pwc-archive/papers-with-abstracts huggingface dataset:
Adversarial Example Decomposition
Authors: Horace He, Aaron Lou, Qingxuan Jiang, Isay Katsman, Serge Belongie, Ser-Nam Lim
Date: 2018-12-04 00:00:00

Research has shown that widely used deep neural networks are vulnerable to carefully crafted adversarial perturbations. Moreover, these adversarial perturbations often transfer across models. We hypothesize that adversarial weakness is composed of three sources of bias: architecture, dataset, and random initialization. We show that one can decompose adversarial examples into an architecture-dependent component, data-dependent component, and noise-dependent component and that these components behave intuitively. For example, noise-dependent components transfer poorly to all other models, while architecture-dependent components transfer better to retrained models with the same architecture. In addition, we demonstrate that these components can be recombined to improve transferability without sacrificing efficacy on the original model.
A Retrieve-and-Edit Framework for Predicting Structured Outputs
Authors: Tatsunori B. Hashimoto, Kelvin Guu, Yonatan Oren, Percy Liang
Date: 2018-12-04 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Retrieval

For the task of generating complex outputs such as source code, editing
existing outputs can be easier than generating complex outputs from scratch.
With this motivation, we propose an approach that first retrieves a training
example based on the input (e.g., natural language description) and then edits
it to the desired output (e.g., code). Our contribution is a computationally
efficient method for learning a retrieval model that embeds the input in a
task-dependent way without relying on a hand-crafted metric or incurring the
expense of jointly training the retriever with the editor. Our
retrieve-and-edit framework can be applied on top of any base model. We show
that on a new autocomplete task for GitHub Python code and the Hearthstone
cards benchmark, retrieve-and-edit significantly boosts the performance of a
vanilla sequence-to-sequence model on both tasks.
e-SNLI: Natural Language Inference with Natural Language Explanations
Authors: Oana-Maria Camburu, Tim Rocktäschel, Thomas Lukasiewicz, Phil Blunsom
Date: 2018-12-04 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Natural Language Inference, Sentence

In order for machine learning to garner widespread public adoption, models
must be able to provide interpretable and robust explanations for their
decisions, as well as learn from human-provided explanations at train time. In
this work, we extend the Stanford Natural Language Inference dataset with an
additional layer of human-annotated natural language explanations of the
entailment relations. We further implement models that incorporate these
explanations into their training process and output them at test time. We show
how our corpus of explanations, which we call e-SNLI, can be used for various
goals, such as obtaining full sentence justifications of a model's decisions,
improving universal sentence representations and transferring to out-of-domain
NLI datasets. Our dataset thus opens up a range of research directions for
using natural language explanations, both for improving models and for
asserting their trust.
Learning to Fuse Things and Stuff
Authors: Jie Li, Allan Raventos, Arjun Bhargava, Takaaki Tagawa, Adrien Gaidon
Date: 2018-12-04 00:00:00
Tasks: Instance Segmentation, Panoptic Segmentation, Segmentation, Semantic Segmentation

We propose an end-to-end learning approach for panoptic segmentation, a novel task unifying instance (things) and semantic (stuff) segmentation. Our model, TASCNet, uses feature maps from a shared backbone network to predict in a single feed-forward pass both things and stuff segmentations. We explicitly constrain these two output distributions through a global things and stuff binary mask to enforce cross-task consistency. Our proposed unified network is competitive with the state of the art on several benchmarks for panoptic segmentation as well as on the individual semantic and instance segmentation tasks.
EENMF: An End-to-End Neural Matching Framework for E-Commerce Sponsored Search
Authors: Wenjin Wu, Guojun Liu, Hui Ye, Chenshuang Zhang, Tianshu Wu, Daorui Xiao, Wei. Lin, Xiaoyu Zhu
Date: 2018-12-04 00:00:00
Tasks: Retrieval

