
inference hardware. Also, we highlight the need for better co-design of
algorithms, numerics and computing platforms to address the challenges of
workloads often run in data centers.
Forward Stability of ResNet and Its Variants
Authors: Linan Zhang, Hayden Schaeffer
Date: 2018-11-24 00:00:00

The residual neural network (ResNet) is a popular deep network architecture
which has the ability to obtain high-accuracy results on several image
processing problems. In order to analyze the behavior and structure of ResNet,
recent work has been on establishing connections between ResNets and
continuous-time optimal control problems. In this work, we show that the
post-activation ResNet is related to an optimal control problem with
differential inclusions, and provide continuous-time stability results for the
differential inclusion associated with ResNet. Motivated by the stability
conditions, we show that alterations of either the architecture or the
optimization problem can generate variants of ResNet which improve the
theoretical stability bounds. In addition, we establish stability bounds for
the full (discrete) network associated with two variants of ResNet, in
particular, bounds on the growth of the features and a measure of the
sensitivity of the features with respect to perturbations. These results also
help to show the relationship between the depth, regularization, and stability
of the feature space. Computational experiments on the proposed variants show
that the accuracy of ResNet is preserved and that the accuracy seems to be
monotone with respect to the depth and various corruptions.
Hydra: A Peer to Peer Distributed Training & Data Collection Framework
Authors: Vaibhav Mathur, Karanbir Chahal
Date: 2018-11-24 00:00:00
Tasks: Reinforcement Learning

The world needs diverse and unbiased data to train deep learning models.
Currently data comes from a variety of sources that are unmoderated to a large
extent. The outcomes of training neural networks with unverified data yields
biased models with various strains of homophobia, sexism and racism. Another
trend observed in the world of deep learning is the rise of distributed
training. Although cloud companies provide high performance compute for
training models in the form of GPU's connected with a low latency network,
using these services comes at a high cost. We propose Hydra, a system that
seeks to solve both of these problems in a novel manner by proposing a
decentralized distributed framework which utilizes the substantial amount of
idle compute of everyday electronic devices like smartphones and desktop
computers for training and data collection purposes. Hydra couples a
specialized distributed training framework on a network of these low powered
devices with a reward scheme that incentivizes users to provide high quality
data to unleash the compute capability on this training framework. Such a
system has the ability to capture data from a wide variety of diverse sources
which has been an issue in the current scenario of deep learning. Hydra brings
in several new innovations in training on low powered devices including a fault
tolerant version of the All Reduce algorithm. Furthermore we introduce a
reinforcement learning policy to decide the size of training jobs on different
machines on a heterogeneous cluster of devices with varying network latencies
for Synchronous SGD. The novel thing about such a network is the ability of
each machine to shut down and resume training capabilities at any point of time
without restarting the overall training. To enable such an asynchronous
behaviour we propose a communication framework inspired by the Bittorrent
protocol and the Kademlia DHT.
Hardware Conditioned Policies for Multi-Robot Transfer Learning
Authors: Tao Chen, Adithyavairavan Murali, Abhinav Gupta
Date: 2018-11-24 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Deep Reinforcement Learning, Industrial Robots, Reinforcement Learning, Transfer Learning, Transfer Reinforcement Learning

Deep reinforcement learning could be used to learn dexterous robotic policies
but it is challenging to transfer them to new robots with vastly different
hardware properties. It is also prohibitively expensive to learn a new policy
from scratch for each robot hardware due to the high sample complexity of
modern state-of-the-art algorithms. We propose a novel approach called
\textit{Hardware Conditioned Policies} where we train a universal policy
conditioned on a vector representation of robot hardware. We considered robots
in simulation with varied dynamics, kinematic structure, kinematic lengths and
degrees-of-freedom. First, we use the kinematic structure directly as the
hardware encoding and show great zero-shot transfer to completely novel robots
not seen during training. For robots with lower zero-shot success rate, we also
demonstrate that fine-tuning the policy network is significantly more
sample-efficient than training a model from scratch. In tasks where knowing the
agent dynamics is important for success, we learn an embedding for robot
hardware and show that policies conditioned on the encoding of hardware tend to
generalize and transfer well. The code and videos are available on the project
webpage: https://sites.google.com/view/robot-transfer-hcp.
MEMOIR: Multi-class Extreme Classification with Inexact Margin
Authors: Anton Belyy, Aleksei Sholokhov
Date: 2018-11-24 00:00:00
Tasks: Classification, General Classification, Multi-class Classification

Multi-class classification with a very large number of classes, or extreme classification, is a challenging problem from both statistical and computational perspectives. Most of the classical approaches to multi-class classification, including one-vs-rest or multi-class support vector machines, require the exact estimation of the classifier's margin, at both the training and the prediction steps making them intractable in extreme classification scenarios. In this paper, we study the impact of computing an approximate margin using nearest neighbor (ANN) search structures combined with locality-sensitive hashing (LSH). This approximation allows to dramatically reduce both the training and the prediction time without a significant loss in performance. We theoretically prove that this approximation does not lead to a significant loss of the risk of the model and provide empirical evidence over five publicly available large scale datasets, showing that the proposed approach is highly competitive with respect to state-of-the-art approaches on time, memory and performance measures.
On Periodic Functions as Regularizers for Quantization of Neural Networks
Authors: Maxim Naumov, Utku Diril, Jongsoo Park, Benjamin Ray, Jedrzej Jablonski, Andrew Tulloch
Date: 2018-11-24 00:00:00
Tasks: Quantization

