pwc-archive/papers-with-abstracts huggingface dataset:
Modeling somatic computation with non-neural bioelectric networks
Authors: Santosh Manicka, Michael Levin
Date: 2019-12-09 00:00:00
Tasks: Decision Making

The field of basal cognition seeks to understand how adaptive, context-specific behavior occurs in non-neural biological systems. Embryogenesis and regeneration require plasticity in many tissue types to achieve structural and functional goals in diverse circumstances. Thus, advances in both evolutionary cell biology and regenerative medicine require an understanding of how non-neural tissues could process information. Neurons evolved from ancient cell types that used bioelectric signaling to perform computation. However, it has not been shown whether or how non-neural bioelectric cell networks can support computation. We generalize connectionist methods to non-neural tissue architectures, showing that a minimal non-neural Bio-Electric Network (BEN) model that utilizes the general principles of bioelectricity (electrodiffusion and gating) can compute. We characterize BEN behaviors ranging from elementary logic gates to pattern detectors, using both fixed and transient inputs to recapitulate various biological scenarios. We characterize the mechanisms of such networks using dynamical-systems and information-theory tools, demonstrating that logic can manifest in bidirectional, continuous, and relatively slow bioelectrical systems, complementing conventional neural-centric architectures. Our results reveal a variety of non-neural decision-making processes as manifestations of general cellular biophysical mechanisms and suggest novel bioengineering approaches to construct functional tissues for regenerative medicine and synthetic biology as well as new machine learning architectures.
Adversarial recovery of agent rewards from latent spaces of the limit order book
Authors: Jacobo Roa-Vicens, Yuanbo Wang, Virgile Mison, Yarin Gal, Ricardo Silva
Date: 2019-12-09 00:00:00
Tasks: Reinforcement Learning

Inverse reinforcement learning has proved its ability to explain state-action trajectories of expert agents by recovering their underlying reward functions in increasingly challenging environments. Recent advances in adversarial learning have allowed extending inverse RL to applications with non-stationary environment dynamics unknown to the agents, arbitrary structures of reward functions and improved handling of the ambiguities inherent to the ill-posed nature of inverse RL. This is particularly relevant in real time applications on stochastic environments involving risk, like volatile financial markets. Moreover, recent work on simulation of complex environments enable learning algorithms to engage with real market data through simulations of its latent space representations, avoiding a costly exploration of the original environment. In this paper, we explore whether adversarial inverse RL algorithms can be adapted and trained within such latent space simulations from real market data, while maintaining their ability to recover agent rewards robust to variations in the underlying dynamics, and transfer them to new regimes of the original environment.
JAX, M.D.: A Framework for Differentiable Physics
Authors: Samuel S. Schoenholz, Ekin D. Cubuk
Date: 2019-12-09 00:00:00
Tasks: Drug Discovery, GPU

We introduce JAX MD, a software package for performing differentiable physics simulations with a focus on molecular dynamics. JAX MD includes a number of physics simulation environments, as well as interaction potentials and neural networks that can be integrated into these environments without writing any additional code. Since the simulations themselves are differentiable functions, entire trajectories can be differentiated to perform meta-optimization. These features are built on primitive operations, such as spatial partitioning, that allow simulations to scale to hundreds-of-thousands of particles on a single GPU. These primitives are flexible enough that they can be used to scale up workloads outside of molecular dynamics. We present several examples that highlight the features of JAX MD including: integration of graph neural networks into traditional simulations, meta-optimization through minimization of particle packings, and a multi-agent flocking simulation. JAX MD is available at www.github.com/google/jax-md.
DCIL: Deep Contextual Internal Learning for Image Restoration and Image Retargeting
Authors: Indra Deep Mastan, Shanmuganathan Raman
Date: 2019-12-09 00:00:00
Tasks: Diversity, Image Restoration, Image Retargeting, Super-Resolution, Zero-Shot Learning