E-commerce sponsored search contributes an important part of revenue for the
e-commerce company. In consideration of effectiveness and efficiency, a
large-scale sponsored search system commonly adopts a multi-stage architecture.
We name these stages as ad retrieval, ad pre-ranking and ad ranking. Ad
retrieval and ad pre-ranking are collectively referred to as ad matching in
this paper. We propose an end-to-end neural matching framework (EENMF) to model
two tasks---vector-based ad retrieval and neural networks based ad pre-ranking.
Under the deep matching framework, vector-based ad retrieval harnesses user
recent behavior sequence to retrieve relevant ad candidates without the
constraint of keyword bidding. Simultaneously, the deep model is employed to
perform the global pre-ranking of ad candidates from multiple retrieval paths
effectively and efficiently. Besides, the proposed model tries to optimize the
pointwise cross-entropy loss which is consistent with the objective of predict
models in the ranking stage. We conduct extensive evaluation to validate the
performance of the proposed framework. In the real traffic of a large-scale
e-commerce sponsored search, the proposed approach significantly outperforms
the baseline.
Bag of Tricks for Image Classification with Convolutional Neural Networks
Authors: Tong He, Zhi Zhang, Hang Zhang, Zhongyue Zhang, Junyuan Xie, Mu Li
Date: 2018-12-04 00:00:00
Proceeding: CVPR 2019 6
Tasks: Domain Generalization, General Classification, image-classification, Image Classification, Object Detection, Semantic Segmentation, Transfer Learning

Much of the recent progress made in image classification research can be
credited to training procedure refinements, such as changes in data
augmentations and optimization methods. In the literature, however, most
refinements are either briefly mentioned as implementation details or only
visible in source code. In this paper, we will examine a collection of such
refinements and empirically evaluate their impact on the final model accuracy
through ablation study. We will show that, by combining these refinements
together, we are able to improve various CNN models significantly. For example,
we raise ResNet-50's top-1 validation accuracy from 75.3% to 79.29% on
ImageNet. We will also demonstrate that improvement on image classification
accuracy leads to better transfer learning performance in other application
domains such as object detection and semantic segmentation.
FRAME Revisited: An Interpretation View Based on Particle Evolution
Authors: Xu Cai, Yang Wu, Guanbin Li, Ziliang Chen, Liang Lin
Date: 2018-12-04 00:00:00
Tasks: Descriptive

FRAME (Filters, Random fields, And Maximum Entropy) is an energy-based
descriptive model that synthesizes visual realism by capturing mutual patterns
from structural input signals. The maximum likelihood estimation (MLE) is
applied by default, yet conventionally causes the unstable training energy that
wrecks the generated structures, which remains unexplained. In this paper, we
provide a new theoretical insight to analyze FRAME, from a perspective of
particle physics ascribing the weird phenomenon to KL-vanishing issue. In order
to stabilize the energy dissipation, we propose an alternative Wasserstein
distance in discrete time based on the conclusion that the
Jordan-Kinderlehrer-Otto (JKO) discrete flow approximates KL discrete flow when
the time step size tends to 0. Besides, this metric can still maintain the
model's statistical consistency. Quantitative and qualitative experiments have
been respectively conducted on several widely used datasets. The empirical
studies have evidenced the effectiveness and superiority of our method.
Making BREAD: Biomimetic strategies for Artificial Intelligence Now and in the Future
Authors: Jeffrey L. Krichmar, William Severa, Salar M. Khan, James L. Olds
Date: 2018-12-04 00:00:00

The Artificial Intelligence (AI) revolution foretold of during the 1960s is
well underway in the second decade of the 21st century. Its period of
phenomenal growth likely lies ahead. Still, we believe, there are crucial
lessons that biology can offer that will enable a prosperous future for AI. For
machines in general, and for AI's especially, operating over extended periods
or in extreme environments will require energy usage orders of magnitudes more
efficient than exists today. In many operational environments, energy sources
will be constrained. Any plans for AI devices operating in a challenging
environment must begin with the question of how they are powered, where fuel is
located, how energy is stored and made available to the machine, and how long
the machine can operate on specific energy units. Hence, the materials and
technologies that provide the needed energy represent a critical challenge
towards future use-scenarios of AI and should be integrated into their design.
Here we make four recommendations for stakeholders and especially decision
makers to facilitate a successful trajectory for this technology. First, that
scientific societies and governments coordinate Biomimetic Research for
Energy-efficient, AI Designs (BREAD); a multinational initiative and a funding
strategy for investments in the future integrated design of energetics into AI.
Second, that biomimetic energetic solutions be central to design consideration
for future AI. Third, that a pre-competitive space be organized between
stakeholder partners and fourth, that a trainee pipeline be established to
ensure the human capital required for success in this area.
Parallel-tempered Stochastic Gradient Hamiltonian Monte Carlo for Approximate Multimodal Posterior Sampling
Authors: Rui Luo, Qiang Zhang, Yuanyuan Liu
Date: 2018-12-04 00:00:00

