ADA augments data using AR replicas for robust regression.
problem Improving robustness in nonlinear over-parametrized regression.
method Extends Anchor regression (AR) for data augmentation, using replicas of modified samples.
result ADA provides more robust regression predictions compared to state-of-the-art solutions.
AdaS adapts SGD learning rate based on knowledge gain metrics.
problem Empirical step-size selection in SGD optimization lacks consistency and insight.
method Introduces AdaS algorithm that adapts SGD learning rate based on knowledge gain metrics.
result AdaS outperforms existing adaptive learning methods in convergence and generalization.
Reinforce-Ada improves RL for language models by adaptively sampling difficult prompts.
problem Signal loss in RL for large language models due to undersampling.
method Adaptive sampling based on a non-linear RL objective, optimizing prompt difficulty weighting.
result Reinforce-Ada significantly outperforms uniform sampling, recovering lost signals and accelerating convergence.
Ada-LISTA adapts neural solvers for varying models.
problem Adapting neural solvers for varying models.
method Ada-LISTA receives pairs of signals and dictionaries, learns a universal architecture, and solves sparse coding in linear rate.
result Ada-LISTA solves sparse coding in linear rate for varying models.
Paper extends method to detect reverse engineering attacks on DNN classifiers.
problem Detecting reverse engineering attacks on deep neural network image classifiers.
method Extend ADA approach to detect RE attacks (ADA-RE).
result Successfully detects 'stealthy' RE attacks before they launch test-time evasion attacks.
Single neuron with ADA learns XOR and outperforms other functions.
problem Classifying linearly non-separable data.
method Proposed a new artificial neuron with apical dendrite activation.
result ADA function achieves 100% accuracy on XOR and superior performance on benchmark datasets.
Two algorithms improve federated learning efficiency and resilience.
problem Scalability issues in federated learning due to communication, privacy, and Byzantine attacks.
method Proposes two algorithms, Ada-StoSign and β β β -StoSign, that compress gradients into bit vectors to reduce communication. result Ada-StoSign converges with a rate of O ( log T / T + 1 / M ) O(\log T/\sqrt{T} + 1/\sqrt{M}) O ( log T / T + 1/ M ) and outperforms existing methods. Personalized model improves ADAS-Cog13 cognitive score forecasting.
problem Improving accuracy in predicting cognitive changes in Alzheimer's Disease.
method Meta-Weighted Gaussian Process Experts (pGPE) model for personalized forecasting.
result Meta-weighting of expert models leads to significant improvements in forecasting accuracy.
Adaptive stochastic gradient methods such as AdaGrad have gained popularity in particular for training deep neural networks. The most commonly used and studied variant maintains a diagonal matrix approximation to second order information by accumulating past gradients which are used to tune the step size adaptively. In…
Unified view of GNNs as graph signal denoising.
problem Understanding and improving GNNs for graph data.
method Established GNNs as graph denoising problems with smoothness assumptions.
result Unified framework UGNN for adaptive smoothness graphs.
Proposes Ada-Sit method for mortality prediction of rare diseases.
problem Data insufficiency and clinical diversity of rare diseases make mortality prediction hard.
method Initialization-sharing multi-task learning method (Ada-Sit) for fast adaptation to similar tasks.
result Experimental results show the proposed model is effective for mortality prediction of diverse rare diseases.
The paper supports task classification from eye movements, achieving 95.4% accuracy.
problem Decoding the observer's task from eye movements.
method Exploratory analysis, feature projection, feature elimination, SVM and Ada Boosting classifier training.
result Achieved 95.4% accuracy in task classification.
Ada-BKB optimizes black-box functions on continuous domains with adaptive discretization.
problem Optimizing functions with continuous domains using Gaussian process optimization.
method Adaptive discretization of the function domain to avoid non-convex optimization costs.
result Ada-BKB algorithm runs in O ( T 2 d e x t e f f 2 ) O(T^2 d_ ext{eff}^2) O ( T 2 d e x t e f f 2 ) , significantly faster than existing methods. KD improves DNN performance on unseen data without hyperparameter tuning.
problem Overfitting in DNNs on unseen data.
method Knowledge distillation for semi-supervised domain adaptation.
result KD achieves significantly higher WMH dice scores than baseline and ADA.
