PromID predicts human promoter positions with high accuracy.
problem Difficulties in identifying human promoter sequences.
method Deep learning approach predicting exact TSS positions.
result Significantly reduces false positive predictions.
On-device research index
arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
169,051 papers · 148 categories
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48 results for “TSS identification”
A new Twitter sentiment model predicts stock market trends with high accuracy.
problem Real-time prediction of future stock market prices.
method Baseline correlation approach using polynomial regression, classification, and lexicon-based sentiment analysis.
result Predicts stock market trends with 67.22% accuracy, up to 15 time samples in advance.
Unified framework TSS certifies robustness against semantic transformations.
problem Certifying robustness of ML models against semantic transformations.
method Unified framework TSS categorizes transformations into resolvable and differentially resolvable, proposing randomized smoothing and stratified sampling strategies.
result Significantly outperforms state of the art on over ten types of semantic transformations.
Paper analyzes electricity price and demand TSs using decomposition to detect cyber-attacks.
problem Detecting cyber-attacks in electricity price and demand time series data.
method Performed time series decomposition using additive and multiplicative methods, tested error term for patterns.
result Found a chance of cyber-attacks in the error term of decomposed TSs.
Enhances solar flare prediction with advanced preprocessing and contrastive learning.
problem Accurate prediction of solar flares to mitigate risks to astronauts and equipment.
method Advanced data preprocessing pipeline and contrastive learning with GRU regression model.
result Exceptional True Skill Statistic (TSS) scores, surpassing previous methods.
We present a general regularization-based framework for Multi-task learning (MTL), in which the similarity between tasks can be learned or refined using -norm Multiple Kernel learning (MKL). Based on this very general formulation (including a general loss function), we derive the corresponding dual formulation …
Paper explores using EEG for better speaker identification, even in noisy environments.
problem Speaker identification performance degrades in background noise.
method Uses EEG signals to enhance speaker identification systems, comparing with acoustic features.
result Speaker identification system using only EEG features outperforms one using only acoustic features in high background noise.
In the present paper we study interval identification systems of order three. We prove that the Rauzy induction preserves symmetry: for any symmetric interval identification system of order three after finitely many iterations of the Rauzy induction we always obtain a symmetric system. We also provide an example of sym…
Improved driver identification accuracy using steering wheel data.
problem Accurately identifying drivers based on naturalistic driving behavior.
method Novel approach for window length parameter design, leveraging GRUs neural network.
result Increased driver identification accuracy from under 15% to over 65%.
Simplified identification methods for causal inference with arbitrary interventional distributions.
problem Estimating cause-effect relationships from data with experimental interventions.
method Using Single World Intervention Graphs and nested model factorization, we provide algorithms for identifying causal parameters from mixed observational and interventional distributions.
result Our algorithms are complete for certain types of interventional marginal distributions.
Cyclic coordinate descent identifies models in finite time and converges linearly.
problem Model identification in composite nonsmooth optimization problems.
method Cyclic coordinate descent for a wide class of functions.
result Explicit local linear convergence rates for coordinate descent.
WiPIN uses Wi-Fi signals to identify people without requiring them to walk.
problem Identification requires walking and is unreliable with many users.
method Extracts body information from Wi-Fi signals without user movement.
result Achieves 92% accuracy with 30 users, robust to various settings.
Study on identifying and inferring nonlinear dynamics on unknown networks.
problem Identifying network structure in nonlinear dynamic systems with unknown interactions.
method Showed network structure is not generically identified, requiring sufficient spectral heterogeneity. Developed necessary and sufficient conditions for identification and proposed a semiparametric estimator.
result Necessary and sufficient conditions for identification of network structure in nonlinear dynamic systems.
New findings on complexity limits in fixed budget bandit identification.
problem Determining the best possible error rate for fixed budget bandit identification.
method Analyzing the best non-adaptive sampling procedures and showing the existence of complexities.
result No fixed complexity for certain bandit identification tasks.
Deep learning's convolutional networks improve system identification.
problem Nonlinear system identification problems.
method Exploration of relationships between TCN and Volterra series/block-oriented models.
result TCN outperforms traditional models in sequence modeling tasks.
