pwc-archive/papers-with-abstracts huggingface dataset:
Faster High Density Monochromic QR-Codes with Reduced Error Rate
Authors: Kishor Datta Gupta
Date: 2018-01-01 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Quick Response (QR) code getting widely popular as mobile computing is increasing. But QR code has data limit problem which researchers tried to solve by making color QR codes. But having color QR codes decoding from printed material has lots of image processing problems which are due to different printers color depth, paper quality, environmental effect, dust, light glare and other environmental effects.   In our proposed process, we have applied area-based bit detection by percentage of the color level instead of the general binary color bit detection system. This is independent to the printer and the printed substances effect. There is no needed of color correction palette. We also analyzed the performance of other QR codes with us and we find significant error reductions in our technique. In our process, we used only black and white color to print in each bit module and from the percentage of area covered we deducted the level of color percentage despite positions of color bits in that module which increased its scanning speed and also reduced noise error rate to less than 10 percent
Towards Extracting Absolute Event Timelines From English Clinical Reports
Authors: Artuur Leeuwenberg, Marie-Francine Moens
Date: 2020-09-28 00:00:00
Tasks: Natural Language Understanding, Temporal Information Extraction

Temporal information extraction is a challenging but important area of automatic natural language understanding. Existing approaches annotate and extract various parts of the temporal information conveyed in language like relative event order, temporal expressions, or event durations. Most schemes focus primarily on annotation of temporally certain (often explicit) information, resulting in partial annotation, and under-representation of implicit information. In this article, we propose an approach towards extraction of more complete (implicit and explicit) temporal information for all events, and obtain probabilistic absolute event timelines by modeling temporal uncertainty with information bounds. As a case study, we use our scheme to annotate a set of English clinical reports, and propose and evaluate a multi-regression model for predicting probabilistic absolute timelines, obtaining promising results.
Measures of Model Risk in Continuous-time Finance Models
Authors: Emese Lazar. Shuyuan Qi, Radu Tunaru
Date: 2020-10-16 00:00:00
Tasks: parameter estimation

Measuring model risk is required by regulators on financial and insurance markets. We separate model risk into parameter estimation risk and model specification risk, and we propose expected shortfall type model risk measures applied to Levy jump models and affine jump-diffusion models. We investigate the impact of parameter estimation risk and model specification risk on the models'ability to capture the joint dynamics of stock and option prices. We estimate the parameters using Markov chain Monte Carlo techniques, under the risk-neutral probability measure and the real-world probability measure jointly. We find strong evidence supporting modeling of price jumps.
Effective Combination of DenseNet andBiLSTM for Keyword Spotting
Authors: M. Zeng, N. Xiao
Date: 2019-01-19 00:00:00
Tasks: Keyword Spotting, Time Series, Time Series Analysis

Keyword spotting (KWS) is a major component of human–computer interaction for smarton-device terminals and service robots, the purpose of which is to maximize the detection accuracy whilekeeping footprint size small. In this paper, based on the powerful ability of DenseNet on extracting localfeature-maps, we propose a new network architecture (DenseNet-BiLSTM) for KWS. In our DenseNet-BiLSTM, the DenseNet is primarily applied to obtain local features, while the BiLSTM is used to grabtime series features. In general, the DenseNet is used in computer vision tasks, and it may corrupt contextualinformation for speech audios. In order to make DenseNet suitable for KWS, we propose a variant DenseNet,called DenseNet-Speech, which removes the pool on the time dimension in transition layers to preservespeech time series information. In addition, our DenseNet-Speech uses less dense blocks and filters to keepthe model small, thereby reducing time consumption for mobile devices. The experimental results show thatfeature-maps from DenseNet-Speech maintain time series information well. Our method outperforms thestate-of-the-art methods in terms of accuracy on Google Speech Commands dataset. DenseNet-BiLSTM isable to achieve the accuracy of 96.6% for the 20-commands recognition task with 223K trainable parameters.
Double Machine Learning based Program Evaluation under Unconfoundedness
Authors: Michael C. Knaus
Date: 2020-03-06 00:00:00
Tasks: BIG-bench Machine Learning

