pwc-archive/papers-with-abstracts huggingface dataset:
Cortical Circuits from Scratch: A Metaplastic Architecture for the Emergence of Lognormal Firing Rates and Realistic Topology
Authors: Zoe Tosi, John Beggs
Date: 2017-06-01 00:00:00

Our current understanding of neuroplasticity paints a picture of a complex
interconnected system of dependent processes which shape cortical structure so
as to produce an efficient information processing system. Indeed, the
cooperation of these processes is associated with robust, stable, adaptable
networks with characteristic features of activity and synaptic topology.
However, combining the actions of these mechanisms in models has proven
exceptionally difficult and to date no model has been able to do so without
significant hand-tuning. Until such a model exists that can successfully
combine these mechanisms to form a stable circuit with realistic features, our
ability to study neuroplasticity in the context of (more realistic) dynamic
networks and potentially reap whatever rewards these features and mechanisms
imbue biological networks with is hindered. We introduce a model which combines
five known plasticity mechanisms that act on the network as well as a unique
metaplastic mechanism which acts on other plasticity mechanisms, to produce a
neural circuit model which is both stable and capable of broadly reproducing
many characteristic features of cortical networks. The MANA (metaplastic
artificial neural architecture) represents the first model of its kind in that
it is able to self-organize realistic, nonrandom features of cortical networks,
from a null initial state (no synaptic connectivity or neuronal
differentiation). In the same vein as models like the SORN (self-organizing
recurrent network) MANA represents further progress toward the reverse
engineering of the brain at the network level.
Decomposition of Higher-Order Spectra for Blind Multiple-Input Deconvolution, Pattern Identification and Separation
Authors: Christopher K. Kovach, Matthew A. Howard III
Date: 2019-01-28 00:00:00
Tasks: Denoising

Like the ordinary power spectrum, higher-order spectra (HOS) describe signal
properties that are invariant under translations in time. Unlike the power
spectrum, HOS retain phase information from which details of the signal
waveform can be recovered. Here we consider the problem of identifying multiple
unknown transient waveforms which recur within an ensemble of records at
mutually random delays. We develop a new technique for recovering filters from
HOS whose performance in waveform detection approaches that of an optimal
matched filter, requiring no prior information about the waveforms. Unlike
previous techniques of signal identification through HOS, the method applies
equally well to signals with deterministic and non-deterministic HOS. In the
non-deterministic case, it yields an additive decomposition, introducing a new
approach to the separation of component processes within non-Gaussian signals
having non-deterministic higher moments. We show a close relationship to
minimum-entropy blind deconvolution (MED), which the present technique improves
upon by avoiding the need for numerical optimization, while requiring only
numerically stable operations of time shift, element-wise multiplication and
averaging, making it particularly suited for real-time applications. The
application of HOS decomposition to real-world signals is demonstrated with
blind denoising, detection and classification of normal and abnormal heartbeats
in electrocardiograms.
DeeperLab: Single-Shot Image Parser
Authors: Tien-Ju Yang, Maxwell D. Collins, Yukun Zhu, Jyh-Jing Hwang, Ting Liu, Xiao Zhang, Vivienne Sze, George Papandreou, Liang-Chieh Chen
Date: 2019-02-13 00:00:00
Tasks: GPU, Instance Segmentation, Panoptic Segmentation, Segmentation, Semantic Segmentation

