pwc-archive/papers-with-abstracts huggingface dataset:
Accelerating Incremental Gradient Optimization with Curvature Information
Authors: Hoi-To Wai, Wei Shi, Cesar A. Uribe, Angelia Nedich, Anna Scaglione
Date: 2018-05-31 00:00:00

This paper studies an acceleration technique for incremental aggregated gradient ({\sf IAG}) method through the use of \emph{curvature} information for solving strongly convex finite sum optimization problems. These optimization problems of interest arise in large-scale learning applications. Our technique utilizes a curvature-aided gradient tracking step to produce accurate gradient estimates incrementally using Hessian information. We propose and analyze two methods utilizing the new technique, the curvature-aided IAG ({\sf CIAG}) method and the accelerated CIAG ({\sf A-CIAG}) method, which are analogous to gradient method and Nesterov's accelerated gradient method, respectively. Setting $\kappa$ to be the condition number of the objective function, we prove the $R$ linear convergence rates of $1 - \frac{4c_0 \kappa}{(\kappa+1)^2}$ for the {\sf CIAG} method, and $1 - \sqrt{\frac{c_1}{2\kappa}}$ for the {\sf A-CIAG} method, where $c_0,c_1 \leq 1$ are constants inversely proportional to the distance between the initial point and the optimal solution. When the initial iterate is close to the optimal solution, the $R$ linear convergence rates match with the gradient and accelerated gradient method, albeit {\sf CIAG} and {\sf A-CIAG} operate in an incremental setting with strictly lower computation complexity. Numerical experiments confirm our findings. The source codes used for this paper can be found on \url{http://github.com/hoitowai/ciag/}.
Question Answering through Transfer Learning from Large Fine-grained Supervision Data
Authors: Sewon Min, Minjoon Seo, Hannaneh Hajishirzi
Date: 2017-02-07 00:00:00
Proceeding: ACL 2017 7
Tasks: Question Answering, Transfer Learning

We show that the task of question answering (QA) can significantly benefit
from the transfer learning of models trained on a different large, fine-grained
QA dataset. We achieve the state of the art in two well-studied QA datasets,
WikiQA and SemEval-2016 (Task 3A), through a basic transfer learning technique
from SQuAD. For WikiQA, our model outperforms the previous best model by more
than 8%. We demonstrate that finer supervision provides better guidance for
learning lexical and syntactic information than coarser supervision, through
quantitative results and visual analysis. We also show that a similar transfer
learning procedure achieves the state of the art on an entailment task.
Technical Report: Inconsistency in Answer Set Programs and Extensions
Authors: Christoph Redl
Date: 2018-05-31 00:00:00

Answer Set Programming (ASP) is a well-known problem solving approach based
on nonmonotonic logic programs. HEX-programs extend ASP with external atoms for
accessing arbitrary external information, which can introduce values that do
not appear in the input program. In this work we consider inconsistent ASP- and
HEX-programs, i.e., programs without answer sets. We study characterizations of
inconsistency, introduce a novel notion for explaining inconsistencies in terms
of input facts, analyze the complexity of reasoning tasks in context of
inconsistency analysis, and present techniques for computing inconsistency
reasons. This theoretical work is motivated by two concrete applications, which
we also present. The first one is the new modeling technique of query answering
over subprograms as a convenient alternative to the well-known saturation
technique. The second application is a new evaluation algorithm for
HEX-programs based on conflict-driven learning for programs with multiple
components: while for certain program classes previous techniques suffer an
evaluation bottleneck, the new approach shows significant, potentially
exponential speedup in our experiments. Since well-known ASP extensions such as
constraint ASP and DL-programs correspond to special cases of HEX, all
presented results are interesting beyond the specific formalism.
Probabilistically Safe Robot Planning with Confidence-Based Human Predictions
Authors: Jaime F. Fisac, Andrea Bajcsy, Sylvia L. Herbert, David Fridovich-Keil, Steven Wang, Claire J. Tomlin, Anca D. Dragan
Date: 2018-05-31 00:00:00
Tasks: Trajectory Planning