Accurate diagnosis of Alzheimer's Disease (AD) entails clinical evaluation of multiple cognition metrics and biomarkers. Metrics such as the Alzheimer's Disease Assessment Scale - Cognitive test (ADAS-cog) comprise multiple subscores that quantify different aspects of a patient's cognitive state such as learning, memor…
Generative models learn from high-level music representations, but this work models music in raw audio.
problem Capturing long-range correlations in music generation.
method Exploring autoregressive discrete autoencoders (ADAs) to model raw audio waveforms.
result Unconditional generation of piano music in raw audio domain with long-range correlations.
New algorithm reduces regret in online portfolio and quantum state learning.
problem Efficiently learning portfolios and quantum states online with minimal regret.
method BISONS algorithm for online portfolio selection, SCHRODINGER'S BISONS for quantum states, with polylogarithmic regret.
result First efficient algorithm with polylogarithmic regret for online portfolio selection and quantum states.
Paper presents CeNN quantization for efficient CPS applications.
problem Efficient processing for CPS applications, especially in telemedicine and ADAS.
method Incremental quantization of CeNNs with various strategies.
result Achieved up to 7.8x speedup with no performance loss.
This paper certifies cluster assignments from sum-of-norms clustering algorithms.
problem Certifying the correct cluster assignments from approximate solutions of sum-of-norms clustering.
method Presented a clustering test that identifies and certifies the correct cluster assignment from an approximate solution.
result The correct cluster assignment is guaranteed to be certified by a primal-dual path following algorithm after sufficient iterations.
Deep learning powers automotive innovations like self-driving cars.
problem Improving vehicle safety and functionality.
method Deployment of deep learning models in ADAS systems.
result Deep learning enhances performance and functionality of ADAS solutions.
In this paper, we introduce the use of a personalized Gaussian Process model (pGP) to predict the key metrics of Alzheimer's Disease progression (MMSE, ADAS-Cog13, CDRSB and CS) based on each patient's previous visits. We start by learning a population-level model using multi-modal data from previously seen patients us…
New RL algorithm tackles nonstationary MDPs with linear approximations and varying rewards.
problem Nonstationary reinforcement learning with evolving reward and state transition functions.
method Developed a new algorithm LSVI-UCB-Restart with periodic restart, and parameter-free Ada-LSVI-UCB-Restart for unknown variation budgets.
result First minimax dynamic regret lower bound for nonstationary linear MDPs and linear MDPs lower bound.
Deep learning predicts Alzheimer's Disease progression with high accuracy.
problem Predicting multiple aspects of Alzheimer's Disease progression.
method Unsupervised deep learning on 1908 patients' 18-month clinical data.
result Model accurately predicts ADAS-Cog scores and identifies word recall as a predictor.
New algorithm boosts classification for imbalanced data.
problem Accurately classifying observations in severely imbalanced datasets.
method SAMME.C2 algorithm blending boosting and cost-sensitive techniques.
result Consistently superior performance in imbalanced classification problems.
Data mining techniques predict breast cancer types with high accuracy.
problem Early detection of breast cancer to reduce mortality rates.
method Twelve classification algorithms applied to the Breast Cancer Wisconsin dataset.
result High accuracy in predicting malignant and benign breast cancer.
Advanced driver assistance systems (ADAS) can be significantly improved with effective driver action prediction (DAP). Predicting driver actions early and accurately can help mitigate the effects of potentially unsafe driving behaviors and avoid possible accidents. In this paper, we formulate driver action prediction a…
ADASS selects adaptive subsets for SGD training acceleration.
problem Fixed sample size in SGD limits training efficiency.
method ADASS selects adaptive subsets based on Lipschitz constants.
result ADASS achieves comparable accuracy with full training set.
