Papers

# Best matching news and articles for Research papers

Title Summary/Keywords
Think outside the black box
https://towardsdatascience.com/ | Today
Content: How glass box models can provide X AI without compromising resultsPhoto by on These models are often referred to as black box models. We have set of input features, then we give these features to our model, which then does complex calculation, and comes to decision. However, we do not know exactly how the model comes to its decisions, which features it finds important or even what it looks at. In some cases, it is not terribly important for humans to fully understand how the model works...

Keywords: artificial intelligence, game, machine learning
Advancing Excel as a programming language with Andy Gordon and Simon Peyton Jones
https://www.microsoft.com/ | Yesterday
Content: Today, people around the globefrom teachers to small business owners to finance executivesuse Microsoft Excel to make sense of the information that occupies their respective worlds, and whether they realize it or not, in doing so, theyre taking on the role of programmer. In this episode, Senior Principal Research Manager Andy Gordon, who leads the Calc Intelligence team at Microsoft Research, and Senior Principal Researcher Simon Peyton Jones provide an inside account of the journey Excel has...

Keywords: scala, java, security, tpu, c
Quantpedia in April 2021
https://quantpedia.com/ | Yesterday
Content: The time flies very fast, and like every month, have again bunch of interesting improvements would like to present to all of you. We again have two new reports for the Market Phase Analysis and the Portfolio Risk Parity reports that will describe soon.But first, let s recapitulate Quantpedia Premium development. Ten new have been added to our database, and twelve new related research papers have been included in existing Premium strategies during the last month. Additionally, we have...

Keywords: quant, analysis, database, test
Guide to Googles STAC: An SSL Framework For Object Detection
https://analyticsindiamag.com/ | Today
Content: The Google Brain team has introduced STAC, semi supervised learning SSL framework to perform object detection in simplified wayThe post Guide to Googles STAC An SSL Framework For Object Detection appeared first on Analytics India Magazine....

Keywords: tensorflow, git , supervised learning
Work-Life Balance Is An Outdated Term: Pooja Goyal, Avishkaar
https://analyticsindiamag.com/ | Yesterday
Content: Companies across the world are embracing digitisation like nobodys business. This tectonic shift has opened up lot of job opportunities, especially technical roles. However, lack of gender diversity remains huge challenge at workplaces. Though women make up half of Indian population, the representation of women in technical roles at India Inc stood at 8230 The post Work Life Balance Is An Outdated Term Pooja Goyal, Avishkaar appeared first on Analytics India Magazine....

Keywords: machine learning, driverless car, ai
Supercomputer-Powered CRISPR Simulation Lights Path to Better DNA Editing
https://www.hpcwire.com/ | Today
Content: CRISPR Cas9 mostly just known as CRISPR is powerful genome editing tool that uses an enzyme Cas9 to slice off sections of DNA and guide RNA to repair and modify the DNA as desired, opening the door for cures to diseases like Huntingtons disease, sickle cell anemia, polycystic kidney disease and others. 8230 The post Supercomputer Powered CRISPR Simulation Lights Path to Better DNA Editing appeared first on HPCwire....

Keywords: computing, sampling, design, node
Top 8 Resources To Learn Self-Supervised Learning In 2021
https://analyticsindiamag.com/ | Today
Content: Did you know that almost 90 of the worlds data has been created in the last two years alone, and nearly 2.5 quintillion bytes of data are produced by humans every day 160 On social media platforms like Instagram, close to 95 million photos and videos are shared, while 500 million tweets are sent out every 8230 The post Top Resources To Learn Self Supervised Learning In 2021 appeared first on Analytics India Magazine....

Keywords: dimensionality reduction, clustering, statistic, mathematic
&#129504;&#129302; Edge#86: How DeepMind Prevents RL Agents from Getting "Too Clever"
https://thesequence.substack.com/ | Today
Content: What 8217 s New in AI, deep dive into one of the freshest research papers or technology frameworks that is worth your attention. Our goal is to keep you up to date with new developments in AI in way that complements the concepts we are debating in other editions of our newsletter....