In order to safely operate around humans, robots can employ predictive models
of human motion. Unfortunately, these models cannot capture the full complexity
of human behavior and necessarily introduce simplifying assumptions. As a
result, predictions may degrade whenever the observed human behavior departs
from the assumed structure, which can have negative implications for safety. In
this paper, we observe that how "rational" human actions appear under a
particular model can be viewed as an indicator of that model's ability to
describe the human's current motion. By reasoning about this model confidence
in a real-time Bayesian framework, we show that the robot can very quickly
modulate its predictions to become more uncertain when the model performs
poorly. Building on recent work in provably-safe trajectory planning, we
leverage these confidence-aware human motion predictions to generate assured
autonomous robot motion. Our new analysis combines worst-case tracking error
guarantees for the physical robot with probabilistic time-varying human
predictions, yielding a quantitative, probabilistic safety certificate. We
demonstrate our approach with a quadcopter navigating around a human.
Meta-Learning Probabilistic Inference For Prediction
Authors: Jonathan Gordon, John Bronskill, Matthias Bauer, Sebastian Nowozin, Richard E. Turner
Date: 2018-05-24 00:00:00
Proceeding: ICLR 2019 5
Tasks: Few-Shot Image Classification, Few-Shot Learning, Meta-Learning, Prediction

This paper introduces a new framework for data efficient and versatile learning. Specifically: 1) We develop ML-PIP, a general framework for Meta-Learning approximate Probabilistic Inference for Prediction. ML-PIP extends existing probabilistic interpretations of meta-learning to cover a broad class of methods. 2) We introduce VERSA, an instance of the framework employing a flexible and versatile amortization network that takes few-shot learning datasets as inputs, with arbitrary numbers of shots, and outputs a distribution over task-specific parameters in a single forward pass. VERSA substitutes optimization at test time with forward passes through inference networks, amortizing the cost of inference and relieving the need for second derivatives during training. 3) We evaluate VERSA on benchmark datasets where the method sets new state-of-the-art results, handles arbitrary numbers of shots, and for classification, arbitrary numbers of classes at train and test time. The power of the approach is then demonstrated through a challenging few-shot ShapeNet view reconstruction task.
MONET: Multiview Semi-supervised Keypoint Detection via Epipolar Divergence
Authors: Yuan Yao, Yasamin Jafarian, Hyun Soo Park
Date: 2018-05-31 00:00:00
Proceeding: ICCV 2019 10
Tasks: Data Augmentation, Keypoint Detection

This paper presents MONET -- an end-to-end semi-supervised learning framework for a keypoint detector using multiview image streams. In particular, we consider general subjects such as non-human species where attaining a large scale annotated dataset is challenging. While multiview geometry can be used to self-supervise the unlabeled data, integrating the geometry into learning a keypoint detector is challenging due to representation mismatch. We address this mismatch by formulating a new differentiable representation of the epipolar constraint called epipolar divergence---a generalized distance from the epipolar lines to the corresponding keypoint distribution. Epipolar divergence characterizes when two view keypoint distributions produce zero reprojection error. We design a twin network that minimizes the epipolar divergence through stereo rectification that can significantly alleviate computational complexity and sampling aliasing in training. We demonstrate that our framework can localize customized keypoints of diverse species, e.g., humans, dogs, and monkeys.
Variational Inverse Control with Events: A General Framework for Data-Driven Reward Definition
Authors: Justin Fu, Avi Singh, Dibya Ghosh, Larry Yang, Sergey Levine
Date: 2018-05-29 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: continuous-control, Continuous Control, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The design of a reward function often poses a major practical challenge to
real-world applications of reinforcement learning. Approaches such as inverse
reinforcement learning attempt to overcome this challenge, but require expert
demonstrations, which can be difficult or expensive to obtain in practice. We
propose variational inverse control with events (VICE), which generalizes
inverse reinforcement learning methods to cases where full demonstrations are
not needed, such as when only samples of desired goal states are available. Our
method is grounded in an alternative perspective on control and reinforcement
learning, where an agent's goal is to maximize the probability that one or more
events will happen at some point in the future, rather than maximizing
cumulative rewards. We demonstrate the effectiveness of our methods on
continuous control tasks, with a focus on high-dimensional observations like
images where rewards are hard or even impossible to specify.
Generative Ratio Matching Networks
Authors: Akash Srivastava, Kai Xu, Michael U. Gutmann, Charles Sutton
Date: 2018-05-31 00:00:00
Proceeding: ICLR 2020 1