This paper reviews, applies and extends recently proposed methods based on Double Machine Learning (DML) with a focus on program evaluation under unconfoundedness. DML based methods leverage flexible prediction models to adjust for confounding variables in the estimation of (i) standard average effects, (ii) different forms of heterogeneous effects, and (iii) optimal treatment assignment rules. An evaluation of multiple programs of the Swiss Active Labour Market Policy illustrates how DML based methods enable a comprehensive program evaluation. Motivated by extreme individualised treatment effect estimates of the DR-learner, we propose the normalised DR-learner (NDR-learner) to address this issue. The NDR-learner acknowledges that individualised effect estimates can be stabilised by an individualised normalisation of inverse probability weights.
Sample-Efficient Neural Architecture Search by Learning Action Space for Monte Carlo Tree Search
Authors: Linnan Wang, Saining Xie, Teng Li, Rodrigo Fonseca, Yuandong Tian
Date: 2019-01-01 00:00:00
Tasks: Evolutionary Algorithms, Image Classification, Neural Architecture Search

Neural Architecture Search (NAS) has emerged as a promising technique for automatic neural network design. However, existing MCTS based NAS approaches often utilize manually designed action space, which is not directly related to the performance metric to be optimized (e.g., accuracy), leading to sample-inefficient explorations of architectures. To improve the sample efficiency, this paper proposes Latent Action Neural Architecture Search (LaNAS), which learns actions to recursively partition the search space into good or bad regions that contain networks with similar performance metrics. During the search phase, as different action sequences lead to regions with different performance, the search efficiency can be significantly improved by biasing towards the good regions. On three NAS tasks, empirical results demonstrate that LaNAS is at least an order more sample efficient than baseline methods including evolutionary algorithms, Bayesian optimizations and random search. When applied in practice, both one-shot and regular LaNAS consistently outperforms existing results. Particularly, LaNAS achieves 99.0\% accuracy on CIFAR-10 and 80.8\% top1 accuracy at 600 MFLOPS on ImageNet in only 800 samples, significantly outperforming AmoebaNet with $33\times$ fewer samples.
A Probabilistic Perspective on Gaussian Filtering and Smoothing
Authors: Marc Peter Deisenroth, Henrik Ohlsson
Date: 2010-06-10 00:00:00

We present a general probabilistic perspective on Gaussian filtering and smoothing. This allows us to show that common approaches to Gaussian filtering/smoothing can be distinguished solely by their methods of computing/approximating the means and covariances of joint probabilities. This implies that novel filters and smoothers can be derived straightforwardly by providing methods for computing these moments. Based on this insight, we derive the cubature Kalman smoother and propose a novel robust filtering and smoothing algorithm based on Gibbs sampling.
Parameter Estimation using Neural Networks in the Presence of Detector Effects
Authors: Anders Andreassen, Shih-Chieh Hsu, Benjamin Nachman, Natchanon Suaysom, Adi Suresh
Date: 2020-10-07 00:00:00
Tasks: parameter estimation

Histogram-based template fits are the main technique used for estimating parameters of high energy physics Monte Carlo generators. Parametrized neural network reweighting can be used to extend this fitting procedure to many dimensions and does not require binning. If the fit is to be performed using reconstructed data, then expensive detector simulations must be used for training the neural networks. We introduce a new two-level fitting approach that only requires one dataset with detector simulation and then a set of additional generation-level datasets without detector effects included. This Simulation-level fit based on Reweighting Generator-level events with Neural networks (SRGN) is demonstrated using simulated datasets for a variety of examples including a simple Gaussian random variable, parton shower tuning, and the top quark mass extraction.
Deep Neural Networks for Low-Cost Eye Tracking
Authors: Ildar Rakhmatulin, Andrew Duchowski
Date: 2020-10-02 00:00:00
Tasks: Position