We present a single-shot, bottom-up approach for whole image parsing. Whole
image parsing, also known as Panoptic Segmentation, generalizes the tasks of
semantic segmentation for 'stuff' classes and instance segmentation for 'thing'
classes, assigning both semantic and instance labels to every pixel in an
image. Recent approaches to whole image parsing typically employ separate
standalone modules for the constituent semantic and instance segmentation tasks
and require multiple passes of inference. Instead, the proposed DeeperLab image
parser performs whole image parsing with a significantly simpler, fully
convolutional approach that jointly addresses the semantic and instance
segmentation tasks in a single-shot manner, resulting in a streamlined system
that better lends itself to fast processing. For quantitative evaluation, we
use both the instance-based Panoptic Quality (PQ) metric and the proposed
region-based Parsing Covering (PC) metric, which better captures the image
parsing quality on 'stuff' classes and larger object instances. We report
experimental results on the challenging Mapillary Vistas dataset, in which our
single model achieves 31.95% (val) / 31.6% PQ (test) and 55.26% PC (val) with 3
frames per second (fps) on GPU or near real-time speed (22.6 fps on GPU) with
reduced accuracy.
Learning to Control Self-Assembling Morphologies: A Study of Generalization via Modularity
Authors: Deepak Pathak, Chris Lu, Trevor Darrell, Phillip Isola, Alexei A. Efros
Date: 2019-02-14 00:00:00
Proceeding: NeurIPS 2019 12

Contemporary sensorimotor learning approaches typically start with an existing complex agent (e.g., a robotic arm), which they learn to control. In contrast, this paper investigates a modular co-evolution strategy: a collection of primitive agents learns to dynamically self-assemble into composite bodies while also learning to coordinate their behavior to control these bodies. Each primitive agent consists of a limb with a motor attached at one end. Limbs may choose to link up to form collectives. When a limb initiates a link-up action, and there is another limb nearby, the latter is magnetically connected to the 'parent' limb's motor. This forms a new single agent, which may further link with other agents. In this way, complex morphologies can emerge, controlled by a policy whose architecture is in explicit correspondence with the morphology. We evaluate the performance of these dynamic and modular agents in simulated environments. We demonstrate better generalization to test-time changes both in the environment, as well as in the structure of the agent, compared to static and monolithic baselines. Project video and code are available at https://pathak22.github.io/modular-assemblies/
Unsupervised Visuomotor Control through Distributional Planning Networks
Authors: Tianhe Yu, Gleb Shevchuk, Dorsa Sadigh, Chelsea Finn
Date: 2019-02-14 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

While reinforcement learning (RL) has the potential to enable robots to
autonomously acquire a wide range of skills, in practice, RL usually requires
manual, per-task engineering of reward functions, especially in real world
settings where aspects of the environment needed to compute progress are not
directly accessible. To enable robots to autonomously learn skills, we instead
consider the problem of reinforcement learning without access to rewards. We
aim to learn an unsupervised embedding space under which the robot can measure
progress towards a goal for itself. Our approach explicitly optimizes for a
metric space under which action sequences that reach a particular state are
optimal when the goal is the final state reached. This enables learning
effective and control-centric representations that lead to more autonomous
reinforcement learning algorithms. Our experiments on three simulated
environments and two real-world manipulation problems show that our method can
learn effective goal metrics from unlabeled interaction, and use the learned
goal metrics for autonomous reinforcement learning.
Deep Generative Endmember Modeling: An Application to Unsupervised Spectral Unmixing
Authors: Ricardo Augusto Borsoi, Tales Imbiriba, José Carlos Moreira Bermudez
Date: 2019-02-14 00:00:00
Tasks: Decoder, Hyperspectral image analysis

Endmember (EM) spectral variability can greatly impact the performance of standard hyperspectral image analysis algorithms. Extended parametric models have been successfully applied to account for the EM spectral variability. However, these models still lack the compromise between flexibility and low-dimensional representation that is necessary to properly explore the fact that spectral variability is often confined to a low-dimensional manifold in real scenes. In this paper we propose to learn a spectral variability model directly form the observed data, instead of imposing it \emph{a priori}. This is achieved through a deep generative EM model, which is estimated using a variational autoencoder (VAE). The encoder and decoder that compose the generative model are trained using pure pixel information extracted directly from the observed image, what allows for an unsupervised formulation. The proposed EM model is applied to the solution of a spectral unmixing problem, which we cast as an alternating nonlinear least-squares problem that is solved iteratively with respect to the abundances and to the low-dimensional representations of the EMs in the latent space of the deep generative model. Simulations using both synthetic and real data indicate that the proposed strategy can outperform the competing state-of-the-art algorithms.
Superposition of many models into one
Authors: Brian Cheung, Alex Terekhov, Yubei Chen, Pulkit Agrawal, Bruno Olshausen
Date: 2019-02-14 00:00:00
Proceeding: NeurIPS 2019 12