Keywords: ai , framework, rl
Friend or foe? Machine learning and how it is shaping our lives
https://physicsworld.com/ | Yesterday
Content: Can robot write symphony Can robot take blank canvas and turn it into masterpiece So asks Will Smiths Detective Del Spooner of the artificial being Sonny, in memorable scene from the 2004 film I, Robot. Sonny, portrayed by Alan Tudyk, retorts with question of his own Can you This idea that we may have differing expectations of artificial intelligence than we do of our fellow human beings is recurring theme of ACitizens Guide to Artificial Intelligence, by John Zerilli, philo...

Keywords: machine learning, tpu, neural network
Fair Machine Learning Under Partial Compliance
https://arxiv.org | Yesterday
Content: Typically, fair machine learning research focuses on a single decisionmaker and assumes that the underlying population is stationary. However, many of the critical domains motivating this work are characterized by competitive marketplaces with many decisionmakers. Realistically, we might expect only a subset of them to adopt any non-compulsory fairness-conscious policy, a situation that political philosophers call partial compliance. This possibility raises important questions: how does the stra...

Keywords: machine learning
On Energy-Based Models with Overparametrized Shallow Neural Networks
https://arxiv.org | Yesterday
Content: Energy-based models (EBMs) are a simple yet powerful framework for generative modeling. They are based on a trainable energy function which defines an associated Gibbs measure, and they can be trained and sampled from via well-established statistical tools, such as MCMC. Neural networks may be used as energy function approximators, providing both a rich class of expressive models as well as a flexible device to incorporate data structure. In this work we focus on shallow neural networks. Buildin...

Keywords: network, supervised learning, neural network,
Attack-agnostic Adversarial Detection on Medical Data Using Explainable Machine Learning
https://arxiv.org | Yesterday
Content: Explainable machine learning has become increasingly prevalent, especially in healthcare where explainable models are vital for ethical and trusted automated decision making. Work on the susceptibility of deep learning models to adversarial attacks has shown the ease of designing samples to mislead a model into making incorrect predictions. In this work, we propose a model agnostic explainability-based method for the accurate detection of adversarial samples on two datasets with different comple...

Keywords: machine learning, deep learning, design,
Intermittency of CsPbBr$_3$ perovskite quantum dots analyzed by an unbiased statistical analysis
https://arxiv.org | Yesterday
Content: We analyze intermittency in intensity and fluorescence lifetime of CsPbBr$_3$ perovskite quantum dots by applying unbiased Bayesian inference analysis methods. We apply changepoint analysis (CPA) and a Bayesian state clustering algorithm to determine the timing of switching events and the number of states between which switching occurs in a statistically unbiased manner, which we have benchmarked particularly to apply to highly multistate emitters. We conclude that perovskite quantum dots displa...

Keywords: analysis, statistic, quant, clustering
Improved Surrogate Modeling of Fluid Dynamics with Physics-Informed Neural Networks
https://arxiv.org | Yesterday
Content: Physics-Informed Neural Networks (PINNs) have recently shown great promise as a way of incorporating physics-based domain knowledge, including fundamental governing equations, into neural network models for many complex engineering systems. They have been particularly effective in the area of inverse problems, where boundary conditions may be ill-defined, and data-absent scenarios, where typical supervised learning approaches will fail. Here, we further explore the use of this modeling methodolo...

Keywords: network, supervised learning, ios, neural
Functional portfolio optimization in stochastic portfolio theory
https://arxiv.org | Yesterday
Content: In this paper we develop a concrete and fully implementable approach to the optimization of functionally generated portfolios in stochastic portfolio theory. The main idea is to optimize over a family of rank-based portfolios parameterized by an exponentially concave function on the unit interval. This choice can be motivated by the long term stability of the capital distribution observed in large equity markets, and allows us to circumvent the curse of dimensionality. The resulting optimization...

Keywords: metric, ios
Understanding Long Range Memory Effects in Deep Neural Networks
https://arxiv.org | Yesterday
Content: \textit{Stochastic gradient descent} (SGD) is of fundamental importance in deep learning. Despite its simplicity, elucidating its efficacy remains challenging. Conventionally, the success of SGD is attributed to the \textit{stochastic gradient noise} (SGN) incurred in the training process. Based on this general consensus, SGD is frequently treated and analyzed as the Euler-Maruyama discretization of a \textit{stochastic differential equation} (SDE) driven by either Brownian or L\'evy stable moti...