Recently, there is a vast interest in developing methods which are independent of the training samples such as deep image prior, zero-shot learning, and internal learning. The methods above are based on the common goal of maximizing image features learning from a single image despite inherent technical diversity. In this work, we bridge the gap between the various unsupervised approaches above and propose a general framework for image restoration and image retargeting. We use contextual feature learning and internal learning to improvise the structure similarity between the source and the target images. We perform image resize application in the following setups: classical image resize using super-resolution, a challenging image resize where the low-resolution image contains noise, and content-aware image resize using image retargeting. We also provide comparisons to the relevant state-of-the-art methods.
Location Trace Privacy Under Conditional Priors
Authors: Casey Meehan, Kamalika Chaudhuri
Date: 2019-12-09 00:00:00

Providing meaningful privacy to users of location based services is particularly challenging when multiple locations are revealed in a short period of time. This is primarily due to the tremendous degree of dependence that can be anticipated between points. We propose a R\'enyi differentially private framework for bounding expected privacy loss for conditionally dependent data. Additionally, we demonstrate an algorithm for achieving this privacy under Gaussian process conditional priors. This framework both exemplifies why conditionally dependent data is so challenging to protect and offers a strategy for preserving privacy to within a fixed radius for every user location in a trace.
Unsupervised Curricula for Visual Meta-Reinforcement Learning
Authors: Allan Jabri, Kyle Hsu, Ben Eysenbach, Abhishek Gupta, Sergey Levine, Chelsea Finn
Date: 2019-12-09 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Clustering, Meta-Learning, Meta Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In principle, meta-reinforcement learning algorithms leverage experience across many tasks to learn fast reinforcement learning (RL) strategies that transfer to similar tasks. However, current meta-RL approaches rely on manually-defined distributions of training tasks, and hand-crafting these task distributions can be challenging and time-consuming. Can "useful" pre-training tasks be discovered in an unsupervised manner? We develop an unsupervised algorithm for inducing an adaptive meta-training task distribution, i.e. an automatic curriculum, by modeling unsupervised interaction in a visual environment. The task distribution is scaffolded by a parametric density model of the meta-learner's trajectory distribution. We formulate unsupervised meta-RL as information maximization between a latent task variable and the meta-learner's data distribution, and describe a practical instantiation which alternates between integration of recent experience into the task distribution and meta-learning of the updated tasks. Repeating this procedure leads to iterative reorganization such that the curriculum adapts as the meta-learner's data distribution shifts. In particular, we show how discriminative clustering for visual representation can support trajectory-level task acquisition and exploration in domains with pixel observations, avoiding pitfalls of alternatives. In experiments on vision-based navigation and manipulation domains, we show that the algorithm allows for unsupervised meta-learning that transfers to downstream tasks specified by hand-crafted reward functions and serves as pre-training for more efficient supervised meta-learning of test task distributions.
cGANs with Multi-Hinge Loss
Authors: Ilya Kavalerov, Wojciech Czaja, Rama Chellappa
Date: 2019-12-09 00:00:00
Tasks: Conditional Image Generation

We propose a new algorithm to incorporate class conditional information into the critic of GANs via a multi-class generalization of the commonly used Hinge loss that is compatible with both supervised and semi-supervised settings. We study the compromise between training a state of the art generator and an accurate classifier simultaneously, and propose a way to use our algorithm to measure the degree to which a generator and critic are class conditional. We show the trade-off between a generator-critic pair respecting class conditioning inputs and generating the highest quality images. With our multi-hinge loss modification we are able to improve Inception Scores and Frechet Inception Distance on the Imagenet dataset. We make our tensorflow code available at https://github.com/ilyakava/gan.
Learning Latent State Spaces for Planning through Reward Prediction
Authors: Aaron Havens, Yi Ouyang, Prabhat Nagarajan, Yasuhiro Fujita
Date: 2019-12-09 00:00:00
Tasks: Model-based Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Model-based reinforcement learning methods typically learn models for high-dimensional state spaces by aiming to reconstruct and predict the original observations. However, drawing inspiration from model-free reinforcement learning, we propose learning a latent dynamics model directly from rewards. In this work, we introduce a model-based planning framework which learns a latent reward prediction model and then plans in the latent state-space. The latent representation is learned exclusively from multi-step reward prediction which we show to be the only necessary information for successful planning. With this framework, we are able to benefit from the concise model-free representation, while still enjoying the data-efficiency of model-based algorithms. We demonstrate our framework in multi-pendulum and multi-cheetah environments where several pendulums or cheetahs are shown to the agent but only one of which produces rewards. In these environments, it is important for the agent to construct a concise latent representation to filter out irrelevant observations. We find that our method can successfully learn an accurate latent reward prediction model in the presence of the irrelevant information while existing model-based methods fail. Planning in the learned latent state-space shows strong performance and high sample efficiency over model-free and model-based baselines.
Prediction of Sewer Pipe Deterioration Using Random Forest Classification
Authors: Razieh Tavakoli, Ali Sharifara, Mohammad Najafi
Date: 2019-12-09 00:00:00
Tasks: Classification, General Classification, Prediction

