pwc-archive/papers-with-abstracts huggingface dataset:
Hessian-based Analysis of Large Batch Training and Robustness to Adversaries
Authors: Zhewei Yao, Amir Gholami, Qi Lei, Kurt Keutzer, Michael W. Mahoney
Date: 2018-02-22 00:00:00
Proceeding: NeurIPS 2018 12

Large batch size training of Neural Networks has been shown to incur accuracy
loss when trained with the current methods. The exact underlying reasons for
this are still not completely understood. Here, we study large batch size
training through the lens of the Hessian operator and robust optimization. In
particular, we perform a Hessian based study to analyze exactly how the
landscape of the loss function changes when training with large batch size. We
compute the true Hessian spectrum, without approximation, by back-propagating
the second derivative. Extensive experiments on multiple networks show that
saddle-points are not the cause for generalization gap of large batch size
training, and the results consistently show that large batch converges to
points with noticeably higher Hessian spectrum. Furthermore, we show that
robust training allows one to favor flat areas, as points with large Hessian
spectrum show poor robustness to adversarial perturbation. We further study
this relationship, and provide empirical and theoretical proof that the inner
loop for robust training is a saddle-free optimization problem \textit{almost
everywhere}. We present detailed experiments with five different network
architectures, including a residual network, tested on MNIST, CIFAR-10, and
CIFAR-100 datasets. We have open sourced our method which can be accessed at
[1].
Scalable synthesis of safety certificates from data with application to learning-based control
Authors: Kim P. Wabersich, Melanie N. Zeilinger
Date: 2017-11-30 00:00:00

The control of complex systems faces a trade-off between high performance and safety guarantees, which in particular restricts the application of learning-based methods to safety-critical systems. A recently proposed framework to address this issue is the use of a safety controller, which guarantees to keep the system within a safe region of the state space. This paper introduces efficient techniques for the synthesis of a safe set and control law, which offer improved scalability properties by relying on approximations based on convex optimization problems. The first proposed method requires only an approximate linear system model and Lipschitz continuity of the unknown nonlinear dynamics. The second method extends the results by showing how a Gaussian process prior on the unknown system dynamics can be used in order to reduce conservatism of the resulting safe set. We demonstrate the results with numerical examples, including an autonomous convoy of vehicles.
Neuronal Circuit Policies
Authors: Mathias Lechner, Ramin M. Hasani, Radu Grosu
Date: 2018-03-22 00:00:00
Tasks: Reinforcement Learning, Reinforcement Learning (RL)

We propose an effective way to create interpretable control agents, by
re-purposing the function of a biological neural circuit model, to govern
simulated and real world reinforcement learning (RL) test-beds. We model the
tap-withdrawal (TW) neural circuit of the nematode, C. elegans, a circuit
responsible for the worm's reflexive response to external mechanical touch
stimulations, and learn its synaptic and neuronal parameters as a policy for
controlling basic RL tasks. We also autonomously park a real rover robot on a
pre-defined trajectory, by deploying such neuronal circuit policies learned in
a simulated environment. For reconfiguration of the purpose of the TW neural
circuit, we adopt a search-based RL algorithm. We show that our neuronal
policies perform as good as deep neural network policies with the advantage of
realizing interpretable dynamics at the cell level.
Convolutional Networks for Object Category and 3D Pose Estimation from 2D Images
Authors: Siddharth Mahendran, Haider Ali, Rene Vidal
Date: 2017-11-20 00:00:00
Tasks: 3D Pose Estimation, Object, Object Categorization, Pose Estimation

Current CNN-based algorithms for recovering the 3D pose of an object in an
image assume knowledge about both the object category and its 2D localization
in the image. In this paper, we relax one of these constraints and propose to
solve the task of joint object category and 3D pose estimation from an image
assuming known 2D localization. We design a new architecture for this task
composed of a feature network that is shared between subtasks, an object
categorization network built on top of the feature network, and a collection of
category dependent pose regression networks. We also introduce suitable loss
functions and a training method for the new architecture. Experiments on the
challenging PASCAL3D+ dataset show state-of-the-art performance in the joint
categorization and pose estimation task. Moreover, our performance on the joint
task is comparable to the performance of state-of-the-art methods on the
simpler 3D pose estimation with known object category task.
Aligning Across Large Gaps in Time
Authors: Hunter Goforth, Simon Lucey
Date: 2018-03-22 00:00:00
Tasks: 3D Reconstruction, Image Registration