Keywords: deep learning
Modelling and Simulating the Noisy Behaviour of Near-term Quantum Computers
https://arxiv.org | Yesterday
Content: Noise dominates every aspect of near-term quantum computers, rendering it exceedingly difficult to carry out even small computations. In this paper we are concerned with the modelling of noise in NISQ computers. We focus on three error groups that represent the main sources of noise during a computation and present quantum channels that model each source. We engineer a noise model that combines all three noise channels and simulates the evolution of the quantum computer using its calibrated erro...

Keywords: quant, genetic
Conditional Invertible Neural Networks for Diverse Image-to-Image Translation
https://arxiv.org | Yesterday
Content: We introduce a new architecture called a conditional invertible neural network (cINN), and use it to address the task of diverse image-to-image translation for natural images. This is not easily possible with existing INN models due to some fundamental limitations. The cINN combines the purely generative INN model with an unconstrained feed-forward network, which efficiently preprocesses the conditioning image into maximally informative features. All parameters of a cINN are jointly optimized wi...

Keywords: network, neural network
ScissionLite: Accelerating Distributed Deep Neural Networks Using Transfer Layer
https://arxiv.org | Yesterday
Content: Industrial Internet of Things (IIoT) applications can benefit from leveraging edge computing. For example, applications underpinned by deep neural networks (DNN) models can be sliced and distributed across the IIoT device and the edge of the network for improving the overall performance of inference and for enhancing privacy of the input data, such as industrial product images. However, low network performance between IIoT devices and the edge is often a bottleneck. In this study, we develop Sci...

Keywords: network, sampling, iot, internet of
Unobservable causal loops explain both the quantum computational speedup and quantum nonlocality
https://arxiv.org | Yesterday
Content: An unobservable form of retrocausality is at the center of this work. It emerges by legitimately time-symmetrizing the quantum description of the reversible process between two one-to-one correlated measurement outcomes. This leaves the quantum description unaltered but shows that it is the quantum superposition of unobservable time-symmetrization instances each of which hosts a causal loop: a quantum measurement that retrocausally changes the input state of the unitary transformation that led t...

Keywords: quant
Deep Neural Network for Musical Instrument Recognition using MFCCs
https://arxiv.org | Yesterday
Content: The task of efficient automatic music classification is of vital importance and forms the basis for various advanced applications of AI in the musical domain. Musical instrument recognition is the task of instrument identification by virtue of its audio. This audio, also termed as the sound vibrations are leveraged by the model to match with the instrument classes. In this paper, we use an artificial neural network (ANN) model that was trained to perform classification on twenty different classe...

Keywords: network, classification, neural network, ai
Aggregate Cyber-Risk Management in the IoT Age: Cautionary Statistics for (Re)Insurers and Likes
https://arxiv.org | Yesterday
Content: In this paper, we provide (i) a rigorous general theory to elicit conditions on (tail-dependent) heavy-tailed cyber-risk distributions under which a risk management firm might find it (non)sustainable to provide aggregate cyber-risk coverage services for smart societies, and (ii)a real-data driven numerical study to validate claims made in theory assuming boundedly rational cyber-risk managers, alongside providing ideas to boost markets that aggregate dependent cyber-risks with heavy-tails.To th...

Keywords: nan
AI-assisted super-resolution cosmological simulations
https://arxiv.org | Yesterday
Content: Cosmological simulations of galaxy formation are limited by finite computational resources. We draw from the ongoing rapid advances in Artificial Intelligence (specifically Deep Learning) to address this problem. Neural networks have been developed to learn from high-resolution (HR) image data, and then make accurate super-resolution (SR) versions of different low-resolution (LR) images. We apply such techniques to LR cosmological N-body simulations, generating SR versions. Specifically, we are ...

Keywords: artificial intelligence, network, r ,
GALA: Greedy ComputAtion for Linear Algebra in Privacy-Preserved Neural Networks
https://arxiv.org | Yesterday
Content: Machine Learning as a Service (MLaaS) is enabling a wide range of smart applications on end devices. However, privacy-preserved computation is still expensive. Our investigation has found that the most time-consuming component of the HE-based linear computation is a series of Permutation (Perm) operations that are imperative for dot product and convolution in privacy-preserved MLaaS. To this end, we propose GALA: Greedy computAtion for Linear Algebra in privacy-preserved neural networks, which v...

