How To Read Research Papers

Deep Learning Models for Visual Inspection on Automotive Assembling Line. (arXiv:2007.01857v1 [cs.CV])
Automotive manufacturing assembly tasks are built upon visual inspections such as scratch identification on machined surfaces, part identification and selection,...

Improved flat-back 3D gadgets in origami extrusions completely downward compatible with the conventional pyramid-supported 3D gadgets. (arXiv:2007.01859v1 [cs.CG])
An origami extrusion is a folding of a 3D object in the middle of a flat piece of paper, using...

An Analysis of Data Driven, Decision-Making Capabilities of Managers in Banks. (arXiv:2007.01862v1 [cs.HC])
Organizations are adopting data analytics and Business Intelligence (BI) tools to gain insights from the past data, forecast future events,...

Accurate Bounding-box Regression with Distance-IoU Loss for Visual Tracking. (arXiv:2007.01864v1 [cs.CV])
Most existing tracking methods are based on using a classifier and multi-scale estimation to estimate the state of the target....

Selecting Regions of Interest in Large Multi-Scale Images for Cancer Pathology. (arXiv:2007.01866v1 [eess.IV])
Recent breakthroughs in object detection and image classification using Convolutional Neural Networks (CNNs) are revolutionizing the state of the art...

TLIO: Tight Learned Inertial Odometry. (arXiv:2007.01867v1 [cs.RO])
In this work we propose a tightly-coupled Extended Kalman Filter framework for IMU-only state estimation. Strap-down IMU measurements provide relative...

Dalek -- a deep-learning emulator for TARDIS. (arXiv:2007.01868v1 [astro-ph.IM])
Supernova spectral time series contain a wealth of information about the progenitor and explosion process of these energetic events. The...

Egocentric Action Recognition by Video Attention and Temporal Context. (arXiv:2007.01883v1 [cs.CV])
We present the submission of Samsung AI Centre Cambridge to the CVPR2020 EPIC-Kitchens Action Recognition Challenge. In this challenge, action...

High-recall causal discovery for autocorrelated time series with latent confounders. (arXiv:2007.01884v1 [stat.ME])
We present a new method for linear and nonlinear, lagged and contemporaneous constraint-based causal discovery from observational time series in...

A Unifying View of Optimism in Episodic Reinforcement Learning. (arXiv:2007.01891v1 [cs.LG])
The principle of optimism in the face of uncertainty underpins many theoretically successful reinforcement learning algorithms. In this paper we...

b-articulation points and b-bridges in strongly biconnected directed graphs. (arXiv:2007.01897v1 [cs.DS])
A directed graph $G=(V,E)$ is called strongly biconnected if $G$ is strongly connected and the underlying graph of $G$ is...

A Few-Shot Sequential Approach for Object Counting. (arXiv:2007.01899v1 [cs.CV])
In this work, we address the problem of few-shot multi-classobject counting with point-level annotations. The proposed techniqueleverages a class agnostic...

Examining Redundancy in the Context of Safe Machine Learning. (arXiv:2007.01900v1 [cs.LG])
This paper describes a set of experiments with neural network classifiers on the MNIST database of digits. The purpose is...

Model Distillation for Revenue Optimization: Interpretable Personalized Pricing. (arXiv:2007.01903v1 [stat.ML])
Data-driven pricing strategies are becoming increasingly common, where customers are offered a personalized price based on features that are predictive...

The Effect of Class Imbalance on Precision-Recall Curves. (arXiv:2007.01905v1 [cs.LG])
In this note I study how the precision of a classifier depends on the ratio $r$ of positive to negative...

Graph2Kernel Grid-LSTM: A Multi-Cued Model for Pedestrian Trajectory Prediction by Learning Adaptive Neighborhoods. (arXiv:2007.01915v1 [cs.CV])
Pedestrian trajectory prediction is a prominent research track that has advanced towards modelling of crowd social and contextual interactions, with...

Abstractive and mixed summarization for long-single documents. (arXiv:2007.01918v1 [cs.CL])
The lack of diversity in the datasets available for automatic summarization of documents has meant that the vast majority of...

Efficient Marginalization of Discrete and Structured Latent Variables via Sparsity. (arXiv:2007.01919v1 [cs.LG])
Training neural network models with discrete (categorical or structured) latent variables can be computationally challenging, due to the need for...

Human-Robot Team Coordination with Dynamic and Latent Human Task Proficiencies: Scheduling with Learning Curves. (arXiv:2007.01921v1 [cs.RO])
As robots become ubiquitous in the workforce, it is essential that human-robot collaboration be both intuitive and adaptive. A robot's...

Knowledge Distillation Beyond Model Compression. (arXiv:2007.01922v1 [cs.LG])
Knowledge distillation (KD) is commonly deemed as an effective model compression technique in which a compact model (student) is trained...


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