We present a method of temporally-invariant image registration for outdoor
scenes, with invariance across time of day, across seasonal variations, and
across decade-long periods, for low- and high-texture scenes. Our method can be
useful for applications in remote sensing, GPS-denied UAV localization, 3D
reconstruction, and many others. Our method leverages a recently proposed
approach to image registration, where fully-convolutional neural networks are
used to create feature maps which can be registered using the
Inverse-Composition Lucas-Kanade algorithm (ICLK). We show that invariance that
is learned from satellite imagery can be transferable to time-lapse data
captured by webcams mounted on buildings near ground-level.
Fast Semantic Segmentation on Video Using Block Motion-Based Feature Interpolation
Authors: Samvit Jain, Joseph E. Gonzalez
Date: 2018-03-21 00:00:00
Tasks: Optical Flow Estimation, Semantic Segmentation

Convolutional networks optimized for accuracy on challenging, dense
prediction tasks are prohibitively slow to run on each frame in a video. The
spatial similarity of nearby video frames, however, suggests opportunity to
reuse computation. Existing work has explored basic feature reuse and feature
warping based on optical flow, but has encountered limits to the speedup
attainable with these techniques. In this paper, we present a new, two part
approach to accelerating inference on video. First, we propose a fast feature
propagation technique that utilizes the block motion vectors present in
compressed video (e.g. H.264 codecs) to cheaply propagate features from frame
to frame. Second, we develop a novel feature estimation scheme, termed feature
interpolation, that fuses features propagated from enclosing keyframes to
render accurate feature estimates, even at sparse keyframe frequencies. We
evaluate our system on the Cityscapes and CamVid datasets, comparing to both a
frame-by-frame baseline and related work. We find that we are able to
substantially accelerate segmentation on video, achieving near real-time frame
rates (20.1 frames per second) on large images (960 x 720 pixels), while
maintaining competitive accuracy. This represents an improvement of almost 6x
over the single-frame baseline and 2.5x over the fastest prior work.
Understanding Measures of Uncertainty for Adversarial Example Detection
Authors: Lewis Smith, Yarin Gal
Date: 2018-03-22 00:00:00
Tasks: General Classification

Measuring uncertainty is a promising technique for detecting adversarial
examples, crafted inputs on which the model predicts an incorrect class with
high confidence. But many measures of uncertainty exist, including predictive
en- tropy and mutual information, each capturing different types of
uncertainty. We study these measures, and shed light on why mutual information
seems to be effective at the task of adversarial example detection. We
highlight failure modes for MC dropout, a widely used approach for estimating
uncertainty in deep models. This leads to an improved understanding of the
drawbacks of current methods, and a proposal to improve the quality of
uncertainty estimates using probabilistic model ensembles. We give illustrative
experiments using MNIST to demonstrate the intuition underlying the different
measures of uncertainty, as well as experiments on a real world Kaggle dogs vs
cats classification dataset.
Text2Shape: Generating Shapes from Natural Language by Learning Joint Embeddings
Authors: Kevin Chen, Christopher B. Choy, Manolis Savva, Angel X. Chang, Thomas Funkhouser, Silvio Savarese
Date: 2018-03-22 00:00:00
Tasks: Metric Learning, Retrieval

We present a method for generating colored 3D shapes from natural language.
To this end, we first learn joint embeddings of freeform text descriptions and
colored 3D shapes. Our model combines and extends learning by association and
metric learning approaches to learn implicit cross-modal connections, and
produces a joint representation that captures the many-to-many relations
between language and physical properties of 3D shapes such as color and shape.
To evaluate our approach, we collect a large dataset of natural language
descriptions for physical 3D objects in the ShapeNet dataset. With this learned
joint embedding we demonstrate text-to-shape retrieval that outperforms
baseline approaches. Using our embeddings with a novel conditional Wasserstein
GAN framework, we generate colored 3D shapes from text. Our method is the first
to connect natural language text with realistic 3D objects exhibiting rich
variations in color, texture, and shape detail. See video at
https://youtu.be/zraPvRdl13Q
Live Target Detection with Deep Learning Neural Network and Unmanned Aerial Vehicle on Android Mobile Device
Authors: Ali Canberk Anar, Erkan Bostanci, Mehmet Serdar Guzel
Date: 2018-03-19 00:00:00