We present a method for storing multiple models within a single set of parameters. Models can coexist in superposition and still be retrieved individually. In experiments with neural networks, we show that a surprisingly large number of models can be effectively stored within a single parameter instance. Furthermore, each of these models can undergo thousands of training steps without significantly interfering with other models within the superposition. This approach may be viewed as the online complement of compression: rather than reducing the size of a network after training, we make use of the unrealized capacity of a network during training.
MultiGrain: a unified image embedding for classes and instances
Authors: Maxim Berman, Hervé Jégou, Andrea Vedaldi, Iasonas Kokkinos, Matthijs Douze
Date: 2019-02-14 00:00:00
Tasks: Classification, Data Augmentation, General Classification, image-classification, Image Classification, Image Retrieval, Object, Retrieval

MultiGrain is a network architecture producing compact vector representations
that are suited both for image classification and particular object retrieval.
It builds on a standard classification trunk. The top of the network produces
an embedding containing coarse and fine-grained information, so that images can
be recognized based on the object class, particular object, or if they are
distorted copies. Our joint training is simple: we minimize a cross-entropy
loss for classification and a ranking loss that determines if two images are
identical up to data augmentation, with no need for additional labels. A key
component of MultiGrain is a pooling layer that takes advantage of
high-resolution images with a network trained at a lower resolution.
  When fed to a linear classifier, the learned embeddings provide
state-of-the-art classification accuracy. For instance, we obtain 79.4% top-1
accuracy with a ResNet-50 learned on Imagenet, which is a +1.8% absolute
improvement over the AutoAugment method. When compared with the cosine
similarity, the same embeddings perform on par with the state-of-the-art for
image retrieval at moderate resolutions.
Integrating Propositional and Relational Label Side Information for Hierarchical Zero-Shot Image Classification
Authors: Colin Samplawski, Heesung Kwon, Erik Learned-Miller, Benjamin M. Marlin
Date: 2019-02-14 00:00:00
Tasks: Attribute, General Classification, image-classification, Image Classification, Zero-Shot Image Classification, Zero-Shot Learning

Zero-shot learning (ZSL) is one of the most extreme forms of learning from
scarce labeled data. It enables predicting that images belong to classes for
which no labeled training instances are available. In this paper, we present a
new ZSL framework that leverages both label attribute side information and a
semantic label hierarchy. We present two methods, lifted zero-shot prediction
and a custom conditional random field (CRF) model, that integrate both forms of
side information. We propose benchmark tasks for this framework that focus on
making predictions across a range of semantic levels. We show that lifted
zero-shot prediction can dramatically outperform baseline methods when making
predictions within specified semantic levels, and that the probability
distribution provided by the CRF model can be leveraged to yield further
performance improvements when making unconstrained predictions over the
hierarchy.
Exploring Frame Segmentation Networks for Temporal Action Localization
Authors: Ke Yang, Xiaolong Shen, Peng Qiao, Shijie Li, Dongsheng Li, Yong Dou
Date: 2019-02-14 00:00:00
Tasks: Action Localization, Open-Ended Question Answering, Temporal Action Localization