Deep learning models have been successfully used in computer vision and many
other fields. We propose an unorthodox algorithm for performing quantization of
the model parameters. In contrast with popular quantization schemes based on
thresholds, we use a novel technique based on periodic functions, such as
continuous trigonometric sine or cosine as well as non-continuous hat
functions. We apply these functions component-wise and add the sum over the
model parameters as a regularizer to the model loss during training. The
frequency and amplitude hyper-parameters of these functions can be adjusted
during training. The regularization pushes the weights into discrete points
that can be encoded as integers. We show that using this technique the
resulting quantized models exhibit the same accuracy as the original ones on
CIFAR-10 and ImageNet datasets.
FANet: Quality-Aware Feature Aggregation Network for Robust RGB-T Tracking
Authors: Yabin Zhu, Chenglong Li, Bin Luo, Jin Tang
Date: 2018-11-24 00:00:00
Tasks: Rgb-T Tracking, Visual Tracking

This paper investigates how to perform robust visual tracking in adverse and challenging conditions using complementary visual and thermal infrared data (RGBT tracking). We propose a novel deep network architecture called qualityaware Feature Aggregation Network (FANet) for robust RGBT tracking. Unlike existing RGBT trackers, our FANet aggregates hierarchical deep features within each modality to handle the challenge of significant appearance changes caused by deformation, low illumination, background clutter and occlusion. In particular, we employ the operations of max pooling to transform these hierarchical and multi-resolution features into uniform space with the same resolution, and use 1x1 convolution operation to compress feature dimensions to achieve more effective hierarchical feature aggregation. To model the interactions between RGB and thermal modalities, we elaborately design an adaptive aggregation subnetwork to integrate features from different modalities based on their reliabilities and thus are able to alleviate noise effects introduced by low-quality sources. The whole FANet is trained in an end-to-end manner. Extensive experiments on large-scale benchmark datasets demonstrate the high-accurate performance against other state-of-the-art RGBT tracking methods.
Robust RGB-D Face Recognition Using Attribute-Aware Loss
Authors: Luo Jiang, Juyong Zhang, Bailin Deng
Date: 2018-11-24 00:00:00
Tasks: Attribute, Face Recognition

Existing convolutional neural network (CNN) based face recognition algorithms typically learn a discriminative feature mapping, using a loss function that enforces separation of features from different classes and/or aggregation of features within the same class. However, they may suffer from bias in the training data such as uneven sampling density, because they optimize the adjacency relationship of the learned features without considering the proximity of the underlying faces. Moreover, since they only use facial images for training, the learned feature mapping may not correctly indicate the relationship of other attributes such as gender and ethnicity, which can be important for some face recognition applications. In this paper, we propose a new CNN-based face recognition approach that incorporates such attributes into the training process. Using an attribute-aware loss function that regularizes the feature mapping using attribute proximity, our approach learns more discriminative features that are correlated with the attributes. We train our face recognition model on a large-scale RGB-D data set with over 100K identities captured under real application conditions. By comparing our approach with other methods on a variety of experiments, we demonstrate that depth channel and attribute-aware loss greatly improve the accuracy and robustness of face recognition.
Tell, Draw, and Repeat: Generating and Modifying Images Based on Continual Linguistic Instruction
Authors: Alaaeldin El-Nouby, Shikhar Sharma, Hannes Schulz, Devon Hjelm, Layla El Asri, Samira Ebrahimi Kahou, Yoshua Bengio, Graham W. Taylor
Date: 2018-11-24 00:00:00
Proceeding: ICCV 2019 10
Tasks: Image Generation, Text to Image Generation, Text-to-Image Generation

Conditional text-to-image generation is an active area of research, with many possible applications. Existing research has primarily focused on generating a single image from available conditioning information in one step. One practical extension beyond one-step generation is a system that generates an image iteratively, conditioned on ongoing linguistic input or feedback. This is significantly more challenging than one-step generation tasks, as such a system must understand the contents of its generated images with respect to the feedback history, the current feedback, as well as the interactions among concepts present in the feedback history. In this work, we present a recurrent image generation model which takes into account both the generated output up to the current step as well as all past instructions for generation. We show that our model is able to generate the background, add new objects, and apply simple transformations to existing objects. We believe our approach is an important step toward interactive generation. Code and data is available at: https://www.microsoft.com/en-us/research/project/generative-neural-visual-artist-geneva/ .
OCLEP+: One-class Anomaly and Intrusion Detection Using Minimal Length of Emerging Patterns
Authors: Guozhu Dong, Sai Kiran Pentukar
Date: 2018-11-24 00:00:00
Tasks: Classification, General Classification, Intrusion Detection, One-Class Classification

This paper presents a method called One-class Classification using Length
statistics of Emerging Patterns Plus (OCLEP+).