Automates UI implementation from designer images.
problem Automating UI implementation from designer images.
method Generative model training and imitation learning.
result 92.5% accuracy on Android Button attribute inference.
On-device research index
arXiv research
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168,742 papers · 148 categories
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48 results for “UI automation”
DS-UI improves DNN uncertainty inference by combining a DNN classifier with MoGMM.
problem Improving uncertainty inference in DNN-based image recognition.
method Combines DNN classifier with MoGMM for probabilistic interpretation of features.
result DS-UI outperforms state-of-the-art UI methods in misclassification detection.
Let I be a symmetrically-normed ideal of the space of bounded operators acting on a Hilbert space H. Let be a family of mutually orthogonal projections on H. The pinching operator associated with the former family of projections is given by P: I --> I, P(x)=\sum_{i=1}^{w} p_i x p_i.…
The democratization of machine learning (ML) has led to ML-based machine vision systems for autonomous driving, traffic monitoring, and video surveillance. However, true democratization cannot be achieved without greatly simplifying the process of collecting groundtruth for training and testing these systems. This grou…
In this paper, we study local solutions F=(F1,..,Fn) of a general functional equation of the form F1(U1(x,y))+....+Fn(Un(x,y))=0. A such equation will be called an ``abelian functional equation'' (Afe). We will restrict ourselves to the case when the inner functions Ui's are real rational functions. First we prove that…
No-regret optimization for time-varying functions using uncertainty injection.
problem Optimizing time-varying functions with no-regret in bandit feedback.
method W-SparQ-GP-UCB, incorporating uncertainty injection and additional queries.
result Achieves no-regret with a vanishing number of additional queries per iteration.
Critical task and cognition-based environments, such as in military and defense operations, aviation user-technology interaction evaluation on UI, understanding intuitiveness of a hardware model or software toolkit, etc. require an assessment of how much a particular task is generating mental workload on a user. This i…
In this paper, we propose a fully supervised speaker diarization approach, named unbounded interleaved-state recurrent neural networks (UIS-RNN). Given extracted speaker-discriminative embeddings (a.k.a. d-vectors) from input utterances, each individual speaker is modeled by a parameter-sharing RNN, while the RNN state…
Unified flow solves Christoffel-Minkowski problem for .
problem Solving the Christoffel-Minkowski problem for .
method Anisotropic expanding flow of smooth hypersurfaces with speed .
result The flow converges to a solution of the Christoffel-Minkowski problem.
For , we give two proofs of the fact that the \emph{Birman exact sequence} for the Torelli group \[ 1\to π_1(S_g)\to {\cal I}_{g,1}\to {\cal I}_g\to 1 \] does not split. This result was claimed by G. Mess in \cite{mess1990unit}, but his proof has a critical and unrepairable error which will be discussed in the int…
New algorithm improves matrix estimation with one-sided covariates.
problem Estimating matrix means with unobserved row covariates.
method Proposes an algorithm for nonparametric matrix estimation with observed column covariates.
result Achieves minimax optimal nonparametric rate in moderately proportioned matrices.
Automated theorem prover proves non-orderability of groups.
problem Proving non-orderability of groups.
method Generic automated theorem prover with tools like positive cones, torsions, generalised torsions, and cofinal elements.
result Demonstrated automated proof of non-orderability of groups.
In this paper, we present a data science automation system called Prediction Factory. The system uses several key automation algorithms to enable data scientists to rapidly develop predictive models and share them with domain experts. To assess the system's impact, we implemented 3 different interfaces for creating pre…
With an abundance of research papers in deep learning, reproducibility or adoption of the existing works becomes a challenge. This is due to the lack of open source implementations provided by the authors. Further, re-implementing research papers in a different library is a daunting task. To address these challenges, w…
This paper surveys scalable automated alignment methods for LLMs.
problem Scalability issues in traditional human-annotated alignment methods for LLMs.
method Categorizes and discusses various automated alignment methods.
result Emerging automated alignment methods are effective and scalable.