We propose a new sampler that integrates the protocol of parallel tempering
with the Nos\'e-Hoover (NH) dynamics. The proposed method can efficiently draw
representative samples from complex posterior distributions with multiple
isolated modes in the presence of noise arising from stochastic gradient. It
potentially facilitates deep Bayesian learning on large datasets where complex
multimodal posteriors and mini-batch gradient are encountered.
Deep Generative Modeling of LiDAR Data
Authors: Lucas Caccia, Herke van Hoof, Aaron Courville, Joelle Pineau
Date: 2018-12-04 00:00:00
Tasks: Point Cloud Generation

Building models capable of generating structured output is a key challenge for AI and robotics. While generative models have been explored on many types of data, little work has been done on synthesizing lidar scans, which play a key role in robot mapping and localization. In this work, we show that one can adapt deep generative models for this task by unravelling lidar scans into a 2D point map. Our approach can generate high quality samples, while simultaneously learning a meaningful latent representation of the data. We demonstrate significant improvements against state-of-the-art point cloud generation methods. Furthermore, we propose a novel data representation that augments the 2D signal with absolute positional information. We show that this helps robustness to noisy and imputed input; the learned model can recover the underlying lidar scan from seemingly uninformative data
MDU-Net: Multi-scale Densely Connected U-Net for biomedical image segmentation
Authors: Jiawei Zhang, Yuzhen Jin, Jilan Xu, Xiaowei Xu, Yanchun Zhang
Date: 2018-12-02 00:00:00
Tasks: Decoder, Image Segmentation, Quantization, Segmentation, Semantic Segmentation

Radiologist is "doctor's doctor", biomedical image segmentation plays a
central role in quantitative analysis, clinical diagnosis, and medical
intervention. In the light of the fully convolutional networks (FCN) and U-Net,
deep convolutional networks (DNNs) have made significant contributions in
biomedical image segmentation applications. In this paper, based on U-Net, we
propose MDUnet, a multi-scale densely connected U-net for biomedical image
segmentation. we propose three different multi-scale dense connections for U
shaped architectures encoder, decoder and across them. The highlights of our
architecture is directly fuses the neighboring different scale feature maps
from both higher layers and lower layers to strengthen feature propagation in
current layer. Which can largely improves the information flow encoder, decoder
and across them. Multi-scale dense connections, which means containing shorter
connections between layers close to the input and output, also makes much
deeper U-net possible. We adopt the optimal model based on the experiment and
propose a novel Multi-scale Dense U-Net (MDU-Net) architecture with
quantization. Which reduce overfitting in MDU-Net for better accuracy. We
evaluate our purpose model on the MICCAI 2015 Gland Segmentation dataset
(GlaS). The three multi-scale dense connections improve U-net performance by up
to 1.8% on test A and 3.5% on test B in the MICCAI Gland dataset. Meanwhile the
MDU-net with quantization achieves the superiority over U-Net performance by up
to 3% on test A and 4.1% on test B.
Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning
Authors: Zhibo Wang, Mengkai Song, Zhifei Zhang, Yang song, Qian Wang, Hairong Qi
Date: 2018-12-03 00:00:00
Tasks: Edge-computing, Federated Learning, Privacy Preserving