Keywords: network, neural network, design, coding,
A Note on Statistical Inference for Noisy Incomplete 1-Bit Matrix
https://arxiv.org | Yesterday
Content: We consider the statistical inference for noisy incomplete 1-bit matrix. Instead of observing a subset of real-valued entries of a matrix M, we only have one binary (1-bit) measurement for each entry in this subset, where the binary measurement follows a Bernoulli distribution whose success probability is determined by the value of the entry. Despite the importance of uncertainty quantification to matrix completion, most of the categorical matrix completion literature focus on point estimation a...

Keywords: analysis, sampling, quant, statistic, design
RIANN -- A Robust Neural Network Outperforms Attitude Estimation Filters
https://arxiv.org | Yesterday
Content: Inertial-sensor-based attitude estimation is a crucial technology in various applications, from human motion tracking to autonomous aerial and ground vehicles. Application scenarios differ in characteristics of the performed motion, presence of disturbances, and environmental conditions. Since state-of-the-art attitude estimators do not generalize well over these characteristics, their parameters must be tuned for the individual motion characteristics and circumstances. We propose RIANN, a real-...

Keywords: network, sampling, neural network, ios,
Video Salient Object Detection via Adaptive Local-Global Refinement
https://arxiv.org | Yesterday
Content: Video salient object detection (VSOD) is an important task in many vision applications. Reliable VSOD requires to simultaneously exploit the information from both the spatial domain and the temporal domain. Most of the existing algorithms merely utilize simple fusion strategies, such as addition and concatenation, to merge the information from different domains. Despite their simplicity, such fusion strategies may introduce feature redundancy, and also fail to fully exploit the relationship betw...

Keywords: algorithms, object detection, framework
Pervasive AI for IoT Applications: Resource-efficient Distributed Artificial Intelligence
https://arxiv.org | Yesterday
Content: Artificial intelligence (AI) has witnessed a substantial breakthrough in a variety of Internet of Things (IoT) applications and services, spanning from recommendation systems to robotics control and military surveillance. This is driven by the easier access to sensory data and the enormous scale of pervasive/ubiquitous devices that generate zettabytes (ZB) of real-time data streams. Designing accurate models using such data streams, to predict future insights and revolutionize the decision-takin...

Keywords: artificial intelligence, iot, internet of
Software Engineering for AI-Based Systems: A Survey
https://arxiv.org | Yesterday
Content: AI-based systems are software systems with functionalities enabled by at least one AI component (e.g., for image- and speech-recognition, and autonomous driving). AI-based systems are becoming pervasive in society due to advances in AI. However, there is limited synthesized knowledge on Software Engineering (SE) approaches for building, operating, and maintaining AI-based systems. To collect and analyze state-of-the-art knowledge about SE for AI-based systems, we conducted a systematic mapping s...

Keywords: ai , test
Envisioning Communities: A Participatory Approach Towards AI for Social Good
https://arxiv.org | Yesterday
Content: Research in artificial intelligence (AI) for social good presupposes some definition of social good, but potential definitions have been seldom suggested and never agreed upon. The normative question of what AI for social good research should be "for" is not thoughtfully elaborated, or is frequently addressed with a utilitarian outlook that prioritizes the needs of the majority over those who have been historically marginalized, brushing aside realities of injustice and inequity. We argue that A...

Keywords: artificial intelligence, framework, design, ai
Multi-scale Image Decomposition using a Local Statistical Edge Model
https://arxiv.org | Yesterday
Content: We present a progressive image decomposition method based on a novel non-linear filter named Sub-window Variance filter. Our method is specifically designed for image detail enhancement purpose; this application requires extraction of image details which are small in terms of both spatial and variation scales. We propose a local statistical edge model which develops its edge awareness using spatially defined image statistics. Our decomposition method is controlled by two intuitive parameters whi...

Keywords: statistic, design
Skilled and Mobile: Survey Evidence of AI Researchers' Immigration Preferences
https://arxiv.org | Yesterday
Content: Countries, companies, and universities are increasingly competing over top-tier artificial intelligence (AI) researchers. Where are these researchers likely to immigrate and what affects their immigration decisions? We conducted a survey $(n = 524)$ of the immigration preferences and motivations of researchers that had papers accepted at one of two prestigious AI conferences: the Conference on Neural Information Processing Systems (NeurIPS) and the International Conference on Machine Learning (I...

