Researchers mapped CNNs to resistive devices for training, overcoming noise and bound limitations.
problem Training deep CNNs with resistive cross-point devices.
method Mapped CNN layers to RPU arrays, implemented noise and bound management techniques, and digitally programmable update management.
result Successfully applied RPU concept for training CNNs, enabling broader applicability.
New neural network units resist adversarial attacks effectively.
problem Adversarial attacks on neural networks that misclassify inputs.
method Introduced RBFI units with non-linear structure.
result RBFI units maintain high accuracy in adversarial attacks.
Study uses AI and ML to predict and optimize corrosion resistance of aluminum alloys.
problem Corrosion resistance of aluminum alloys in marine environments.
method Investigated two ML approaches: direct and inverse, using Random Forest, neural network, and Gaussian Process Regression.
result Gaussian Process Regression with hybrid kernel functions provided superior predictive performance.
New neural network units resist adversarial attacks effectively.
problem Adversarial attacks on machine learning models.
method Introduced MWD units, developed training techniques, and computed robustness.
result MWD networks are significantly more robust to adversarial attacks.
RPU devices accelerate deep neural network training by orders of magnitude.
problem Training large deep neural networks is time-consuming and resource-intensive.
method Resistive processing units (RPU) store and update weights locally, reducing data movement and parallelizing training.
result RPU can achieve acceleration factors of 30,000X compared to state-of-the-art microprocessors.
Study predicts antimicrobial resistance in ICU patients quickly.
problem Delayed AMR testing in ICU leads to suboptimal treatment.
method Developed predictive models using clinical and microbiological data.
result Machine learning models predict AMR with higher accuracy than naive model.
RPU devices accelerate LSTM training with significant power and speed benefits.
problem Training large-scale LSTM networks efficiently using resistive cross-point devices.
method Extending RPU concept to LSTMs, studying imperfections, and optimizing input signal resolution.
result RPU devices can provide large acceleration factors for RNNs, with 1500 times larger models than fully connected networks.
Solves optimal stopping problem for financial technical analysis.
problem Optimal stopping problem for technical analysis models.
method Wide-class dynamics modeling support/resistance lines.
result Solution to optimal stopping problem for technical analysis.
This paper optimizes predicting support and resistance levels in financial markets.
problem Optimizing prediction of resistance and support levels in financial markets.
method Assuming a constant elasticity of variance process, the paper derives optimal trading boundaries using the aspiration level hypothesis.
result Optimal trading boundaries serve as predictors of resistance and support levels, located relative to the median interval of the hidden aspiration level.
Reduces data needed for resist modeling in lithography simulations.
problem High data demand for resist modeling in lithography simulations.
method Transfer learning and active data selection.
result 3-10X reduction in training data with comparable accuracy.
Improved reinforcement learning for noisy systems.
problem Data efficiency and noise resistance in reinforcement learning.
method Extends PILCO algorithm to POMDPs, incorporating filtering process.
result Significantly higher performance in partially observed tasks.
The paper discovers and evaluates support and resistance levels in financial time series.
problem Understanding and predicting support and resistance levels in financial markets.
method Developed a heuristic discovery algorithm to identify SR levels in intraday price series.
result Discovered SR levels statistically significantly reverse price trends and have a decay aspect over time.
Improved community detection using effective resistance-based seed sets.
problem Low quality of seed nodes hinders community detection.
method Two-step process: effective resistance-based germination followed by PageRank.
result Improves precision and recall in community detection.
New approach uses RS levels to improve stock trading RL model efficiency and stability.
problem Stability and adaptivity challenges in current financial RL applications.
method Leverages RS levels as regularization terms in MBRL for stock trading optimization.
result Enhanced performance and better profit gain with less riskiness compared to pure MBRL models.
This paper approximates p-resistance for multi-class graph clustering.
problem Efficiently clustering graphs with p-Laplacian bias. method Developed an approximation to p-resistance for multi-class clustering. result Approximated p-resistance provides a computationally feasible alternative to p-Laplacian eigenvectors. RESIST improves decentralized learning resilience against MITM attacks.
problem Decentralized learning's vulnerability to MITM attacks.
method Multistep consensus gradient descent framework with robust statistics-based screening methods.
result Achieves algorithmic and statistical convergence for various ERM problems.