Temporal action localization is an important task of computer vision. Though
many methods have been proposed, it still remains an open question how to
predict the temporal location of action segments precisely. Most
state-of-the-art works train action classifiers on video segments
pre-determined by action proposal. However, recent work found that a desirable
model should move beyond segment-level and make dense predictions at a fine
granularity in time to determine precise temporal boundaries. In this paper, we
propose a Frame Segmentation Network (FSN) that places a temporal CNN on top of
the 2D spatial CNNs. Spatial CNNs are responsible for abstracting semantics in
spatial dimension while temporal CNN is responsible for introducing temporal
context information and performing dense predictions. The proposed FSN can make
dense predictions at frame-level for a video clip using both spatial and
temporal context information. FSN is trained in an end-to-end manner, so the
model can be optimized in spatial and temporal domain jointly. We also adapt
FSN to use it in weakly supervised scenario (WFSN), where only video level
labels are provided when training. Experiment results on public dataset show
that FSN achieves superior performance in both frame-level action localization
and temporal action localization.
Engineered Self-Organization for Resilient Robot Self-Assembly with Minimal Surprise
Authors: Tanja Katharina Kaiser, Heiko Hamann
Date: 2019-02-14 00:00:00

In collective robotic systems, the automatic generation of controllers for complex tasks is still a challenging problem. Open-ended evolution of complex robot behaviors can be a possible solution whereby an intrinsic driver for pattern formation and self-organization may prove to be important. We implement such a driver in collective robot systems by evolving prediction networks as world models in pair with action-selection networks. Fitness is given for good predictions which causes a bias towards easily predictable environments and behaviors in the form of emergent patterns, that is, environments of minimal surprise. There is no task-dependent bias or any other explicit predetermination for the different qualities of the emerging patterns. A careful configuration of actions, sensor models, and the environment is required to stimulate the emergence of complex behaviors. We study self-assembly to increase the scenario's complexity for our minimal surprise approach and, at the same time, limit the complexity of our simulations to a grid world to manage the feasibility of this approach. We investigate the impact of different swarm densities and the shape of the environment on the emergent patterns. Furthermore, we study how evolution can be biased towards the emergence of desired patterns. We analyze the resilience of the resulting self-assembly behaviors by causing damages to the assembled pattern and observe the self-organized reassembly of the structure. In summary, we evolved swarm behaviors for resilient self-assembly and successfully engineered self-organization in simulation. In future work, we plan to transfer our approach to a swarm of real robots.
Classifying Treatment Responders Under Causal Effect Monotonicity
Authors: Nathan Kallus
Date: 2019-02-14 00:00:00
Tasks: Causal Inference, Classification, General Classification

In the context of individual-level causal inference, we study the problem of predicting whether someone will respond or not to a treatment based on their features and past examples of features, treatment indicator (e.g., drug/no drug), and a binary outcome (e.g., recovery from disease). As a classification task, the problem is made difficult by not knowing the example outcomes under the opposite treatment indicators. We assume the effect is monotonic, as in advertising's effect on a purchase or bail-setting's effect on reappearance in court: either it would have happened regardless of treatment, not happened regardless, or happened only depending on exposure to treatment. Predicting whether the latter is latently the case is our focus. While previous work focuses on conditional average treatment effect estimation, formulating the problem as a classification task rather than an estimation task allows us to develop new tools more suited to this problem. By leveraging monotonicity, we develop new discriminative and generative algorithms for the responder-classification problem. We explore and discuss connections to corrupted data and policy learning. We provide an empirical study with both synthetic and real datasets to compare these specialized algorithms to standard benchmarks.
A Broad Class of Discrete-Time Hypercomplex-Valued Hopfield Neural Networks
Authors: Fidelis Zanetti de Castro, Marcos Eduardo Valle
Date: 2019-02-14 00:00:00

In this paper, we address the stability of a broad class of discrete-time hypercomplex-valued Hopfield-type neural networks. To ensure the neural networks belonging to this class always settle down at a stationary state, we introduce novel hypercomplex number systems referred to as real-part associative hypercomplex number systems. Real-part associative hypercomplex number systems generalize the well-known Cayley-Dickson algebras and real Clifford algebras and include the systems of real numbers, complex numbers, dual numbers, hyperbolic numbers, quaternions, tessarines, and octonions as particular instances. Apart from the novel hypercomplex number systems, we introduce a family of hypercomplex-valued activation functions called $\mathcal{B}$-projection functions. Broadly speaking, a $\mathcal{B}$-projection function projects the activation potential onto the set of all possible states of a hypercomplex-valued neuron. Using the theory presented in this paper, we confirm the stability analysis of several discrete-time hypercomplex-valued Hopfield-type neural networks from the literature. Moreover, we introduce and provide the stability analysis of a general class of Hopfield-type neural networks on Cayley-Dickson algebras.
Procrastinating with Confidence: Near-Optimal, Anytime, Adaptive Algorithm Configuration
Authors: Robert Kleinberg, Kevin Leyton-Brown, Brendan Lucier, Devon Graham
Date: 2019-02-14 00:00:00
Proceeding: NeurIPS 2019 12