Wastewater infrastructure systems deteriorate over time due to a combination of physical and chemical factors. Failure of this significant infrastructure could affect important social, environmental, and economic impacts. Furthermore, recognizing the optimized timeline for inspection of sewer pipelines are challenging tasks for the utility managers and other authorities. Regular examination of sewer networks is not cost-effective due to limited time and high cost of assessment technologies and a large inventory of pipes. To avoid such obstacles, various researchers endeavored to improve infrastructure condition assessment methodologies to maintain sewer pipe systems at the desired condition. Sewer condition prediction models are developed to provide a framework to forecast the future condition of pipes to schedule inspection frequencies. The main goal of this study is to develop a predictive model for wastewater pipes using random forest classification. Predictive models can effectively predict sewer pipe condition and can increase the certainty level of the predictive results and decrease uncertainty in the current condition of wastewater pipes. The developed random forest classification model has achieved a stratified test set false negative rate, the false positive rate, and an excellent area under the ROC curve of 0.81 in a case study application for the City of LA, California. An area under the ROC curve > 0.80 indicates the developed model is an "excellent" choice for predicting the condition of individual pipes in a sewer network. The deterioration models can be used in the industry to improve the inspection timeline and maintenance planning.
Robust and Sample Optimal Algorithms for PSD Low-Rank Approximation
Authors: Ainesh Bakshi, Nadiia Chepurko, David P. Woodruff
Date: 2019-12-09 00:00:00

Recently, Musco and Woodruff (FOCS, 2017) showed that given an $n \times n$ positive semidefinite (PSD) matrix $A$, it is possible to compute a $(1+\epsilon)$-approximate relative-error low-rank approximation to $A$ by querying $O(nk/\epsilon^{2.5})$ entries of $A$ in time $O(nk/\epsilon^{2.5} +n k^{\omega-1}/\epsilon^{2(\omega-1)})$. They also showed that any relative-error low-rank approximation algorithm must query $\Omega(nk/\epsilon)$ entries of $A$, this gap has since remained open. Our main result is to resolve this question by obtaining an optimal algorithm that queries $O(nk/\epsilon)$ entries of $A$ and outputs a relative-error low-rank approximation in $O(n(k/\epsilon)^{\omega-1})$ time. Note, our running time improves that of Musco and Woodruff, and matches the information-theoretic lower bound if the matrix-multiplication exponent $\omega$ is $2$. We then extend our techniques to negative-type distance matrices. Bakshi and Woodruff (NeurIPS, 2018) showed a bi-criteria, relative-error low-rank approximation which queries $O(nk/\epsilon^{2.5})$ entries and outputs a rank-$(k+4)$ matrix. We show that the bi-criteria guarantee is not necessary and obtain an $O(nk/\epsilon)$ query algorithm, which is optimal. Our algorithm applies to all distance matrices that arise from metrics satisfying negative-type inequalities, including $\ell_1, \ell_2,$ spherical metrics and hypermetrics. Next, we introduce a new robust low-rank approximation model which captures PSD matrices that have been corrupted with noise. While a sample complexity lower bound precludes sublinear algorithms for arbitrary PSD matrices, we provide the first sublinear time and query algorithms when the corruption on the diagonal entries is bounded. As a special case, we show sample-optimal sublinear time algorithms for low-rank approximation of correlation matrices corrupted by noise.
Learning a Neural 3D Texture Space from 2D Exemplars
Authors: Philipp Henzler, Niloy J. Mitra, Tobias Ritschel
Date: 2019-12-09 00:00:00
Proceeding: CVPR 2020 6
Tasks: Computational Efficiency, Diversity

