Datasets and Evaluation for Simultaneous Localization and Mapping Related Problems A Comprehensive Survey

Simultaneous Localization and Mapping (SLAM) has found an increasingutilization lately, such as self-driving cars, robot navigation, 3D mapping, virtual reality (VR) and augmented reality (AR) The employment of datasets is essentially a kind ofsimulation but profits many aspects – capacity of drilling algorithm hourly,exemption of costly hardware and ground truth system, and equitable benchmarkfor evaluation.…

Derivation of the Backpropagation Algorithm Based on Derivative Amplification Coefficients

The backpropagation algorithm for neural networks is widely felt hard to understand . This paper provides a new derivation of this algorithm based on the concept of derivative amplification coefficients . The concept is found to well carry over to conventional feedforward neural networks and it paves the way for the use of mathematical induction in establishing a key result that enablesbackpropagating for derivative amplification .…

Neural Termination Analysis

We introduce a novel approach to automated termination analysis of computer programs . We train neural networks to act as ranking functions . The existence of a valid ranking function provesthat the program terminates . We present a custom loss function for learning lexicographic ranking functions and uses satisfactioniability modulo theories for verification .…

High dimensional nonlinear approximation by parametric manifolds in Hölder Nikol skii spaces of mixed smoothness

We study high-dimensional nonlinear approximation of functions inH\”older-Nikol’skii spaces $H^\alpha_\infty(\mathbb{I}^d) on the unit cube$\mathbb {I}$d:=[0,1]^d$ We derived a novel right asymptotic orderof orderof noncontinuous manifold $N-widths of the unit ball of $H$N- widths . Inconstructing approximation methods, the function decomposition by the tensorproduct Faber series plays a central role .…

Infinite GMRES for parameterized linear systems

Methods combine the well-established GMRES method for linear systems with algorithms for nonlineareigenvalue problems (NEPs) to generate a basis for the Krylov subspace . Weshow convergence factor bounds obtained similarly to those for the method GMRESfor linear systems. More specifically, a bound is obtained based on themagnitude of the parameter $mu$ and the spectrum of the linear companionmatrix, which corresponds to the reciprocal solutions to the corresponding NEP.…

Learning the exchange correlation functional from nature with fully differentiable density functional theory

Machine learning is essential for advanced material discovery . We show how training aneural network to replace the exchange-correlation functional within afully-differentiable three-dimensional Kohn-Sham density functional theory framework can greatly improve simulation accuracy . Using only eight experimental data points on diatomic molecules, our trainedexchange-correlrelation network provided improved prediction of atomization andionization energies across a collection of 110 molecules when compared with commonly used DFT functionals and more expensive coupled clustersimulations .…

Constrained Ensemble Langevin Monte Carlo

The classical Langevin Monte Carlo method looks for i.i.d. samples from atarget distribution by descending along the gradient of the target distribution . It is popular partially due to its fast convergence rate . However, the numerical cost is sometimes high because the gradient can be hard to obtain .…

Deep Reinforcement Learning for the Control of Robotic Manipulation A Focussed Mini Review

Deep learning has provided new ways of manipulating, processing and analyzing data . Another subfield of machine learning namedreinforcement learning, tries to find an optimal behavior strategy through interactions with the environment . Combining deep learning and reinforcementlearning permits resolving critical issues relative to the dimensionality and scalability of data in tasks with sparse reward signals .…

Asynchronous semi anonymous dynamics over large scale networks

We analyze a class of stochastic processes, referred to as asynchronous andsemi-anonymous dynamics (ASD) over directed labeled random networks . These processes are a natural tool to describe general best-response and noisybest-response dynamics in network games where each agent, at random timesgoverned by independent Poisson clocks, can choose among a finite set ofactions .…

Manipulation Planning Among Movable Obstacles Using Physics Based Adaptive Motion Primitives

Robot manipulation in cluttered scenes often requires contact-rich interactions with objects . For each object in a scene, depending on its properties, the robot mayor may not be allowed to make contact with, tilt, or topple it . To ensure that these constraints are satisfied during non-prehensile interactions, a planner can query a physics-based simulator to evaluate the complex multi-bodyinteractions caused by robot actions .…

Reliable Probabilistic Face Embeddings in the Wild

Probabilistic Face Embeddings (PFE) can improve face recognition performance by integrating data uncertainty into the featurerepresentation . However, existing PFE methods tend to be over-confident inestimating uncertainty and is too slow to apply to large-scale face matching . This paper proposes a regularized probabilistic face embedding method to improve the robustness and speed of PFE .…

Distributed Storage Allocations for Optimal Service Rates

This paper considers the uncertainty in nodeaccess and download service . In one access model, a user can access each node with a fixedprobability, and the other, a random fixed-size subset of nodes . For afixed redundancy level, the systems’ service rate is determined by the allocation of coded chunks over storage nodes .…