FAL improves formation resistivity prediction from cased boreholes with noise resistance.
problem Noise and high-frequency disaster in predicting formation resistivity from cased boreholes.
method Frequency-aware framework and temporal anti-noise block for LSTM.
result FAL achieves a 24.3% improvement in R2 over LSTM, reaching R2=0.91.
Study tackles causal effects of close contact on MRSA infections from entangled treatment data.
problem Estimating causal effects of close contact on MRSA infections from observational data with entangled treatments.
method NEAT method that models treatment assignment mechanism and mitigates confounding biases.
result NEAT method effectively estimates causal effects from entangled treatment data.
Optimal trading strategy under market resistance and concave price impact model.
problem Optimal trading in a market with endogenous resistance and concave price impact.
method Modeling market resistance, deriving a stochastic Fredholm equation, proving existence and uniqueness, proposing an iterative scheme.
result Existence and uniqueness of optimal control under certain conditions, exponential convergence of iterative scheme.
Extends Newton's minimal resistance problem to Riemannian surfaces.
problem Minimal resistance on Riemannian surfaces.
method Derive resistance functional, analyze constrained minimization.
result Smooth extremals are loxodromes, global minimizers characterized.
OGAN attacks autoencoders to prevent deepfake creation.
problem Preventing deepfake creation using adversarial attacks.
method Training-resistant adversarial attack on face-swapping autoencoders.
result OGAN attacks are training-resistant and transfer across different models and faces.
RESPIRE calibrates low-cost air-quality sensors for CO levels, resistant to outliers.
problem Calibrating LCAQ sensors against regulatory-grade monitors is expensive and time-consuming.
method PROvably outlier-resistant semi-parametric regression technique.
result RESPIRE offers improved prediction in cross-site, cross-season, and cross-sensor settings.
New curvature defined via graph resistances leads to Ricci flow.
problem Defining curvature on graph edges for analysis.
method Introducing Ricci--Foster curvature based on effective resistances and studying Ricci flow.
result Existence of solutions to Ricci flow on short time intervals, preservation of nonnegative curvature.
Researchers use interpretable classifiers to predict antibiotic resistance.
problem Predicting antibiotic resistance from genome sequences.
method Set Covering Machines for highly interpretable models.
result Highly interpretable models for antibiotic resistance prediction.
New method calculates discrete curvature using effective resistances.
problem Calculating discrete curvature on graphs.
method Effective resistances to calculate curvature on graph nodes and links.
result Relation to established discrete curvatures and convergence to continuous curvature.
Researchers propose improved multivariate prediction models for HIV drug resistance.
problem Predicting drug resistances of HIV from mutation information.
method Revised stacking algorithms to borrow information among multiple prediction tasks.
result Proposed methods outperform other multivariate prediction methods.
Generative ODE model learns unknown variables in medical systems.
problem Estimating unknown variables in complex medical systems.
method Variational autoencoder incorporating known ODE functions.
result Modeling known-unknowns improves system parameter discovery and extrapolation.
Extends Newton's minimal resistance problem to Lorentz-Minkowski space.
problem Minimal resistance in Lorentz-Minkowski space.
method Derived functional energy, determined Euler-Lagrange equation, analyzed maximum principle, found separable and radial solutions.
result Obtained solutions with conical singularities at the origin and analyzed the Single Shock Condition.
Proposes a new method to improve deep model security against adversarial deformations.
problem Deep neural networks' resistance to adversarial attacks, especially location perturbations.
method Regularizes flow gradients to provide a tighter bound and improve model resistance.
result Models trained with flow gradient regularization show better resistance to adversarial deformations compared to input gradient regularization and adversarial training.
The article deals with intrinsic metrics, Dirac operators and spectral triples induced by regular Dirichlet and resistance forms. We show, in particular, that if a local resistance form is given and the space is compact in resistance metric, then the intrinsic metric yields a geodesic space. Given a regular Dirichlet f…
This research improves CNNs' resistance to adversarial examples.
problem Vulnerability of CNNs to adversarial examples.
method Intelligent hyperparameter selection for model resistance.
result Selection of model hyperparameters impacts resistance to adversarial examples.
Graph curvature measured by inverse resistance distance.
problem Defining and analyzing curvature in graphs.
method Defining curvature via inverse resistance distance and proving properties.
result Graphs with positive curvature have controlled diameter and spectral properties.