Research uses SWT and BDLSTM to forecast stock and oil prices amid COVID-19.
problem Impact of COVID-19 on stock and oil prices forecasting.
method Integrates Stationary Wavelet Transform and Bidirectional Long Short-Term Memory networks.
result BDLSTM+WT-ADA achieved satisfactory results in Crude Oil price forecasting.
Graded Transformers embed algebraic structure in neural networks through graded transformations.
problem Efficiently modeling hierarchical and structured data in neural networks.
method Introduces Linearly Graded Transformer (LGT) and Exponentially Graded Transformer (EGT) with graded scaling operators.
result Establishes rigorous guarantees and improved efficiency for structured data.
Long-term lane change prediction model predicts maneuvers with 75% accuracy.
problem Predicting long-term lane changes for safer autonomous driving.
method Introduced three models: logistic regression, MLP, and RNN. Used NGSIM dataset with new labeling scheme.
result Developed model predicts 75% of lane changes with an average advanced time of 8.05 seconds.
A new method makes adversarial domain adaptation aware of class relationships.
problem Ignoring inter-class semantic relationships in domain adaptation.
method RADA algorithm that aligns inter-class dependencies learned from domain discriminator with those from label predictor.
result Improves performance on benchmark datasets by incorporating class relationships.
Generative framework tackles zero-shot learning with adversarial domain adaptation.
problem Domain shift between seen and unseen class distributions in zero-shot learning.
method End-to-end learning of seen and unseen class distributions, adversarial domain adaptation.
result Superior accuracies compared to state-of-the-art models on various benchmark datasets.
Adapts ISO 26262 for machine learning in automotive software.
problem Conflict between innovation and safety in automotive software development.
method Detailed assessment and adaptation of ISO 26262 for machine learning, analyzing and proposing new requirements.
result Adapted ISO 26262 for machine learning, addressing safety and innovation in automotive software.
Study benchmarks cryptocurrency risk using GBM, revealing Lognormal limitations.
problem Tackles limitations of Lognormal assumption in modeling cryptocurrency volatility and VaR.
method Applies Geometric Brownian Motion (GBM) with Maximum Likelihood Estimation and correlated Monte Carlo Simulation.
result Observed limitations of Lognormal assumption in cryptocurrency volatility and VaR calculations.
Industrial vehicle uses LiDAR and camera to detect and avoid restricted areas.
problem Avoiding collisions in industrial settings with automated vehicles.
method Combining LiDAR and camera data, using deep learning for projection, and model-predictive control.
result Reduces false positives in LiDAR detection of reflective beacons.
LGD breaks the chicken-and-egg loop in adaptive SGD by using LSH sampling.
problem Challenging per-iteration cost of adaptive gradient sampling.
method Locality Sensitive Hashing (LSH) sampled Stochastic Gradient Descent (LGD).
result Superior and faster gradient estimation with similar per-iteration cost.
Improved pedestrian crossing prediction for AVs using contextual factors.
problem Accurate prediction of pedestrian crossing behavior for autonomous vehicles.
method Factored Latent-Dynamic Conditional Random Fields (FLDCRF) for multi-label sequence prediction and joint interaction modeling.
result Achieved at least 0.9 seconds earlier prediction accuracy for pedestrian crossing behavior compared to existing methods.
New findings show many popular bandit algorithms are unstable, contradicting minimax optimality.
problem Challenges in statistical inference from bandit algorithms due to adaptive, non-i.i.d. nature.
method Analysis of stability properties of optimism-based bandit algorithms.
result Widely used minimax-optimal UCB-style algorithms are unstable.
Study predicts Type 2 diabetes with 85% accuracy using machine learning.
problem Early detection of Type 2 diabetes improves patient health outcomes.
method Used machine learning algorithms and a weighted ensemble model on patient data.
result Ensemble model achieved 85% accuracy in predicting Type 2 diabetes.