Automation engineering is the task of integrating, via software, various sensors, actuators, and controls for automating a real-world process. Today, automation engineering is supported by a suite of software tools including integrated development environments (IDE), hardware configurators, compilers, and runtimes. The…
Automation of machine learning model development is increasingly becoming an established research area. While automated model selection and automated data pre-processing have been studied in depth, there is, however, a gap concerning automated model adaptation strategies when multiple strategies are available. Manually…
New automated market makers for multi-asset trading.
problem Liquidity management in multi-asset trading.
method Derived from self-financing transactions and rebalancing principles.
result Constant product market maker as a special case.
While artificial intelligence (AI) and other automation technologies might lead to enormous progress in healthcare, they may also have undesired consequences for people working in the field. In this interdisciplinary study, we capture empirical evidence of not only what healthcare work could be automated, but also what…
DriveML automates machine learning tasks in R.
problem Manual machine learning tasks are time-consuming and error-prone.
method Automated data preparation, feature engineering, model building, and model explanation.
result DriveML performs best across different parameters compared to other packages.
Contributions of recent deep-neural-network (DNN) based techniques have been playing a significant role in human-computer interaction (HCI) and user interface (UI) domains. One of the commonly used DNNs is human pose estimation. This kind of technique is widely used for motion capturing of humans, and to generate or mo…
Always-on sensing of mobile device user's contextual information is critical to many intelligent use cases nowadays such as healthcare, drive assistance, voice UI. State-of-the-art approaches for predicting user context have proved the value to leverage multiple sensing modalities for better accuracy. However, those co…
This research compiles knowledge on decentralized exchanges with AMM protocols.
problem Improving and developing AMM-based decentralized exchanges.
method Established a general AMM framework, compared mechanics, discussed security and privacy.
result Illustrated conservation and slippage functions of AMM protocols.
Fast, fully-automated histograms for large data sets.
problem Efficiently constructing histograms for large-scale data.
method G-Enum histograms using Minimum Description Length (MDL) principle for model selection and a greedy search heuristic.
result Construct histograms in linearithmic time, significantly faster than previous methods.
Automated machine learning (AutoML) aims to find optimal machine learning solutions automatically given a machine learning problem. It could release the burden of data scientists from the multifarious manual tuning process and enable the access of domain experts to the off-the-shelf machine learning solutions without e…
Automates infectious disease policy-making via inference in epidemiological models.
problem Improving policy-making for infectious diseases during pandemics.
method Performing inference in existing epidemiological models using a probabilistic programming language.
result Automated inference leads to better disease progression outcomes and policy prescriptions.
Automated market-making for CBDCs and stable coins on blockchain.
problem Creating fair exchange rates for digital assets on blockchain.
method Developed an innovative approach for generating fair exchange rates.
result Illustrated the approach's efficacy on G-10 currency exchange rates.
New formula identifies and quantifies costs for automated market makers.
problem Adverse selection costs faced by liquidity providers in automated market makers.
method Derives a Black-Scholes-like formula for AMMs and identifies loss-versus-rebalancing cost.
result Closed-form expressions for LVR applicable to all automated market makers.
GPT-f uses language models to find new proofs in formal math.
problem Generating original mathematical terms for automated theorem proving.
method Transformer-based language model for automated theorem proving and proof assistant.
result GPT-f contributed new proofs to the Metamath library.
Efficient Connected and Automated Driving System with Multi-agent Graph Reinforcement Learningstat.ML
This paper improves transportation efficiency by teaching automated vehicles to cooperate.
problem Improving efficiency and safety of transportation systems with automated vehicles.
method Multi-agent graph reinforcement learning with attention mechanism.
result Automated vehicles can achieve better performance when learning to cooperate with each other.
Evidence suggests Rapid-Eye-Movement (REM) Sleep Behaviour Disorder (RBD) is an early predictor of Parkinson's disease. This study proposes a fully-automated framework for RBD detection consisting of automated sleep staging followed by RBD identification. Analysis was assessed using a limited polysomnography montage fr…
New metric to measure liquidity position PNL, delta hedging algorithm for automated market makers.
problem Vulnerability of liquidity positions to price changes in underlying assets.
method Proposes a new metric for measuring PNL, delta hedging algorithm for various AMMs.
result New metric more accurately measures net value change due to price movement.