Algorithm configuration methods optimize the performance of a parameterized heuristic algorithm on a given distribution of problem instances. Recent work introduced an algorithm configuration procedure ("Structured Procrastination") that provably achieves near optimal performance with high probability and with nearly minimal runtime in the worst case. It also offers an $\textit{anytime}$ property: it keeps tightening its optimality guarantees the longer it is run. Unfortunately, Structured Procrastination is not $\textit{adaptive}$ to characteristics of the parameterized algorithm: it treats every input like the worst case. Follow-up work ("LeapsAndBounds") achieves adaptivity but trades away the anytime property. This paper introduces a new algorithm, "Structured Procrastination with Confidence", that preserves the near-optimality and anytime properties of Structured Procrastination while adding adaptivity. In particular, the new algorithm will perform dramatically faster in settings where many algorithm configurations perform poorly. We show empirically both that such settings arise frequently in practice and that the anytime property is useful for finding good configurations quickly.
Convergence analysis of Tikhonov regularization for non-linear statistical inverse learning problems
Authors: Abhishake Rastogi, Gilles Blanchard, Peter Mathé
Date: 2019-02-14 00:00:00

We study a non-linear statistical inverse learning problem, where we observe
the noisy image of a quantity through a non-linear operator at some random
design points. We consider the widely used Tikhonov regularization (or method
of regularization, MOR) approach to reconstruct the estimator of the quantity
for the non-linear ill-posed inverse problem. The estimator is defined as the
minimizer of a Tikhonov functional, which is the sum of a data misfit term and
a quadratic penalty term. We develop a theoretical analysis for the minimizer
of the Tikhonov regularization scheme using the ansatz of reproducing kernel
Hilbert spaces. We discuss optimal rates of convergence for the proposed
scheme, uniformly over classes of admissible solutions, defined through
appropriate source conditions.
Estimating the Circuit Deobfuscating Runtime based on Graph Deep Learning
Authors: Zhiqian Chen, Gaurav Kolhe, Setareh Rafatirad, Sai Manoj P. D., Houman Homayoun, Liang Zhao, Chang-Tien Lu
Date: 2019-02-14 00:00:00
Tasks: Deep Learning

Circuit obfuscation is a recently proposed defense mechanism to protect digital integrated circuits (ICs) from reverse engineering by using camouflaged gates i.e., logic gates whose functionality cannot be precisely determined by the attacker. There have been effective schemes such as satisfiability-checking (SAT)-based attacks that can potentially decrypt obfuscated circuits, called deobfuscation. Deobfuscation runtime could have a large span ranging from few milliseconds to thousands of years or more, depending on the number and layouts of the ICs and camouflaged gates. And hence accurately pre-estimating the deobfuscation runtime is highly crucial for the defenders to maximize it and optimize their defense. However, estimating the deobfuscation runtime is a challenging task due to 1) the complexity and heterogeneity of graph-structured circuit, 2) the unknown and sophisticated mechanisms of the attackers for deobfuscation. To address the above mentioned challenges, this work proposes the first machine-learning framework that predicts the deobfuscation runtime based on graph deep learning techniques. Specifically, we design a new model, ICNet with new input and convolution layers to characterize and extract graph frequencies from ICs, which are then integrated by heterogeneous deep fully-connected layers to obtain final output. ICNet is an end-to-end framework which can automatically extract the determinant features for deobfuscation runtime. Extensive experiments demonstrate its effectiveness and efficiency.