This paper describes the stages faced during the development of an Android
program which obtains and decodes live images from DJI Phantom 3 Professional
Drone and implements certain features of the TensorFlow Android Camera Demo
application. Test runs were made and outputs of the application were noted. A
lake was classified as seashore, breakwater and pier with the proximities of
24.44%, 21.16% and 12.96% respectfully. The joystick of the UAV controller and
laptop keyboard was classified with the proximities of 19.10% and 13.96%
respectfully. The laptop monitor was classified as screen, monitor and
television with the proximities of 18.77%, 14.76% and 14.00% respectfully. The
computer used during the development of this study was classified as notebook
and laptop with the proximities of 20.04% and 11.68% respectfully. A tractor
parked at a parking lot was classified with the proximity of 12.88%. A group of
cars in the same parking lot were classified as sports car, racer and
convertible with the proximities of 31.75%, 18.64% and 13.45% respectfully at
an inference time of 851ms.
Learning SO(3) Equivariant Representations with Spherical CNNs
Authors: Carlos Esteves, Christine Allen-Blanchette, Ameesh Makadia, Kostas Daniilidis
Date: 2017-11-17 00:00:00
Proceeding: ECCV 2018 9
Tasks: 3D Classification, Data Augmentation, General Classification, Retrieval

We address the problem of 3D rotation equivariance in convolutional neural
networks. 3D rotations have been a challenging nuisance in 3D classification
tasks requiring higher capacity and extended data augmentation in order to
tackle it. We model 3D data with multi-valued spherical functions and we
propose a novel spherical convolutional network that implements exact
convolutions on the sphere by realizing them in the spherical harmonic domain.
Resulting filters have local symmetry and are localized by enforcing smooth
spectra. We apply a novel pooling on the spectral domain and our operations are
independent of the underlying spherical resolution throughout the network. We
show that networks with much lower capacity and without requiring data
augmentation can exhibit performance comparable to the state of the art in
standard retrieval and classification benchmarks.
KonIQ-10k: Towards an ecologically valid and large-scale IQA database
Authors: Hanhe Lin, Vlad Hosu, Dietmar Saupe
Date: 2018-03-22 00:00:00
Tasks: Diversity, Image Quality Assessment, valid

The main challenge in applying state-of-the-art deep learning methods to
predict image quality in-the-wild is the relatively small size of existing
quality scored datasets. The reason for the lack of larger datasets is the
massive resources required in generating diverse and publishable content. We
present a new systematic and scalable approach to create large-scale, authentic
and diverse image datasets for Image Quality Assessment (IQA). We show how we
built an IQA database, KonIQ-10k, consisting of 10,073 images, on which we
performed very large scale crowdsourcing experiments in order to obtain
reliable quality ratings from 1,467 crowd workers (1.2 million ratings). We
argue for its ecological validity by analyzing the diversity of the dataset, by
comparing it to state-of-the-art IQA databases, and by checking the reliability
of our user studies.
Essentially No Barriers in Neural Network Energy Landscape
Authors: Felix Draxler, Kambis Veschgini, Manfred Salmhofer, Fred A. Hamprecht
Date: 2018-03-02 00:00:00
Proceeding: ICML 2018 7

Training neural networks involves finding minima of a high-dimensional
non-convex loss function. Knowledge of the structure of this energy landscape
is sparse. Relaxing from linear interpolations, we construct continuous paths
between minima of recent neural network architectures on CIFAR10 and CIFAR100.
Surprisingly, the paths are essentially flat in both the training and test
landscapes. This implies that neural networks have enough capacity for
structural changes, or that these changes are small between minima. Also, each
minimum has at least one vanishing Hessian eigenvalue in addition to those
resulting from trivial invariance.
Efficient Recurrent Neural Networks using Structured Matrices in FPGAs
Authors: Zhe Li, Shuo Wang, Caiwen Ding, Qinru Qiu, Yanzhi Wang, Yun Liang
Date: 2018-03-20 00:00:00
Tasks: Model Compression, Time Series, Time Series Analysis