Deep generative models can learn to generate realistic-looking images, but many of the most effective methods are adversarial and involve a saddlepoint optimization, which requires a careful balancing of training between a generator network and a critic network. Maximum mean discrepancy networks (MMD-nets) avoid this issue by using kernel as a fixed adversary, but unfortunately, they have not on their own been able to match the generative quality of adversarial training. In this work, we take their insight of using kernels as fixed adversaries further and present a novel method for training deep generative models that does not involve saddlepoint optimization. We call our method generative ratio matching or GRAM for short. In GRAM, the generator and the critic networks do not play a zero-sum game against each other, instead, they do so against a fixed kernel. Thus GRAM networks are not only stable to train like MMD-nets but they also match and beat the generative quality of adversarially trained generative networks.
Multi-View Silhouette and Depth Decomposition for High Resolution 3D Object Representation
Authors: Edward Smith, Scott Fujimoto, David Meger
Date: 2018-02-27 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: 3D Object Reconstruction, 3D Object Super-Resolution, Depth Estimation, Depth Prediction, Object Reconstruction, Super-Resolution

We consider the problem of scaling deep generative shape models to
high-resolution. Drawing motivation from the canonical view representation of
objects, we introduce a novel method for the fast up-sampling of 3D objects in
voxel space through networks that perform super-resolution on the six
orthographic depth projections. This allows us to generate high-resolution
objects with more efficient scaling than methods which work directly in 3D. We
decompose the problem of 2D depth super-resolution into silhouette and depth
prediction to capture both structure and fine detail. This allows our method to
generate sharp edges more easily than an individual network. We evaluate our
work on multiple experiments concerning high-resolution 3D objects, and show
our system is capable of accurately predicting novel objects at resolutions as
large as 512$\mathbf{\times}$512$\mathbf{\times}$512 -- the highest resolution
reported for this task. We achieve state-of-the-art performance on 3D object
reconstruction from RGB images on the ShapeNet dataset, and further demonstrate
the first effective 3D super-resolution method.
Imaging with SPADs and DMDs: Seeing through Diffraction-Photons
Authors: Ibrahim Alsolami, Wolfgang Heidrich
Date: 2018-05-31 00:00:00

This paper addresses the problem of imaging in the presence of diffraction-photons. Diffraction-photons arise from the low contrast ratio of DMDs ($\sim\,1000:1$), and very much degrade the quality of images captured by SPAD-based systems. Herein, a joint illumination-deconvolution scheme is designed to overcome diffraction-photons, enabling the acquisition of intensity and depth images. Additionally, a proof-of-concept experiment is conducted to demonstrate the viability of the designed scheme. It is shown that by co-designing the illumination and deconvolution phases of imaging, one can substantially overcome diffraction-photons.
PeerNets: Exploiting Peer Wisdom Against Adversarial Attacks
Authors: Jan Svoboda, Jonathan Masci, Federico Monti, Michael M. Bronstein, Leonidas Guibas
Date: 2018-05-31 00:00:00
Proceeding: ICLR 2019 5
Tasks: Autonomous Driving

Deep learning systems have become ubiquitous in many aspects of our lives.
Unfortunately, it has been shown that such systems are vulnerable to
adversarial attacks, making them prone to potential unlawful uses. Designing
deep neural networks that are robust to adversarial attacks is a fundamental
step in making such systems safer and deployable in a broader variety of
applications (e.g. autonomous driving), but more importantly is a necessary
step to design novel and more advanced architectures built on new computational
paradigms rather than marginally building on the existing ones. In this paper
we introduce PeerNets, a novel family of convolutional networks alternating
classical Euclidean convolutions with graph convolutions to harness information
from a graph of peer samples. This results in a form of non-local forward
propagation in the model, where latent features are conditioned on the global
structure induced by the graph, that is up to 3 times more robust to a variety
of white- and black-box adversarial attacks compared to conventional
architectures with almost no drop in accuracy.
Resisting Adversarial Attacks using Gaussian Mixture Variational Autoencoders
Authors: Partha Ghosh, Arpan Losalka, Michael J. Black
Date: 2018-05-31 00:00:00