We propose a generative model of 2D and 3D natural textures with diversity, visual fidelity and at high computational efficiency. This is enabled by a family of methods that extend ideas from classic stochastic procedural texturing (Perlin noise) to learned, deep, non-linearities. The key idea is a hard-coded, tunable and differentiable step that feeds multiple transformed random 2D or 3D fields into an MLP that can be sampled over infinite domains. Our model encodes all exemplars from a diverse set of textures without a need to be re-trained for each exemplar. Applications include texture interpolation, and learning 3D textures from 2D exemplars.
Butterfly-Net2: Simplified Butterfly-Net and Fourier Transform Initialization
Authors: Zhongshu Xu, Yingzhou Li, Xiuyuan Cheng
Date: 2019-12-09 00:00:00
Tasks: Deblurring, Denoising

Structured CNN designed using the prior information of problems potentially improves efficiency over conventional CNNs in various tasks in solving PDEs and inverse problems in signal processing. This paper introduces BNet2, a simplified Butterfly-Net and inline with the conventional CNN. Moreover, a Fourier transform initialization is proposed for both BNet2 and CNN with guaranteed approximation power to represent the Fourier transform operator. Experimentally, BNet2 and the Fourier transform initialization strategy are tested on various tasks, including approximating Fourier transform operator, end-to-end solvers of linear and nonlinear PDEs, and denoising and deblurring of 1D signals. On all tasks, under the same initialization, BNet2 achieves similar accuracy as CNN but has fewer parameters. And Fourier transform initialized BNet2 and CNN consistently improve the training and testing accuracy over the randomly initialized CNN.
A Weak Supervision Approach to Detecting Visual Anomalies for Automated Testing of Graphics Units
Authors: Adi Szeskin, Lev Faivishevsky, Ashwin K Muppalla, Amitai Armon, Tom Hope
Date: 2019-12-09 00:00:00
Tasks: Multiple Instance Learning

We present a deep learning system for testing graphics units by detecting novel visual corruptions in videos. Unlike previous work in which manual tagging was required to collect labeled training data, our weak supervision method is fully automatic and needs no human labelling. This is achieved by reproducing driver bugs that increase the probability of generating corruptions, and by making use of ideas and methods from the Multiple Instance Learning (MIL) setting. In our experiments, we significantly outperform unsupervised methods such as GAN-based models and discover novel corruptions undetected by baselines, while adhering to strict requirements on accuracy and efficiency of our real-time system.
Optimism in Reinforcement Learning with Generalized Linear Function Approximation
Authors: Yining Wang, Ruosong Wang, Simon S. Du, Akshay Krishnamurthy
Date: 2019-12-09 00:00:00
Proceeding: ICLR 2021 1
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We design a new provably efficient algorithm for episodic reinforcement learning with generalized linear function approximation. We analyze the algorithm under a new expressivity assumption that we call "optimistic closure," which is strictly weaker than assumptions from prior analyses for the linear setting. With optimistic closure, we prove that our algorithm enjoys a regret bound of $\tilde{O}(\sqrt{d^3 T})$ where $d$ is the dimensionality of the state-action features and $T$ is the number of episodes. This is the first statistically and computationally efficient algorithm for reinforcement learning with generalized linear functions.
Estimation of Muscle Fascicle Orientation in Ultrasonic Images
Authors: Regina Pohle-Fröhlich, Christoph Dalitz, Charlotte Richter, Benjamin Stäudle, Kirsten Albracht
Date: 2019-12-09 00:00:00