Keywords: artificial intelligence, machine learning, ai
Leveraging Machine Learning for Industrial Wireless Communications
https://arxiv.org | Yesterday
Content: Two main trends characterize today's communication landscape and are finding their way into industrial facilities: the rollout of 5G with its distinct support for vertical industries and the increasing success of machine learning (ML). The combination of those two technologies open the doors to many exciting industrial applications and its impact is expected to rapidly increase in the coming years, given the abundant data growth and the availability of powerful edge computers in production facil...

Keywords: machine learning, turing, ml
Ethics and Governance of Artificial Intelligence: Evidence from a Survey of Machine Learning Researchers
https://arxiv.org | Yesterday
Content: Machine learning (ML) and artificial intelligence (AI) researchers play an important role in the ethics and governance of AI, including taking action against what they perceive to be unethical uses of AI (Belfield, 2020; Van Noorden, 2020). Nevertheless, this influential group's attitudes are not well understood, which undermines our ability to discern consensuses or disagreements between AI/ML researchers. To examine these researchers' views, we conducted a survey of those who published in the ...

Keywords: artificial intelligence, ai , rust,
Software Engineering for Blockchain Based Software Systems: Foundations, Survey, and Future Directions
https://arxiv.org | Yesterday
Content: Many scientific and practical areas have shown increasing interest in reaping the benefits of blockchain technology to empower software systems. However, the unique characteristics and requirements associated with Blockchain Based Software (BBS) systems raise new challenges across the development lifecycle that entail an extensive improvement of conventional software engineering. This article presents a systematic literature review of the state-of-the-art in BBS engineering research from a softw...

Keywords: blockchain, foundation
Strategies for the Construction of Machine-Learning Potentials for Accurate and Efficient Atomic-Scale Simulations
https://arxiv.org | Yesterday
Content: Recent advances in machine-learning interatomic potentials have enabled the efficient modeling of complex atomistic systems with an accuracy that is comparable to that of conventional quantum mechanics based methods. At the same time, the construction of new machine-learning potentials can seem a daunting task, as it involves data-science techniques that are not yet common in chemistry and materials science. Here, we provide a tutorial-style overview of strategies and best practices for the cons...

Keywords: network, quant, r , neural
Motion Artifact Reduction in Quantitative Susceptibility Mapping using Deep Neural Network
https://arxiv.org | Yesterday
Content: An approach to reduce motion artifacts in Quantitative Susceptibility Mapping using deep learning is proposed. We use an affine motion model with randomly created motion profiles to simulate motion-corrupted QSM images. The simulated QSM image is paired with its motion-free reference to train a neural network using supervised learning. The trained network is tested on unseen simulated motion-corrupted QSM images, in healthy volunteers and in Parkinson's disease patients. The results show that mo...

Keywords: network, quant, supervised learning, neural
Casimir energy with chiral fermions on a quantum computer
https://arxiv.org | Yesterday
Content: In this paper we discuss the computation of Casimir energy on a quantum computer. The Casimir energy is an ideal quantity to calculate on a quantum computer as near term hybrid classical quantum algorithms exist to calculate the ground state energy and the Casimir energy gives physical implications for this quantity in a variety of settings. Depending on boundary conditions and whether the field is bosonic or fermionic we illustrate how the Casimir energy calculation can be set up on a quantum c...

Keywords: algorithms, qubit, quant, ionic
TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing
https://arxiv.org | Yesterday
Content: Various robustness evaluation methodologies from different perspectives have been proposed for different natural language processing (NLP) tasks. These methods have often focused on either universal or task-specific generalization capabilities. In this work, we propose a multilingual robustness evaluation platform for NLP tasks (TextFlint) that incorporates universal text transformation, task-specific transformation, adversarial attack, subpopulation, and their combinations to provide comprehens...

Keywords: analysis, natural language processing, nlp,
Reinforcement Learning for Scalable Logic Optimization with Graph Neural Networks
https://arxiv.org | Yesterday
Content: Logic optimization is an NP-hard problem commonly approached through hand-engineered heuristics. We propose to combine graph convolutional networks with reinforcement learning and a novel, scalable node embedding method to learn which local transforms should be applied to the logic graph. We show that this method achieves a similar size reduction as ABC on smaller circuits and outperforms it by 1.5-1.75x on larger random graphs....