Susceptibility of deep neural networks to adversarial attacks poses a major
theoretical and practical challenge. All efforts to harden classifiers against
such attacks have seen limited success. Two distinct categories of samples to
which deep networks are vulnerable, "adversarial samples" and "fooling
samples", have been tackled separately so far due to the difficulty posed when
considered together. In this work, we show how one can address them both under
one unified framework. We tie a discriminative model with a generative model,
rendering the adversarial objective to entail a conflict. Our model has the
form of a variational autoencoder, with a Gaussian mixture prior on the latent
vector. Each mixture component of the prior distribution corresponds to one of
the classes in the data. This enables us to perform selective classification,
leading to the rejection of adversarial samples instead of misclassification.
Our method inherently provides a way of learning a selective classifier in a
semi-supervised scenario as well, which can resist adversarial attacks. We also
show how one can reclassify the rejected adversarial samples.
Rethinking Knowledge Graph Propagation for Zero-Shot Learning
Authors: Michael Kampffmeyer, Yinbo Chen, Xiaodan Liang, Hao Wang, Yu-jia Zhang, Eric P. Xing
Date: 2018-05-29 00:00:00
Proceeding: CVPR 2019 6
Tasks: Zero-Shot Learning

Graph convolutional neural networks have recently shown great potential for
the task of zero-shot learning. These models are highly sample efficient as
related concepts in the graph structure share statistical strength allowing
generalization to new classes when faced with a lack of data. However,
multi-layer architectures, which are required to propagate knowledge to distant
nodes in the graph, dilute the knowledge by performing extensive Laplacian
smoothing at each layer and thereby consequently decrease performance. In order
to still enjoy the benefit brought by the graph structure while preventing
dilution of knowledge from distant nodes, we propose a Dense Graph Propagation
(DGP) module with carefully designed direct links among distant nodes. DGP
allows us to exploit the hierarchical graph structure of the knowledge graph
through additional connections. These connections are added based on a node's
relationship to its ancestors and descendants. A weighting scheme is further
used to weigh their contribution depending on the distance to the node to
improve information propagation in the graph. Combined with finetuning of the
representations in a two-stage training approach our method outperforms
state-of-the-art zero-shot learning approaches.
How convolutional neural network see the world - A survey of convolutional neural network visualization methods
Authors: Zhuwei Qin, Fuxun Yu, ChenChen Liu, Xiang Chen
Date: 2018-04-30 00:00:00
Tasks: Image Retrieval, object-detection, Object Detection, Retrieval

Nowadays, the Convolutional Neural Networks (CNNs) have achieved impressive
performance on many computer vision related tasks, such as object detection,
image recognition, image retrieval, etc. These achievements benefit from the
CNNs outstanding capability to learn the input features with deep layers of
neuron structures and iterative training process. However, these learned
features are hard to identify and interpret from a human vision perspective,
causing a lack of understanding of the CNNs internal working mechanism. To
improve the CNN interpretability, the CNN visualization is well utilized as a
qualitative analysis method, which translates the internal features into
visually perceptible patterns. And many CNN visualization works have been
proposed in the literature to interpret the CNN in perspectives of network
structure, operation, and semantic concept. In this paper, we expect to provide
a comprehensive survey of several representative CNN visualization methods,
including Activation Maximization, Network Inversion, Deconvolutional Neural
Networks (DeconvNet), and Network Dissection based visualization. These methods
are presented in terms of motivations, algorithms, and experiment results.
Based on these visualization methods, we also discuss their practical
applications to demonstrate the significance of the CNN interpretability in
areas of network design, optimization, security enhancement, etc.
DARTS: Deceiving Autonomous Cars with Toxic Signs
Authors: Chawin Sitawarin, Arjun Nitin Bhagoji, Arsalan Mosenia, Mung Chiang, Prateek Mittal
Date: 2018-02-18 00:00:00
Tasks: Traffic Sign Recognition