Recurrent Neural Networks (RNNs) are becoming increasingly important for time
series-related applications which require efficient and real-time
implementations. The recent pruning based work ESE suffers from degradation of
performance/energy efficiency due to the irregular network structure after
pruning. We propose block-circulant matrices for weight matrix representation
in RNNs, thereby achieving simultaneous model compression and acceleration. We
aim to implement RNNs in FPGA with highest performance and energy efficiency,
with certain accuracy requirement (negligible accuracy degradation).
Experimental results on actual FPGA deployments shows that the proposed
framework achieves a maximum energy efficiency improvement of 35.7$\times$
compared with ESE.
Distributed Representations of Signed Networks
Authors: Mohammad Raihanul Islam, B. Aditya Prakash, Naren Ramakrishnan
Date: 2017-02-22 00:00:00
Tasks: Community Detection, Document Embedding, Network Embedding

Recent successes in word embedding and document embedding have motivated
researchers to explore similar representations for networks and to use such
representations for tasks such as edge prediction, node label prediction, and
community detection. Such network embedding methods are largely focused on
finding distributed representations for unsigned networks and are unable to
discover embeddings that respect polarities inherent in edges. We propose
SIGNet, a fast scalable embedding method suitable for signed networks. Our
proposed objective function aims to carefully model the social structure
implicit in signed networks by reinforcing the principles of social balance
theory. Our method builds upon the traditional word2vec family of embedding
approaches and adds a new targeted node sampling strategy to maintain
structural balance in higher-order neighborhoods. We demonstrate the
superiority of SIGNet over state-of-the-art methods proposed for both signed
and unsigned networks on several real world datasets from different domains. In
particular, SIGNet offers an approach to generate a richer vocabulary of
features of signed networks to support representation and reasoning.
Word sense induction using word embeddings and community detection in complex networks
Authors: Edilson A. Corrêa Jr., Diego R. Amancio
Date: 2018-03-22 00:00:00
Tasks: Community Detection, Word Embeddings, Word Sense Disambiguation, Word Sense Induction

Word Sense Induction (WSI) is the ability to automatically induce word senses
from corpora. The WSI task was first proposed to overcome the limitations of
manually annotated corpus that are required in word sense disambiguation
systems. Even though several works have been proposed to induce word senses,
existing systems are still very limited in the sense that they make use of
structured, domain-specific knowledge sources. In this paper, we devise a
method that leverages recent findings in word embeddings research to generate
context embeddings, which are embeddings containing information about the
semantical context of a word. In order to induce senses, we modeled the set of
ambiguous words as a complex network. In the generated network, two instances
(nodes) are connected if the respective context embeddings are similar. Upon
using well-established community detection methods to cluster the obtained
context embeddings, we found that the proposed method yields excellent
performance for the WSI task. Our method outperformed competing algorithms and
baselines, in a completely unsupervised manner and without the need of any
additional structured knowledge source.
Attention, Learn to Solve Routing Problems!
Authors: Wouter Kool, Herke van Hoof, Max Welling
Date: 2018-03-22 00:00:00
Proceeding: ICLR 2019 5
Tasks: Combinatorial Optimization

The recently presented idea to learn heuristics for combinatorial
optimization problems is promising as it can save costly development. However,
to push this idea towards practical implementation, we need better models and
better ways of training. We contribute in both directions: we propose a model
based on attention layers with benefits over the Pointer Network and we show
how to train this model using REINFORCE with a simple baseline based on a
deterministic greedy rollout, which we find is more efficient than using a
value function. We significantly improve over recent learned heuristics for the
Travelling Salesman Problem (TSP), getting close to optimal results for
problems up to 100 nodes. With the same hyperparameters, we learn strong
heuristics for two variants of the Vehicle Routing Problem (VRP), the
Orienteering Problem (OP) and (a stochastic variant of) the Prize Collecting
TSP (PCTSP), outperforming a wide range of baselines and getting results close
to highly optimized and specialized algorithms.
Locally Private Bayesian Inference for Count Models
Authors: Aaron Schein, Zhiwei Steven Wu, Alexandra Schofield, Mingyuan Zhou, Hanna Wallach
Date: 2018-03-22 00:00:00
Tasks: Bayesian Inference, Link Prediction, Privacy Preserving