Keywords: network, node, reinforcement learning, scala
A Python toolbox for unbiased statistical analysis of fluorescence intermittency of multi-level emitters
https://arxiv.org | Yesterday
Content: We report on a Python-toolbox for unbiased statistical analysis of fluorescence intermittency properties of single emitters. Intermittency, i.e., step-wise temporal variations in the instantaneous emission intensity and fluorescence decay rate properties are common to organic fluorophores, II-VI quantum dots and perovskite quantum dots alike. Unbiased statistical analysis of intermittency switching time distributions, involved levels and lifetimes is important to avoid interpretation artefacts. ...

Keywords: analysis, quant, clustering, statistic, python
Two-layer neural networks with values in a Banach space
https://arxiv.org | Yesterday
Content: We study two-layer neural networks whose domain and range are Banach spaces with separable preduals. In addition, we assume that the image space is equipped with a partial order, i.e. it is a Riesz space. As the nonlinearity we choose the lattice operation of taking the positive part; in case of $\mathbb R^d$-valued neural networks this corresponds to the ReLU activation function. We prove inverse and direct approximation theorems with Monte-Carlo rates, extending existing results for the finite...

Keywords: network, neural network
The Araucaria Project. Distances to Nine Galaxies Based on a Statistical Analysis of their Carbon Stars (JAGB Method)
https://arxiv.org | Yesterday
Content: Our work presents an independent calibration of the J-region Asymptotic Giant Branch (JAGB) method using Infrared Survey Facility (IRSF) photometric data and a custom luminosity function profile to determine JAGB mean magnitudes for nine galaxies. We determine a mean absolute magnitude of carbon stars of $M_{LMC}=-6.212 \pm 0.010$ (stat.) $\pm 0.030$ (syst.) mag. We then use near-infrared photometry of a number of nearby galaxies, originally obtained by our group to determine their distances fro...

Keywords: metric
Choosing among notions of multivariate depth statistics
https://arxiv.org | Yesterday
Content: Classical multivariate statistics measures the outlyingness of a point by its Mahalanobis distance from the mean, which is based on the mean and the covariance matrix of the data. A multivariate depth function is a function which, given a point and a distribution in d-space, measures centrality by a number between 0 and 1, while satisfying certain postulates regarding invariance, monotonicity, convexity and continuity. Accordingly, numerous notions of multivariate depth have been proposed in the...

Keywords: algorithms, statistic
On The Problem of Relevance in Statistical Inference
https://arxiv.org | Yesterday
Content: This paper is dedicated to the "50 Years of the Relevance Problem" - a long-neglected topic that begs attention from practical statisticians who are concerned with the problem of drawing inference from large-scale heterogeneous data....

Keywords: statistic
Non-Autoregressive vs Autoregressive Neural Networks for System Identification
https://arxiv.org | Yesterday
Content: The application of neural networks to non-linear dynamic system identification tasks has a long history, which consists mostly of autoregressive approaches. Autoregression, the usage of the model outputs of previous time steps, is a method of transferring a system state between time steps, which is not necessary for modeling dynamic systems with modern neural network structures, such as gated recurrent units (GRUs) and Temporal Convolutional Networks (TCNs). We compare the accuracy and execution...

Keywords: network, regression, tpu, neural network
Software Engineering for Internet of Things: The Practitioner's Perspective
https://arxiv.org | Yesterday
Content: Internet of Things based systems (IoT systems for short) are becoming increasingly popular across different industrial domains and their development is rapidly increasing to provide value-added services to end-users and citizens. Little research to date uncovers the core development process lifecycle needed for IoT systems, and thus software engineers find themselves unprepared and unfamiliar with this new genre of system development. To ameliorate this gap, we conducted a mixed quantitative and...

Keywords: framework, iot, quant
Boundary-Aware 3D Object Detection from Point Clouds
https://arxiv.org | Yesterday
Content: Currently, existing state-of-the-art 3D object detectors are in two-stage paradigm. These methods typically comprise two steps: 1) Utilize region proposal network to propose a fraction of high-quality proposals in a bottom-up fashion. 2) Resize and pool the semantic features from the proposed regions to summarize RoI-wise representations for further refinement. Note that these RoI-wise representations in step 2) are considered individually as an uncorrelated entry when fed to following detection...