Review of automatic de-identification systems for EHR, highlighting challenges beyond accuracy.
problem Challenges in surrogate generation and patient privacy in de-identification of EHR.
method Comprehensive review of 18 recently published systems, focusing on accuracy and challenges.
result Despite accuracy improvements, challenges remain in surrogate generation and patient privacy.
Bayesian methods reduce variance in subspace identification for small data sets.
problem High variance in traditional subspace identification methods for large models or small sample sizes.
method Investigation of Bayesian estimation solutions (regularized and shrinkage estimators) for subspace identification.
result Bayesian estimators reduce estimation risk by up to 40% compared to traditional methods.
Equation discovery method reconstructs model structure and parameters from data.
problem Nonlinear system identification challenges.
method Two interlaced parts: model structure identification and parameter estimation.
result Equation discovery method successfully reconstructs model structure and parameters from data.
A tutorial on non-asymptotic system identification methods.
problem Identifying system parameters in linear models.
method Covering technique, Hanson-Wright Inequality, method of self-normalized martingales.
result Streamlined proofs of least-squares based estimator performance.
A new algorithm identifies one of several nearly optimal arms in linear bandits.
problem Identifying one arm that is close to the best arm in linear bandits.
method Developed a procedure to adapt best-arm identification algorithms for -best-answer identification in transductive linear stochastic bandits.
result Proposed an asymptotically optimal algorithm for -best-answer identification.
Online algorithm identifies PDEs from noisy data snapshots.
problem Identifying PDEs from sequential solution snapshots.
method Combines weak-form discretization with online proximal gradient descent.
result Efficiently identifies and tracks systems with time-varying coefficients.
A distributed system identification method for LTI systems using reverse experience replay.
problem Online system identification of LTI systems over multi-agent networks.
method DSGD-RER, a distributed variant of SGD-RER with backward updates.
result The estimation error decreases as the network size grows.
Tutorial on using concentration inequalities for linear system identification.
problem Learning state-space parameters of linear systems.
method Large-deviations and self-normalized martingales.
result Data-dependent and independent bounds on learning rate.
dynoGP uses deep Gaussian processes for dynamic system identification.
problem System identification for complex dynamical systems.
method Interconnecting linear dynamic GPs and static GPs to model dynamic and static nonlinearities.
result Demonstrates effectiveness of the approach using both simulated and real-world data.
Proposes SPCA to incorporate structural constraints in model identification.
problem Model identification with partial structural knowledge.
method Structural Principal Component Analysis (SPCA) that leverages structural information.
result Demonstrates improved model estimates using synthetic and industrial data.
ADSGD method speeds up model identification in sparse optimization.
problem Implicit model identification in sparse optimization problems.
method Accelerated Doubly Stochastic Gradient Method (ADSGD) for faster explicit model identification.
result ADSGD achieves faster explicit model identification and improved algorithm efficiency.
Study shows neural networks outperform traditional methods in speaker identification.
problem Open-set speaker identification with large populations.
method Discriminative neural networks compared to Gaussian mixture models.
result Multi-class neural networks outperform traditional methods for large speaker populations.
Probabilistic Numeric SMC Sampling for Bayesian Nonlinear System Identification in Continuous Timestat.ML
This paper tackles Bayesian system identification with probabilistic numerical methods.
problem Accurately modeling nonlinear dynamic systems from noisy data.
method Probabilistic Sequential Monte Carlo (SMC) combined with probabilistic numerical integration.
result Efficient identification of latent states and system parameters from noisy measurements.
Study measures uncertainty in MST identification across different correlation networks.
problem Uncertainty in MST identification across various correlation-based market networks.
method Developed a framework using random variable networks (RVN) to measure uncertainty of MST identification.
result FDR is the most appropriate measure for MST identification reliability.
Paper improves phase identification in power systems using information theory.
problem Improving supervised learning accuracy in phase identification.
method Developed two new techniques based on information theory.
result Significant improvement in phase identification accuracy (e.g., from 51.7% to 97.3%).
Optimal best-arm identification in linear bandits reduces sampling budget.
problem Identifying the best arm with fixed confidence in stochastic linear bandits.
method A simple algorithm that tracks an optimal proportion of arm draws, updated as rarely as desired.
result The algorithm's sampling complexity matches known lower bounds, asymptotically almost surely and in expectation.