We present a general method for privacy-preserving Bayesian inference in
Poisson factorization, a broad class of models that includes some of the most
widely used models in the social sciences. Our method satisfies limited
precision local privacy, a generalization of local differential privacy, which
we introduce to formulate privacy guarantees appropriate for sparse count data.
We develop an MCMC algorithm that approximates the locally private posterior
over model parameters given data that has been locally privatized by the
geometric mechanism (Ghosh et al., 2012). Our solution is based on two
insights: 1) a novel reinterpretation of the geometric mechanism in terms of
the Skellam distribution (Skellam, 1946) and 2) a general theorem that relates
the Skellam to the Bessel distribution (Yuan & Kalbfleisch, 2000). We
demonstrate our method in two case studies on real-world email data in which we
show that our method consistently outperforms the commonly-used naive approach,
obtaining higher quality topics in text and more accurate link prediction in
networks. On some tasks, our privacy-preserving method even outperforms
non-private inference which conditions on the true data.
Estimating the Success of Unsupervised Image to Image Translation
Authors: Sagie Benaim, Tomer Galanti, Lior Wolf
Date: 2017-12-21 00:00:00
Proceeding: ECCV 2018 9
Tasks: Generalization Bounds, Image-to-Image Translation, Translation, Unsupervised Image-To-Image Translation

While in supervised learning, the validation error is an unbiased estimator
of the generalization (test) error and complexity-based generalization bounds
are abundant, no such bounds exist for learning a mapping in an unsupervised
way. As a result, when training GANs and specifically when using GANs for
learning to map between domains in a completely unsupervised way, one is forced
to select the hyperparameters and the stopping epoch by subjectively examining
multiple options. We propose a novel bound for predicting the success of
unsupervised cross domain mapping methods, which is motivated by the recently
proposed Simplicity Principle. The bound can be applied both in expectation,
for comparing hyperparameters and for selecting a stopping criterion, or per
sample, in order to predict the success of a specific cross-domain translation.
The utility of the bound is demonstrated in an extensive set of experiments
employing multiple recent algorithms. Our code is available at
https://github.com/sagiebenaim/gan_bound .
BSD-GAN: Branched Generative Adversarial Network for Scale-Disentangled Representation Learning and Image Synthesis
Authors: Zili Yi, Zhiqin Chen, Hao Cai, Wendong Mao, Minglun Gong, Hao Zhang
Date: 2018-03-22 00:00:00
Tasks: Generative Adversarial Network, Image Generation, Representation Learning

We introduce BSD-GAN, a novel multi-branch and scale-disentangled training method which enables unconditional Generative Adversarial Networks (GANs) to learn image representations at multiple scales, benefiting a wide range of generation and editing tasks. The key feature of BSD-GAN is that it is trained in multiple branches, progressively covering both the breadth and depth of the network, as resolutions of the training images increase to reveal finer-scale features. Specifically, each noise vector, as input to the generator network of BSD-GAN, is deliberately split into several sub-vectors, each corresponding to, and is trained to learn, image representations at a particular scale. During training, we progressively "de-freeze" the sub-vectors, one at a time, as a new set of higher-resolution images is employed for training and more network layers are added. A consequence of such an explicit sub-vector designation is that we can directly manipulate and even combine latent (sub-vector) codes which model different feature scales.Extensive experiments demonstrate the effectiveness of our training method in scale-disentangled learning of image representations and synthesis of novel image contents, without any extra labels and without compromising quality of the synthesized high-resolution images. We further demonstrate several image generation and manipulation applications enabled or improved by BSD-GAN. Source codes are available at https://github.com/duxingren14/BSD-GAN.