Keywords: network, object detection, r
Improving Object Detection in Art Images Using Only Style Transfer
https://arxiv.org | Yesterday
Content: Despite recent advances in object detection using deep learning neural networks, these neural networks still struggle to identify objects in art images such as paintings and drawings. This challenge is known as the cross depiction problem and it stems in part from the tendency of neural networks to prioritize identification of an object's texture over its shape. In this paper we propose and evaluate a process for training neural networks to localize objects - specifically people - in art images....

Keywords: network, neural network, object detection,
NeuroXplorer 1.0: An Extensible Framework for Architectural Exploration with Spiking Neural Networks
https://arxiv.org | Yesterday
Content: Recently, both industry and academia have proposed many different neuromorphic architectures to execute applications that are designed with Spiking Neural Network (SNN). Consequently, there is a growing need for an extensible simulation framework that can perform architectural explorations with SNNs, including both platform-based design of today's hardware, and hardware-software co-design and design-technology co-optimization of the future. We present NeuroXplorer, a fast and extensible framewor...

Keywords: analysis, network, euromorphic, metric, neural
Title
Think outside the black box
https://towardsdatascience.com/ | Today

Keywords: artificial intelligence, game, machine learning

Advancing Excel as a programming language with Andy Gordon and Simon Peyton Jones
https://www.microsoft.com/ | Yesterday

Keywords: scala, java, security, tpu, c

Quantpedia in April 2021
https://quantpedia.com/ | Yesterday

Keywords: quant, analysis, database, test

Guide to Googles STAC: An SSL Framework For Object Detection
https://analyticsindiamag.com/ | Today

Keywords: tensorflow, git , supervised learning

Work-Life Balance Is An Outdated Term: Pooja Goyal, Avishkaar
https://analyticsindiamag.com/ | Yesterday

Keywords: machine learning, driverless car, ai

Supercomputer-Powered CRISPR Simulation Lights Path to Better DNA Editing
https://www.hpcwire.com/ | Today

Keywords: computing, sampling, design, node

Top 8 Resources To Learn Self-Supervised Learning In 2021
https://analyticsindiamag.com/ | Today

Keywords: dimensionality reduction, clustering, statistic, mathematic

&#129504;&#129302; Edge#86: How DeepMind Prevents RL Agents from Getting "Too Clever"
https://thesequence.substack.com/ | Today

Keywords: ai , framework, rl

Friend or foe? Machine learning and how it is shaping our lives
https://physicsworld.com/ | Yesterday

Keywords: machine learning, tpu, neural network

Fair Machine Learning Under Partial Compliance
https://arxiv.org | Yesterday

Keywords: machine learning

On Energy-Based Models with Overparametrized Shallow Neural Networks
https://arxiv.org | Yesterday

Keywords: network, supervised learning, neural network,

Attack-agnostic Adversarial Detection on Medical Data Using Explainable Machine Learning
https://arxiv.org | Yesterday

Keywords: machine learning, deep learning, design,

Intermittency of CsPbBr$_3$ perovskite quantum dots analyzed by an unbiased statistical analysis
https://arxiv.org | Yesterday

Keywords: analysis, statistic, quant, clustering

Improved Surrogate Modeling of Fluid Dynamics with Physics-Informed Neural Networks
https://arxiv.org | Yesterday

Keywords: network, supervised learning, ios, neural

Functional portfolio optimization in stochastic portfolio theory
https://arxiv.org | Yesterday

Keywords: metric, ios

Understanding Long Range Memory Effects in Deep Neural Networks
https://arxiv.org | Yesterday

Keywords: deep learning

Modelling and Simulating the Noisy Behaviour of Near-term Quantum Computers
https://arxiv.org | Yesterday

Keywords: quant, genetic

Conditional Invertible Neural Networks for Diverse Image-to-Image Translation
https://arxiv.org | Yesterday

Keywords: network, neural network

ScissionLite: Accelerating Distributed Deep Neural Networks Using Transfer Layer
https://arxiv.org | Yesterday

Keywords: network, sampling, iot, internet of

Unobservable causal loops explain both the quantum computational speedup and quantum nonlocality
https://arxiv.org | Yesterday

Keywords: quant

Deep Neural Network for Musical Instrument Recognition using MFCCs
https://arxiv.org | Yesterday

Keywords: network, classification, neural network, ai

Aggregate Cyber-Risk Management in the IoT Age: Cautionary Statistics for (Re)Insurers and Likes
https://arxiv.org | Yesterday