Arbitrary Conditional Distributions with Energy

Arbitrary Conditioning with Energy (ACE) uses an energy function to specify densities . ACE is state-of-the-art for arbitrary conditional and marginal likelihoodestimation and for tabular data imputation . We also simplify the learningproblem by only learning one-dimensional conditionals, from which more complexdistributions can be recovered during inference .…

Black Box Optimization via Generative Adversarial Nets

Black-box optimization (BBO) algorithms are concerned with finding the bestsolutions for the problems with missing analytical details . Most classical methods for such problems are based on strong and fixed \emph{a priori}assumptions such as Gaussian distribution . But lots of complex real-world problems are far from the distribution of the Gaussian .…

Communication efficient k Means for Edge based Machine Learning

We consider the problem of computing the k-means centers for a largehigh-dimensional dataset in the context of edge-based machine learning . Wepropose to let the data sources send small summaries, generated by jointdimensionality reduction (DR) and cardinality reduction (CR) to support approximating k-Means computation at reduced complexity and communication cost .…

Provable Model based Nonlinear Bandit and Reinforcement Learning Shelve Optimism Embrace Virtual Curvature

This paper studies model-based bandit and reinforcement learning (RL) with nonlinear function approximations . Global convergence is intractable even for one-layer neural net bandit with adeterministic reward . On the other hand, for convergence to localmaxima, it suffices to maximize the virtual return if the model can alsoreasonably predict the size of the gradient and Hessian of the real return .…

Learning N M Fine grained Structured Sparse Neural Networks From Scratch

Sparsity in Deep Neural Networks (DNNs) has been widely studied to compress and accelerate the models on resource-constrained environments . Fine-grained sparsity can achieve a high compression ratio but is not hardware friendly and hence receives limited speed gains . We propose a noveland effective ingredient, sparse-refined straight-through estimator (SR-STE) We also define a metric, Sparse ArchitectureDivergence (SAD), to measure the sparse network’s topology change during the training process .…

Analysis of the Optimization Landscape of Linear Quadratic Gaussian LQG Control

This paper revisits the classical Linear Quadratic Gaussian (LQG) control from a modern optimization perspective . We analyze two aspects of the optimization landscape of the LQG problem: connectivity of the set ofstabilizing controllers and structure of stationary points . These results shed some light on the performanceanalysis of direct policy gradient methods for solving the problem .…

Sensor Planning for Large Numbers of Robots

After a disaster, locating and extracting victims quickly is critical because mortality rises rapidly after the first two days . To assist search and rescueteams and improve response times, teams of camera-equipped aerial robots canengage in tasks such as mapping buildings and locating victims .…

RL Scope Cross Stack Profiling for Deep Reinforcement Learning Workloads

RL has made groundbreaking advancements in robotic, datacenter managements and other applications . System-level bottlenecks in RL workloads are poorly understood . We observe fundamental structural differences in RLworkloads that make them inherently less GPU-bound than supervised learning . RL-Scope is an open-source tool available at https://://://github.com/UofT-EcoSystem/rlscope…

Towards Hierarchical Task Decomposition using Deep Reinforcement Learning for Pick and Place Subtasks

DeepReinforcement Learning (DRL) is one of the leading robotic automation techniquethat has been able to achieve dexterous manipulation and locomotion roboticsskills . We propose a multi-subtask reinforcement learning method where complex tasks can be decomposed into low-level subtasks . Thesesubtasks can be parametrised as expert networks and learnt via existing DRL methods .…

Operation is the hardest teacher estimating DNN accuracy looking for mispredictions

DeepEST looks for failing test cases in the operational dataset of a DNN, with the goal of assessing the DNN expected accuracy by a small and ”informative” test suite . The results show that DeepEST provides DNNaccuracy estimates with precision close to (and often better than) those of existing sampling-based DNN testing techniques, while detecting from 5 to 30times more mispredictions with the same test suite size .…

Contrastive Embeddings for Neural Architectures

The performance of algorithms for neural architecture search strongly depend on the parametrization of the search space . We use contrastive learning to identify networks across different initializations based on their dataJacobians . We show that traditional black-box optimization algorithms, without modification, can reach state-of-the-art performance in Neural ArchitectureSearch .…

Bayesian Poroelastic Aquifer Characterization from InSAR Surface Deformation Data Part II Quantifying the Uncertainty

Uncertainty quantification of groundwater (GW) aquifer parameters is critical for efficient management and sustainable extraction of GW resources . Here we develop a Bayesian inversion framework that usesInterferometric Synthetic Aperture Radar (InSAR) surface deformation data to ferry the laterally heterogeneous permeability of a confined GW aquifer .…

Overhead MNIST A Benchmark Satellite Dataset

The research presents an overhead view of 10 important objects and followsthe general formatting requirements of the most popular machine learning task:digit recognition with MNIST . A prototype deep learning approach with transfer learning and convolutional neural networks (MobileNetV2)correctly identifies the ten overhead classes with an average accuracy of96.7%.…