The paper presents a detailed analysis of modern techniques that can be used to track gaze with a webcam. We present a practical implementation of the most popular methods for tracking gaze. Various models of deep neural networks that can be involved in the process of online gaze monitoring are reviewed. We introduce a new eye-tracking approach where the effectiveness of using a deep learning method is significantly increased. Implementation is in Python where its application is demonstrated by controlling interaction with the computer. Specifically, a dual coordinate system is given for controlling the computer with the help of a gaze. The first set of coordinates-the position of the face relative to the computer, is implemented by detecting color from the infrared LED via the OpenCV library. The second set of coordinates-giving gaze position-is obtained via the YOLO (v3) package. A method of labeling the eyes is given, in which 3 objects are used to track gaze (to the left, to the right, and in the center).
Enabling Fast Differentially Private SGD via Just-in-Time Compilation and Vectorization
Authors: Pranav Subramani, Nicholas Vadivelu, Gautam Kamath
Date: 2020-10-18 00:00:00
Proceeding: NeurIPS 2021 12

A common pain point in differentially private machine learning is the significant runtime overhead incurred when executing Differentially Private Stochastic Gradient Descent (DPSGD), which may be as large as two orders of magnitude. We thoroughly demonstrate that by exploiting powerful language primitives, including vectorization, just-in-time compilation, and static graph optimization, one can dramatically reduce these overheads, in many cases nearly matching the best non-private running times. These gains are realized in two frameworks: JAX and TensorFlow. JAX provides rich support for these primitives as core features of the language through the XLA compiler. We also rebuild core parts of TensorFlow Privacy, integrating features from TensorFlow 2 as well as XLA compilation, granting significant memory and runtime improvements over the current release version. These approaches allow us to achieve up to 50x speedups in comparison to the best alternatives. Our code is available at https://github.com/TheSalon/fast-dpsgd.
Evidential Sparsification of Multimodal Latent Spaces in Conditional Variational Autoencoders
Authors: Masha Itkina, Boris Ivanovic, Ransalu Senanayake, Mykel J. Kochenderfer, Marco Pavone
Date: 2020-10-19 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: Image Generation, Motion Planning, Natural Language Understanding

Discrete latent spaces in variational autoencoders have been shown to effectively capture the data distribution for many real-world problems such as natural language understanding, human intent prediction, and visual scene representation. However, discrete latent spaces need to be sufficiently large to capture the complexities of real-world data, rendering downstream tasks computationally challenging. For instance, performing motion planning in a high-dimensional latent representation of the environment could be intractable. We consider the problem of sparsifying the discrete latent space of a trained conditional variational autoencoder, while preserving its learned multimodality. As a post hoc latent space reduction technique, we use evidential theory to identify the latent classes that receive direct evidence from a particular input condition and filter out those that do not. Experiments on diverse tasks, such as image generation and human behavior prediction, demonstrate the effectiveness of our proposed technique at reducing the discrete latent sample space size of a model while maintaining its learned multimodality.
GASNet: Weakly-supervised Framework for COVID-19 Lesion Segmentation
Authors: Zhanwei Xu, Yukun Cao, Cheng Jin, Guozhu Shao, Xiaoqing Liu, Jie zhou, Heshui Shi, Jianjiang Feng
Date: 2020-10-19 00:00:00
Tasks: Image Segmentation, Lesion Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

Segmentation of infected areas in chest CT volumes is of great significance for further diagnosis and treatment of COVID-19 patients. Due to the complex shapes and varied appearances of lesions, a large number of voxel-level labeled samples are generally required to train a lesion segmentation network, which is a main bottleneck for developing deep learning based medical image segmentation algorithms. In this paper, we propose a weakly-supervised lesion segmentation framework by embedding the Generative Adversarial training process into the Segmentation Network, which is called GASNet. GASNet is optimized to segment the lesion areas of a COVID-19 CT by the segmenter, and to replace the abnormal appearance with a generated normal appearance by the generator, so that the restored CT volumes are indistinguishable from healthy CT volumes by the discriminator. GASNet is supervised by chest CT volumes of many healthy and COVID-19 subjects without voxel-level annotations. Experiments on three public databases show that when using as few as one voxel-level labeled sample, the performance of GASNet is comparable to fully-supervised segmentation algorithms trained on dozens of voxel-level labeled samples.
Deep Structured Prediction for Facial Landmark Detection
Authors: Lisha Chen, Hui Su, Qiang Ji
Date: 2020-10-18 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Face Alignment, Facial Landmark Detection, Prediction, Structured Prediction