Keywords: nan

AI-assisted super-resolution cosmological simulations
https://arxiv.org | Yesterday

Keywords: artificial intelligence, network, r ,

GALA: Greedy ComputAtion for Linear Algebra in Privacy-Preserved Neural Networks
https://arxiv.org | Yesterday

Keywords: network, neural network, design, coding,

A Note on Statistical Inference for Noisy Incomplete 1-Bit Matrix
https://arxiv.org | Yesterday

Keywords: analysis, sampling, quant, statistic, design

RIANN -- A Robust Neural Network Outperforms Attitude Estimation Filters
https://arxiv.org | Yesterday

Keywords: network, sampling, neural network, ios,

Video Salient Object Detection via Adaptive Local-Global Refinement
https://arxiv.org | Yesterday

Keywords: algorithms, object detection, framework

Pervasive AI for IoT Applications: Resource-efficient Distributed Artificial Intelligence
https://arxiv.org | Yesterday

Keywords: artificial intelligence, iot, internet of

Software Engineering for AI-Based Systems: A Survey
https://arxiv.org | Yesterday

Keywords: ai , test

Envisioning Communities: A Participatory Approach Towards AI for Social Good
https://arxiv.org | Yesterday

Keywords: artificial intelligence, framework, design, ai

Multi-scale Image Decomposition using a Local Statistical Edge Model
https://arxiv.org | Yesterday

Keywords: statistic, design

Skilled and Mobile: Survey Evidence of AI Researchers' Immigration Preferences
https://arxiv.org | Yesterday

Keywords: artificial intelligence, machine learning, ai

Leveraging Machine Learning for Industrial Wireless Communications
https://arxiv.org | Yesterday

Keywords: machine learning, turing, ml

Ethics and Governance of Artificial Intelligence: Evidence from a Survey of Machine Learning Researchers
https://arxiv.org | Yesterday

Keywords: artificial intelligence, ai , rust,

Software Engineering for Blockchain Based Software Systems: Foundations, Survey, and Future Directions
https://arxiv.org | Yesterday

Keywords: blockchain, foundation

Strategies for the Construction of Machine-Learning Potentials for Accurate and Efficient Atomic-Scale Simulations
https://arxiv.org | Yesterday

Keywords: network, quant, r , neural

Motion Artifact Reduction in Quantitative Susceptibility Mapping using Deep Neural Network
https://arxiv.org | Yesterday

Keywords: network, quant, supervised learning, neural

Casimir energy with chiral fermions on a quantum computer
https://arxiv.org | Yesterday

Keywords: algorithms, qubit, quant, ionic

TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing
https://arxiv.org | Yesterday

Keywords: analysis, natural language processing, nlp,

Reinforcement Learning for Scalable Logic Optimization with Graph Neural Networks
https://arxiv.org | Yesterday

Keywords: network, node, reinforcement learning, scala

A Python toolbox for unbiased statistical analysis of fluorescence intermittency of multi-level emitters
https://arxiv.org | Yesterday

Keywords: analysis, quant, clustering, statistic, python

Two-layer neural networks with values in a Banach space
https://arxiv.org | Yesterday

Keywords: network, neural network

The Araucaria Project. Distances to Nine Galaxies Based on a Statistical Analysis of their Carbon Stars (JAGB Method)
https://arxiv.org | Yesterday

Keywords: metric

Choosing among notions of multivariate depth statistics
https://arxiv.org | Yesterday

Keywords: algorithms, statistic

On The Problem of Relevance in Statistical Inference
https://arxiv.org | Yesterday

Keywords: statistic

Non-Autoregressive vs Autoregressive Neural Networks for System Identification
https://arxiv.org | Yesterday

Keywords: network, regression, tpu, neural network

Software Engineering for Internet of Things: The Practitioner's Perspective
https://arxiv.org | Yesterday

Keywords: framework, iot, quant

Boundary-Aware 3D Object Detection from Point Clouds
https://arxiv.org | Yesterday

Keywords: network, object detection, r

Improving Object Detection in Art Images Using Only Style Transfer
https://arxiv.org | Yesterday

Keywords: network, neural network, object detection,

NeuroXplorer 1.0: An Extensible Framework for Architectural Exploration with Spiking Neural Networks
https://arxiv.org | Yesterday

Keywords: analysis, network, euromorphic, metric, neural

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