Federated learning, i.e., a mobile edge computing framework for deep
learning, is a recent advance in privacy-preserving machine learning, where the
model is trained in a decentralized manner by the clients, i.e., data curators,
preventing the server from directly accessing those private data from the
clients. This learning mechanism significantly challenges the attack from the
server side. Although the state-of-the-art attacking techniques that
incorporated the advance of Generative adversarial networks (GANs) could
construct class representatives of the global data distribution among all
clients, it is still challenging to distinguishably attack a specific client
(i.e., user-level privacy leakage), which is a stronger privacy threat to
precisely recover the private data from a specific client. This paper gives the
first attempt to explore user-level privacy leakage against the federated
learning by the attack from a malicious server. We propose a framework
incorporating GAN with a multi-task discriminator, which simultaneously
discriminates category, reality, and client identity of input samples. The
novel discrimination on client identity enables the generator to recover user
specified private data. Unlike existing works that tend to interfere the
training process of the federated learning, the proposed method works
"invisibly" on the server side. The experimental results demonstrate the
effectiveness of the proposed attacking approach and the superior to the
state-of-the-art.
Pre-Defined Sparse Neural Networks with Hardware Acceleration
Authors: Sourya Dey, Kuan-Wen Huang, Peter A. Beerel, Keith M. Chugg
Date: 2018-12-04 00:00:00

Neural networks have proven to be extremely powerful tools for modern
artificial intelligence applications, but computational and storage complexity
remain limiting factors. This paper presents two compatible contributions
towards reducing the time, energy, computational, and storage complexities
associated with multilayer perceptrons. Pre-defined sparsity is proposed to
reduce the complexity during both training and inference, regardless of the
implementation platform. Our results show that storage and computational
complexity can be reduced by factors greater than 5X without significant
performance loss. The second contribution is an architecture for hardware
acceleration that is compatible with pre-defined sparsity. This architecture
supports both training and inference modes and is flexible in the sense that it
is not tied to a specific number of neurons. For example, this flexibility
implies that various sized neural networks can be supported on various sized
Field Programmable Gate Array (FPGA)s.
A Spectral Regularizer for Unsupervised Disentanglement
Authors: Aditya Ramesh, Youngduck Choi, Yann Lecun
Date: 2018-12-04 00:00:00
Tasks: Disentanglement

A generative model with a disentangled representation allows for independent
control over different aspects of the output. Learning disentangled
representations has been a recent topic of great interest, but it remains
poorly understood. We show that even for GANs that do not possess disentangled
representations, one can find curved trajectories in latent space over which
local disentanglement occurs. These trajectories are found by iteratively
following the leading right-singular vectors of the Jacobian of the generator
with respect to its input. Based on this insight, we describe an efficient
regularizer that aligns these vectors with the coordinate axes, and show that
it can be used to induce disentangled representations in GANs, in a completely
unsupervised manner.
Cross-Classification Clustering: An Efficient Multi-Object Tracking Technique for 3-D Instance Segmentation in Connectomics
Authors: Yaron Meirovitch, Lu Mi, Hayk Saribekyan, Alexander Matveev, David Rolnick, Nir Shavit
Date: 2018-12-04 00:00:00
Proceeding: CVPR 2019 6
Tasks: Clustering, General Classification, Instance Segmentation, Multi-Object Tracking, Object, Object Tracking, Semantic Segmentation

Pixel-accurate tracking of objects is a key element in many computer vision applications, often solved by iterated individual object tracking or instance segmentation followed by object matching. Here we introduce cross-classification clustering (3C), a technique that simultaneously tracks complex, interrelated objects in an image stack. The key idea in cross-classification is to efficiently turn a clustering problem into a classification problem by running a logarithmic number of independent classifications per image, letting the cross-labeling of these classifications uniquely classify each pixel to the object labels. We apply the 3C mechanism to achieve state-of-the-art accuracy in connectomics -- the nanoscale mapping of neural tissue from electron microscopy volumes. Our reconstruction system increases scalability by an order of magnitude over existing single-object tracking methods (such as flood-filling networks). This scalability is important for the deployment of connectomics pipelines, since currently the best performing techniques require computing infrastructures that are beyond the reach of most laboratories. Our algorithm may offer benefits in other domains that require pixel-accurate tracking of multiple objects, such as segmentation of videos and medical imagery.
Back to the Future for Dialogue Research: A Position Paper
Authors: Philip R Cohen
Date: 2018-12-04 00:00:00
Tasks: Position, slot-filling, Slot Filling