We compare four different algorithms for automatically estimating the muscle fascicle angle from ultrasonic images: the vesselness filter, the Radon transform, the projection profile method and the gray level cooccurence matrix (GLCM). The algorithm results are compared to ground truth data generated by three different experts on 425 image frames from two videos recorded during different types of motion. The best agreement with the ground truth data was achieved by a combination of pre-processing with a vesselness filter and measuring the angle with the projection profile method. The robustness of the estimation is increased by applying the algorithms to subregions with high gradients and performing a LOESS fit through these estimates.
Recurrent Point Processes for Dynamic Review Models
Authors: Kostadin Cvejoski, Ramses J. Sanchez, Bogdan Georgiev, Jannis Schuecker, Christian Bauckhage, Cesar Ojeda
Date: 2019-12-09 00:00:00
Tasks: Point Processes, Recommendation Systems

Recent progress in recommender system research has shown the importance of including temporal representations to improve interpretability and performance. Here, we incorporate temporal representations in continuous time via recurrent point process for a dynamical model of reviews. Our goal is to characterize how changes in perception, user interest and seasonal effects affect review text.
Query Auto Completion for Math Formula Search
Authors: Shaurya Rohatgi, Wei Zhong, Richard Zanibbi, Jian Wu, C. Lee Giles
Date: 2019-12-09 00:00:00
Tasks: Math

Query Auto Completion (QAC) is among the most appealing features of a web search engine. It helps users formulate queries quickly with less effort. Although there has been much effort in this area for text, to the best of our knowledge there is few work on mathematical formula auto completion. In this paper, we implement 5 existing QAC methods on mathematical formula and evaluate them on the NTCIR-12 MathIR task dataset. We report the efficiency of retrieved results using Mean Reciprocal Rank (MRR) and Mean Average Precision(MAP). Our study indicates that the Finite State Transducer outperforms other QAC models with a MRR score of $0.642$.
Temporal Factorization of 3D Convolutional Kernels
Authors: Gabriëlle Ras, Luca Ambrogioni, Umut Güçlü, Marcel A. J. van Gerven
Date: 2019-12-09 00:00:00

3D convolutional neural networks are difficult to train because they are parameter-expensive and data-hungry. To solve these problems we propose a simple technique for learning 3D convolutional kernels efficiently requiring less training data. We achieve this by factorizing the 3D kernel along the temporal dimension, reducing the number of parameters and making training from data more efficient. Additionally we introduce a novel dataset called Video-MNIST to demonstrate the performance of our method. Our method significantly outperforms the conventional 3D convolution in the low data regime (1 to 5 videos per class). Finally, our model achieves competitive results in the high data regime (>10 videos per class) using up to 45% fewer parameters.
DeepFuse: An IMU-Aware Network for Real-Time 3D Human Pose Estimation from Multi-View Image
Authors: Fuyang Huang, Ailing Zeng, Minhao Liu, Qiuxia Lai, Qiang Xu
Date: 2019-12-09 00:00:00
Tasks: 3D Human Pose Estimation, 3D Pose Estimation, Pose Estimation

In this paper, we propose a two-stage fully 3D network, namely \textbf{DeepFuse}, to estimate human pose in 3D space by fusing body-worn Inertial Measurement Unit (IMU) data and multi-view images deeply. The first stage is designed for pure vision estimation. To preserve data primitiveness of multi-view inputs, the vision stage uses multi-channel volume as data representation and 3D soft-argmax as activation layer. The second one is the IMU refinement stage which introduces an IMU-bone layer to fuse the IMU and vision data earlier at data level. without requiring a given skeleton model a priori, we can achieve a mean joint error of $28.9$mm on TotalCapture dataset and $13.4$mm on Human3.6M dataset under protocol 1, improving the SOTA result by a large margin. Finally, we discuss the effectiveness of a fully 3D network for 3D pose estimation experimentally which may benefit future research.