Paper explores robust regression methods and their bias-variance trade-off.
problem Understanding the trade-off between robust estimation and optimization methods.
method Examines traditional outlier-resistant robust estimation and robust optimization.
result Both methods follow converse strategies due to a bias-variance trade-off.
Study identifies similarities in refractory epileptic patients to predict drug resistance.
problem Predict drug resistance in epileptic patients.
method Examined patient data using unsupervised and supervised algorithms to map underlying mechanisms and features contributing to drug resistance.
result Developed predictive models with accuracy of 0.83(+/-0.3) using a radial basis function kernel PCA and Gradient Boosted Decision Tree Ensemble.
Algorithm learns item qualities from noisy comparisons, scaling with graph resistance.
problem Learning item qualities from noisy comparisons.
method Minimax optimal algorithm using comparison graph resistance.
result Algorithm's performance scales with the square root of graph resistance.
Machine learning improves antimicrobial resistance detection.
problem Insufficient representation of antimicrobial resistance data in the Data-for-Good community.
method Standard ensemble machine learning techniques applied to antimicrobial resistance datasets.
result Classification accuracies of mid-90% to low-80% for AMR detection.
Deep learning identifies 30 common bacterial pathogens from Raman spectra.
problem Rapid and accurate identification of pathogenic bacteria.
method State-of-the-art deep learning applied to large Raman spectroscopy dataset.
result 99.0% accuracy in identifying 30 common bacterial pathogens.
Post-quantum cryptography needed for blockchain security.
problem Quantum computers threaten traditional blockchain cryptography.
method Review of theoretical cryptography and quantum information theory.
result Post-quantum cryptography is essential for blockchain security.
AReLU uses attention-based rectification to improve neural network performance.
problem Improving neural network performance through better activation functions.
method Integrates attention mechanism with rectified linear unit (ReLU) to learn and scale feature maps.
result AReLU significantly boosts performance of most network architectures with minimal changes.
The paper studies minimal resistance dynamics in radial fields, finding unique solutions for incompressible flows.
problem Nonlinear dynamics of minimal resistance in radial fields.
method Analysis of two non-equilibrium scenarios: scale-invariant free expansion and incompressible source flow.
result Incompressible flow acts as a structural regularizer, admitting unique, smooth, and strictly concave solutions.
New method designs antimicrobial peptides with high potency and low toxicity.
problem Designing potent antimicrobial drugs with low toxicity.
method CLaSS method using deep generative autoencoder and atomistic simulations.
result Design and synthesis of two novel AMPs with high potency and low toxicity.
Study optimal times to buy and sell stocks using support/resistance lines.
problem Optimal times to buy and sell stocks based on support and resistance lines.
method Mathematical model with probabilistic methods to solve optimal stopping problems.
result Best times to buy and sell stocks are determined by solving free boundary problems.
New piracy-resistant watermarks for deep neural networks prevent false claims.
problem Protecting deep learning models from false claims by embedding pirate watermarks.
method Null embedding, a new approach to build watermarks that resist removal and addition.
result Null embedding achieves piracy resistance and other watermark properties over various tasks and models.
Paper proves minimal resistance for a body in a fluid with decreasing density.
problem Minimal resistance for a body moving through a fluid with non-constant density.
method Local existence and regularity of radial solutions using a fixed-point theorem.
result Maximal domain of the solution is finite, terminating at a critical slope.
Paper explains AdaBoost's overfitting resistance from feature learning perspective.
problem Understanding AdaBoost's resistance to overfitting.
method Adopt AdaBoost for base classifiers, then use them as features in SVM.
result Proposes AdaBoost+SVM algorithm to explain overfitting resistance.
New algorithm eliminates symmetry requirement for training neural networks on resistive device arrays.
problem Training accuracy on resistive device arrays depends on device switching symmetry.
method Developed 'Tiki-Taka' algorithm to minimize unintentional cost term due to device asymmetry.
result Achieves same accuracy with non-symmetric devices as with symmetric devices.
Prototype controls stochastic drug resistance in cells.
problem Emergence of drug-resistant cells from random mutations.
method Deep reinforcement learning for adaptive drug dosing.
result 100% success rate in suppressing cell proliferation.
Stochastic LWTA networks resist adversarial attacks while maintaining accuracy.
problem Adversarial robustness of neural networks.
method Replaced ReLU with stochastic LWTA activations, trained with Variational Bayesian and PGD.
result Stochastic LWTA networks achieve state-of-the-art robustness against adversarial attacks.