This short position paper is intended to provide a critique of current
approaches to dialogue, as well as a roadmap for collaborative dialogue
research. It is unapologetically opinionated, but informed by 40 years of
dialogue re-search. No attempt is made to be comprehensive. The paper will
discuss current research into building so-called "chatbots", slot-filling
dialogue systems, and plan-based dialogue systems. For further discussion of
some of these issues, please see (Allen et al., in press).
Sequential Experiment Design for Hypothesis Verification
Authors: Dhruva Kartik, Ashutosh Nayyar, Urbashi Mitra
Date: 2018-12-04 00:00:00
Tasks: Two-sample testing

Hypothesis testing is an important problem with applications in target
localization, clinical trials etc. Many active hypothesis testing strategies
operate in two phases: an exploration phase and a verification phase. In the
exploration phase, selection of experiments is such that a moderate level of
confidence on the true hypothesis is achieved. Subsequent experiment design
aims at improving the confidence level on this hypothesis to the desired level.
In this paper, the focus is on the verification phase. A confidence measure is
defined and active hypothesis testing is formulated as a confidence
maximization problem in an infinite-horizon average-reward Partially Observable
Markov Decision Process (POMDP) setting. The problem of maximizing confidence
conditioned on a particular hypothesis is referred to as the hypothesis
verification problem. The relationship between hypothesis testing and
verification problems is established. The verification problem can be
formulated as a Markov Decision Process (MDP). Optimal solutions for the
verification MDP are characterized and a simple heuristic adaptive strategy for
verification is proposed based on a zero-sum game interpretation of
Kullback-Leibler divergences. It is demonstrated through numerical experiments
that the heuristic performs better in some scenarios compared to existing
methods in literature.
Mitigating Planner Overfitting in Model-Based Reinforcement Learning
Authors: Dilip Arumugam, David Abel, Kavosh Asadi, Nakul Gopalan, Christopher Grimm, Jun Ki Lee, Lucas Lehnert, Michael L. Littman
Date: 2018-12-03 00:00:00
Tasks: model, Model-based Reinforcement Learning, Position, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

An agent with an inaccurate model of its environment faces a difficult choice: it can ignore the errors in its model and act in the real world in whatever way it determines is optimal with respect to its model. Alternatively, it can take a more conservative stance and eschew its model in favor of optimizing its behavior solely via real-world interaction. This latter approach can be exceedingly slow to learn from experience, while the former can lead to "planner overfitting" - aspects of the agent's behavior are optimized to exploit errors in its model. This paper explores an intermediate position in which the planner seeks to avoid overfitting through a kind of regularization of the plans it considers. We present three different approaches that demonstrably mitigate planner overfitting in reinforcement-learning environments.
Learning to Predict Ego-Vehicle Poses for Sampling-Based Nonholonomic Motion Planning
Authors: Holger Banzhaf, Paul Sanzenbacher, Ulrich Baumann, J. Marius Zöllner
Date: 2018-12-03 00:00:00
Tasks: Motion Planning

Sampling-based motion planning is an effective tool to compute safe
trajectories for automated vehicles in complex environments. However, a fast
convergence to the optimal solution can only be ensured with the use of
problem-specific sampling distributions. Due to the large variety of driving
situations within the context of automated driving, it is very challenging to
manually design such distributions. This paper introduces therefore a
data-driven approach utilizing a deep convolutional neural network (CNN): Given
the current driving situation, future ego-vehicle poses can be directly
generated from the output of the CNN allowing to guide the motion planner
efficiently towards the optimal solution. A benchmark highlights that the CNN
predicts future vehicle poses with a higher accuracy compared to uniform
sampling and a state-of-the-art A*-based approach. Combining this CNN-guided
sampling with the motion planner Bidirectional RRT* reduces the computation
time by up to an order of magnitude and yields a faster convergence to a lower
cost as well as a success rate of 100 % in the tested scenarios.