The increasing availability of large but noisy data sets with a large number of heterogeneous variables leads to the increasing interest in the automation of common tasks for data analysis. The most time-consuming part of this process is the Exploratory Data Analysis, crucial for better domain understanding, data clean…
Paper proposes efficient cost functions for automated market makers in DeFi.
problem Inefficient and computationally complex cost functions in DeFi.
method Proposes and analyzes constant circle/ellipse based cost functions.
result Proposed cost functions are computationally efficient and robust against attacks.
Multi-task learning (MTL) has recently contributed to learning better representations in service of various NLP tasks. MTL aims at improving the performance of a primary task, by jointly training on a secondary task. This paper introduces automated tasks, which exploit the sequential nature of the input data, as second…
Fake engagement is one of the significant problems in Online Social Networks (OSNs) which is used to increase the popularity of an account in an inorganic manner. The detection of fake engagement is crucial because it leads to loss of money for businesses, wrong audience targeting in advertising, wrong product predicti…
Automated denoising score matching handles nonlinear diffusion processes.
problem Nonlinear diffusion processes limit generative modeling and property estimation.
method Local-DSM using local increments and Taylor expansions.
result Tractable training and score estimation for nonlinear diffusion processes.
Review of automation's role in chemical discoveries.
problem Improving autonomous discovery in chemistry.
method Classification of discovery types, assessment of autonomy, case studies.
result Rapid advancements in automation and machine learning are transforming experimentation and modeling.
Optimizes classifiers for varying levels of automation.
problem Supervised learning models often perform worse than human experts on specific instances.
method Focuses on convex margin-based classifiers, showing the problem is NP-hard. For SVMs, the objective function is decomposed into monotone and modular components, allowing efficient algorithms to solve the problem.
result The approach demonstrates that classifiers optimized for varying levels of automation can outperform full automation and human-only models.
We analyze impermanent loss in AMMs and show G3Ms are simplest.
problem Understanding impermanent loss in automated market makers.
method Developed a general framework and analyzed Geometric Mean Market Makers (G3Ms).
result G3Ms have the simplest impermanent loss characteristics.
Traditional market makers are losing their importance as automated systems have largely assumed the role of liquidity provision in markets. We update the model of Glosten and Milgrom (1985) to analyze this new world: we add multiple securities and introduce an automated market maker who uses the relationships between s…
Automated framework forecasts correlated time series in minutes.
problem Forecasting correlated time series with high accuracy and efficiency.
method Data-driven iterative pruning, zero-shot search, fast parameter adaptation.
result Framework achieves state-of-the-art accuracy and is faster than existing methods.
Smart Close-out Nettingq-fin.RM
Smart Close-out Netting aims to automate close-out netting processes.
problem Inefficiencies in close-out netting processes for financial institutions.
method Standardisation and automation of legal and regulatory processes using a data-driven framework and controlled natural language.
result Standardisation and automation can improve close-out netting processes for prudentially regulated financial institutions.
Automated testing improves deep learning model accuracy by 259.2%.
problem Ensuring robustness of deep learning models through automated testing.
method Jointly optimizing differential behaviors and neuron coverage; generating corner-cases; applying transformations and GANs.
result Deep learning model accuracy increased by 259.2% using automated generated corner cases.
Deep-learning CNN automates Cu alloy grain size evaluation.
problem Automating the evaluation of Cu alloy grain size to reduce labor and improve accuracy.
method Deep-learning convolutional neural network (CNN) for automated image acquisition and processing.
result Achieved 91.1% classification accuracy on Cu alloy sub-images.
Review of automation's role in chemical discovery, emphasizing future challenges.
problem Improving automation's contribution to chemical discovery.
method Analysis of exemplary studies and open research directions.
result Future autonomous systems need improvement in data handling, model building, and experiment automation.
Adversarial validation detects concept drift in user targeting systems.
problem Concept drift in user targeting automation systems deteriorates model performance over time.
method Adversarial validation approach to detect and adapt to concept drift.
result Adversarial validation effectively addresses concept drift in user targeting systems.
New models optimize quotes for automated market makers considering various price dynamics and demand variability.
problem Optimizing quotes for automated market makers in volatile price environments.
method Advanced models incorporating stochastic volatility, jumps, Hawkes processes, and Markov-modulated Poisson processes.
result Optimal quotes can be computed using numerical methods tailored to each model.