Existing deep learning based facial landmark detection methods have achieved excellent performance. These methods, however, do not explicitly embed the structural dependencies among landmark points. They hence cannot preserve the geometric relationships between landmark points or generalize well to challenging conditions or unseen data. This paper proposes a method for deep structured facial landmark detection based on combining a deep Convolutional Network with a Conditional Random Field. We demonstrate its superior performance to existing state-of-the-art techniques in facial landmark detection, especially a better generalization ability on challenging datasets that include large pose and occlusion.
Noisy-LSTM: Improving Temporal Awareness for Video Semantic Segmentation
Authors: Bowen Wang, Liangzhi Li, Yuta Nakashima, Ryo Kawasaki, Hajime Nagahara, Yasushi Yagi
Date: 2020-10-19 00:00:00
Tasks: Semantic Segmentation, Video Segmentation, Video Semantic Segmentation

Semantic video segmentation is a key challenge for various applications. This paper presents a new model named Noisy-LSTM, which is trainable in an end-to-end manner, with convolutional LSTMs (ConvLSTMs) to leverage the temporal coherency in video frames. We also present a simple yet effective training strategy, which replaces a frame in video sequence with noises. This strategy spoils the temporal coherency in video frames during training and thus makes the temporal links in ConvLSTMs unreliable, which may consequently improve feature extraction from video frames, as well as serve as a regularizer to avoid overfitting, without requiring extra data annotation or computational costs. Experimental results demonstrate that the proposed model can achieve state-of-the-art performances in both the CityScapes and EndoVis2018 datasets.
Detecting Hands and Recognizing Physical Contact in the Wild
Authors: Supreeth Narasimhaswamy, Trung Nguyen, Minh Hoai
Date: 2020-10-19 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: Object

We investigate a new problem of detecting hands and recognizing their physical contact state in unconstrained conditions. This is a challenging inference task given the need to reason beyond the local appearance of hands. The lack of training annotations indicating which object or parts of an object the hand is in contact with further complicates the task. We propose a novel convolutional network based on Mask-RCNN that can jointly learn to localize hands and predict their physical contact to address this problem. The network uses outputs from another object detector to obtain locations of objects present in the scene. It uses these outputs and hand locations to recognize the hand's contact state using two attention mechanisms. The first attention mechanism is based on the hand and a region's affinity, enclosing the hand and the object, and densely pools features from this region to the hand region. The second attention module adaptively selects salient features from this plausible region of contact. To develop and evaluate our method's performance, we introduce a large-scale dataset called ContactHands, containing unconstrained images annotated with hand locations and contact states. The proposed network, including the parameters of attention modules, is end-to-end trainable. This network achieves approximately 7\% relative improvement over a baseline network that was built on the vanilla Mask-RCNN architecture and trained for recognizing hand contact states.
Deep Reinforcement Learning with Population-Coded Spiking Neural Network for Continuous Control
Authors: Guangzhi Tang, Neelesh Kumar, Raymond Yoo, Konstantinos P. Michmizos
Date: 2020-10-19 00:00:00
Tasks: continuous-control, Continuous Control, Deep Reinforcement Learning, OpenAI Gym, reinforcement-learning, Reinforcement Learning (RL)