Objective: Patient notes in electronic health records (EHRs) may contain critical information for medical investigations. However, the vast majority of medical investigators can only access de-identified notes, in order to protect the confidentiality of patients. In the United States, the Health Insurance Portability a…
Patient notes contain a wealth of information of potentially great interest to medical investigators. However, to protect patients' privacy, Protected Health Information (PHI) must be removed from the patient notes before they can be legally released, a process known as patient note de-identification. The main objectiv…
Comment on deconfounder method, highlighting causal identification issues and risks of parametric assumptions.
problem Causal identification issues in settings where deconfounder method is suggested.
method Summarizing arguments against causal identification, cautioning on parametric assumptions, and discussing nonparametric identification results.
result Nonparametric identification results may be more interesting for detecting causal identification from observed data.
This research improves deep neural networks for parameter identification and prediction in stochastic Volterra integral equations.
problem Parameter identification and prediction in Volterra integral equations driven by Gaussian noise.
method Improved deep neural networks framework that incorporates inter-output relationships into the loss function.
result The framework enhances parameter estimation accuracy and provides accurate solutions for modeling stochastic systems.
Improved language identification with tuplemax loss.
problem Language identification with user-specified small sets of languages.
method Replaced softmax loss with tuplemax loss.
result 2.33% error rate improvement over softmax loss.
Particle identification in ground-based gamma-ray astronomy using convolutional neural networksastro-ph.IM
CNNs improve particle identification in ground-based gamma-ray astronomy.
problem Identifying particles in gamma-ray astronomy images.
method Used convolutional neural networks (CNNs) with PyTorch and TensorFlow.
result Improved accuracy in identifying gamma-rays and background particles.
Modeling dynamical systems is important in many disciplines, e.g., control, robotics, or neurotechnology. Commonly the state of these systems is not directly observed, but only available through noisy and potentially high-dimensional observations. In these cases, system identification, i.e., finding the measurement map…
Efficiently identifies best policies in tabular MDPs with reduced computational cost.
problem Identifying the best policy in tabular MDPs with high computational cost.
method Combines posterior sampling with online learning to achieve asymptotic optimality.
result Achieves optimal sample complexity and posterior contraction rate with per episode.
Consistent partial identification of causal effects proved for neural models.
problem Consistency of neural causal partial identification methods.
method Proving consistency for neural models with continuous and categorical variables, considering architecture design and Lipschitz regularization.
result Proven consistency of partial identification via neural causal models in a general setting.
New gait segmentation method identifies users and adversaries with high accuracy.
problem Simultaneous identification of users and adversaries from accelerometer data.
method Geometric features and a new similarity metric for time series analysis.
result 98.79% accuracy for 6 classes (user-adversary identification) and 99.06% for binary (user only identification).
A new Bayesian approach to linear system identification has been proposed in a series of recent papers. The main idea is to frame linear system identification as predictor estimation in an infinite dimensional space, with the aid of regularization/Bayesian techniques. This approach guarantees the identification of stab…
Semi-parametric framework for nonlinear system identification
problem Nonlinear system identification
method Orthogonal Gaussian process regression
result Interpretable models from incomplete physics
New methods identify causal effects without needing complete proxy variables.
problem Identifying causal effects in the presence of unmeasured confounders.
method Partial identification methods that do not require completeness of proxy variables.
result Obtain bounds on causal effects using available proxy variables.
Unified method for learning from selectively labeled data.
problem Classification with selectively labeled data from multiple decision-makers.
method Unified cost-sensitive learning (UCL) approach.
result Unified method for robust classification in selective labeling.
Bayesian neural networks with nonparametric noise models for system identification.
problem Estimating parameters and noise processes in stochastic dynamic systems.
method Bayesian nonparametric approach using neural networks and Gibbs sampler.
result The method converges to full nonparametric Bayesian regression model.
Deep SSMs use neural networks to identify complex systems.
problem Identifying nonlinear systems with high uncertainty.
method Deep state space models with neural networks.
result Deep SSMs outperform traditional methods on benchmarks.
Most users of online services have unique behavioral or usage patterns. These behavioral patterns can be exploited to identify and track users by using only the observed patterns in the behavior. We study the task of identifying users from statistics of their behavioral patterns. Specifically, we focus on the setting i…