Sign recognition is an integral part of autonomous cars. Any
misclassification of traffic signs can potentially lead to a multitude of
disastrous consequences, ranging from a life-threatening accident to even a
large-scale interruption of transportation services relying on autonomous cars.
In this paper, we propose and examine security attacks against sign recognition
systems for Deceiving Autonomous caRs with Toxic Signs (we call the proposed
attacks DARTS). In particular, we introduce two novel methods to create these
toxic signs. First, we propose Out-of-Distribution attacks, which expand the
scope of adversarial examples by enabling the adversary to generate these
starting from an arbitrary point in the image space compared to prior attacks
which are restricted to existing training/test data (In-Distribution). Second,
we present the Lenticular Printing attack, which relies on an optical
phenomenon to deceive the traffic sign recognition system. We extensively
evaluate the effectiveness of the proposed attacks in both virtual and
real-world settings and consider both white-box and black-box threat models.
Our results demonstrate that the proposed attacks are successful under both
settings and threat models. We further show that Out-of-Distribution attacks
can outperform In-Distribution attacks on classifiers defended using the
adversarial training defense, exposing a new attack vector for these defenses.
Efficient Low-rank Multimodal Fusion with Modality-Specific Factors
Authors: Zhun Liu, Ying Shen, Varun Bharadhwaj Lakshminarasimhan, Paul Pu Liang, Amir Zadeh, Louis-Philippe Morency
Date: 2018-05-31 00:00:00
Proceeding: ACL 2018 7
Tasks: Emotion Recognition, Multimodal Sentiment Analysis, Sentiment Analysis

Multimodal research is an emerging field of artificial intelligence, and one
of the main research problems in this field is multimodal fusion. The fusion of
multimodal data is the process of integrating multiple unimodal representations
into one compact multimodal representation. Previous research in this field has
exploited the expressiveness of tensors for multimodal representation. However,
these methods often suffer from exponential increase in dimensions and in
computational complexity introduced by transformation of input into tensor. In
this paper, we propose the Low-rank Multimodal Fusion method, which performs
multimodal fusion using low-rank tensors to improve efficiency. We evaluate our
model on three different tasks: multimodal sentiment analysis, speaker trait
analysis, and emotion recognition. Our model achieves competitive results on
all these tasks while drastically reducing computational complexity. Additional
experiments also show that our model can perform robustly for a wide range of
low-rank settings, and is indeed much more efficient in both training and
inference compared to other methods that utilize tensor representations.
A Highly Parallel FPGA Implementation of Sparse Neural Network Training
Authors: Sourya Dey, Diandian Chen, Zongyang Li, Souvik Kundu, Kuan-Wen Huang, Keith M. Chugg, Peter A. Beerel
Date: 2018-05-31 00:00:00

We demonstrate an FPGA implementation of a parallel and reconfigurable
architecture for sparse neural networks, capable of on-chip training and
inference. The network connectivity uses pre-determined, structured sparsity to
significantly reduce complexity by lowering memory and computational
requirements. The architecture uses a notion of edge-processing, leading to
efficient pipelining and parallelization. Moreover, the device can be
reconfigured to trade off resource utilization with training time to fit
networks and datasets of varying sizes. The combined effects of complexity
reduction and easy reconfigurability enable significantly greater exploration
of network hyperparameters and structures on-chip. As proof of concept, we show
implementation results on an Artix-7 FPGA.
Defending Against Machine Learning Model Stealing Attacks Using Deceptive Perturbations
Authors: Taesung Lee, Benjamin Edwards, Ian Molloy, Dong Su
Date: 2018-05-31 00:00:00
Tasks: BIG-bench Machine Learning

Machine learning models are vulnerable to simple model stealing attacks if
the adversary can obtain output labels for chosen inputs. To protect against
these attacks, it has been proposed to limit the information provided to the
adversary by omitting probability scores, significantly impacting the utility
of the provided service. In this work, we illustrate how a service provider can
still provide useful, albeit misleading, class probability information, while
significantly limiting the success of the attack. Our defense forces the
adversary to discard the class probabilities, requiring significantly more
queries before they can train a model with comparable performance. We evaluate
several attack strategies, model architectures, and hyperparameters under
varying adversarial models, and evaluate the efficacy of our defense against
the strongest adversary. Finally, we quantify the amount of noise injected into
the class probabilities to mesure the loss in utility, e.g., adding 1.26 nats
per query on CIFAR-10 and 3.27 on MNIST. Our evaluation shows our defense can
degrade the accuracy of the stolen model at least 20%, or require up to 64
times more queries while keeping the accuracy of the protected model almost
intact.