The energy-efficient control of mobile robots is crucial as the complexity of their real-world applications increasingly involves high-dimensional observation and action spaces, which cannot be offset by limited on-board resources. An emerging non-Von Neumann model of intelligence, where spiking neural networks (SNNs) are run on neuromorphic processors, is regarded as an energy-efficient and robust alternative to the state-of-the-art real-time robotic controllers for low dimensional control tasks. The challenge now for this new computing paradigm is to scale so that it can keep up with real-world tasks. To do so, SNNs need to overcome the inherent limitations of their training, namely the limited ability of their spiking neurons to represent information and the lack of effective learning algorithms. Here, we propose a population-coded spiking actor network (PopSAN) trained in conjunction with a deep critic network using deep reinforcement learning (DRL). The population coding scheme dramatically increased the representation capacity of the network and the hybrid learning combined the training advantages of deep networks with the energy-efficient inference of spiking networks. To show the general applicability of our approach, we integrated it with a spectrum of both on-policy and off-policy DRL algorithms. We deployed the trained PopSAN on Intel's Loihi neuromorphic chip and benchmarked our method against the mainstream DRL algorithms for continuous control. To allow for a fair comparison among all methods, we validated them on OpenAI gym tasks. Our Loihi-run PopSAN consumed 140 times less energy per inference when compared against the deep actor network on Jetson TX2, and had the same level of performance. Our results support the efficiency of neuromorphic controllers and suggest our hybrid RL as an alternative to deep learning, when both energy-efficiency and robustness are important.
Increasing Depth Leads to U-Shaped Test Risk in Over-parameterized Convolutional Networks
Authors: Eshaan Nichani, Adityanarayanan Radhakrishnan, Caroline Uhler
Date: 2020-10-19 00:00:00
Tasks: image-classification, Image Classification, Open-Ended Question Answering, regression

Recent works have demonstrated that increasing model capacity through width in over-parameterized neural networks leads to a decrease in test risk. For neural networks, however, model capacity can also be increased through depth, yet understanding the impact of increasing depth on test risk remains an open question. In this work, we demonstrate that the test risk of over-parameterized convolutional networks is a U-shaped curve (i.e. monotonically decreasing, then increasing) with increasing depth. We first provide empirical evidence for this phenomenon via image classification experiments using both ResNets and the convolutional neural tangent kernel (CNTK). We then present a novel linear regression framework for characterizing the impact of depth on test risk, and show that increasing depth leads to a U-shaped test risk for the linear CNTK. In particular, we prove that the linear CNTK corresponds to a depth-dependent linear transformation on the original space and characterize properties of this transformation. We then analyze over-parameterized linear regression under arbitrary linear transformations and, in simplified settings, provably identify the depths which minimize each of the bias and variance terms of the test risk.
End-to-End Learning for Simultaneously Generating Decision Map and Multi-Focus Image Fusion Result
Authors: Boyuan Ma, Xiang Yin, Di wu, Xiaojuan Ban
Date: 2020-10-17 00:00:00
Tasks: 2D Cyclist Detection, Decoder, Multi Focus Image Fusion

The general aim of multi-focus image fusion is to gather focused regions of different images to generate a unique all-in-focus fused image. Deep learning based methods become the mainstream of image fusion by virtue of its powerful feature representation ability. However, most of the existing deep learning structures failed to balance fusion quality and end-to-end implementation convenience. End-to-end decoder design often leads to unrealistic result because of its non-linear mapping mechanism. On the other hand, generating an intermediate decision map achieves better quality for the fused image, but relies on the rectification with empirical post-processing parameter choices. In this work, to handle the requirements of both output image quality and comprehensive simplicity of structure implementation, we propose a cascade network to simultaneously generate decision map and fused result with an end-to-end training procedure. It avoids the dependence on empirical post-processing methods in the inference stage. To improve the fusion quality, we introduce a gradient aware loss function to preserve gradient information in output fused image. In addition, we design a decision calibration strategy to decrease the time consumption in the application of multiple images fusion. Extensive experiments are conducted to compare with 19 different state-of-the-art multi-focus image fusion structures with 6 assessment metrics. The results prove that our designed structure can generally ameliorate the output fused image quality, while implementation efficiency increases over 30\% for multiple images fusion.