A guide to using low-pass graph filters for network data.
problem Understanding and processing graph data with low-pass filters.
method Definition and application of low-pass graph filters to graph data.
result Low-pass filters effectively retain lower frequency graph data contents.
Guiding reinforcement learning with suboptimal controllers speeds up training.
problem Sparse rewards in reinforcement learning make exploration inefficient.
method Use a suboptimal controller to guide exploration, applying a Q-filter loss conditionally.
result The approach leads to faster policy refinement and better performance.
Unified framework for inference in complex nonlinear processes.
problem Challenges in inferring nonlinear continuous stochastic processes with sparse observations and complex topologies.
method Neural Backward Filtering Forward Guiding (NBFFG) framework that constructs a variational posterior using a proxy linear-Gaussian process.
result Empirical results show NBFFG outperforms baselines on synthetic benchmarks and high-dimensional phylogenetic analysis tasks.
Study uses SAR data to map defoliation and regrowth in tundra-forest areas.
problem Mapping defoliation and regrowth in tundra-forest areas using SAR data.
method Novel guided nonlocal means speckle filtering of polarimetric covariance matrix.
result Over 99.7% classification accuracy in defoliation and regrowth mapping.
Recursive KalmanNet generalizes well in noisy, out-of-distribution scenarios.
problem Generalization in noisy, out-of-distribution scenarios.
method Recurrent neural network guided by a Kalman filter.
result Recursive KalmanNet performs well in scenarios with different temporal dynamics from training data.
CNN predicts optimal filter parameters for BM3D denoising.
problem Optimizing denoising filter performance in real-time.
method Trained a CNN to predict the best filter parameter value for BM3D.
result CNN-guided BM3D outperforms unguided BM3D across different noise levels.
A new ensemble filter uses transport maps and MMD optimization for high-dimensional data assimilation.
problem High-dimensional data assimilation challenges in ensemble filtering.
method Optimized Maximum Mean Discrepancy (MMD) for transport map construction.
result Significant improvement in robustness and posterior approximation.
Improved real-time UAV terrain following with RVM-RLS filter.
problem Accurate real-time waypoints estimation under measurement noise in nonlinear, time-varying systems.
method Residual Variance Matching Recursive Least Squares (RVM-RLS) filter guided by RVME criterion.
result Improved waypoints estimation accuracy by approximately 88% compared to benchmarks.
SG-NTF completes HDI tensors with spectral mapping and spatio-temporal gating.
problem High-dimensional and incomplete tensor completion.
method Spectra-Guided Neural Tucker Factorization (SG-NTF) with Spatio-Temporal Co-Gating (STCG).
result Maintains competitive completion accuracy with parameter efficiency.
We compare in this paper several feature selection methods for the Naive Bayes Classifier (NBC) when the data under study are described by a large number of redundant binary indicators. Wrapper approaches guided by the NBC estimation of the classification error probability out-perform filter approaches while retaining …
New filter bank regularization improves DCNNs by incorporating image priors.
problem Improving DCNNs' robustness and generality.
method Structured filter bank regularization of DCNN kernels.
result Filter bank regularization leads to faster convergence and better generalization.
FSinR is a comprehensive R package for feature selection.
problem Feature selection in machine learning.
method Filter and wrapper methods, search algorithms.
result Provides a variety of feature selection methods and search algorithms.
USTAR combines multiple social media modalities to model user-guided activities.
problem Lack of comprehensive spatiotemporal activity models using all social media modalities.
method Online learning method embedding locations, time, text, and users into a single space, incorporating NGTSM and GTSM records, and using collaborative filtering.
result USTAR significantly improves region and keyword retrieval compared to state-of-the-art methods.
A new algorithm learns model regimes and parameters efficiently.
problem Learning high-dimensional parameters with discontinuous jumps.
method Differentiable interacting multiple model particle filter with gradient descent.
result Superior numerical performance compared to previous methods.
New method improves stability of collaborative filtering.
problem Stability issues in matrix approximation for recommender systems.
method Introduces new optimization objectives and solves the optimization problem for stable matrix approximation.
result Achieves better accuracy in rating prediction and top-N recommendation tasks.
Improves image quality in generative models by estimating pixel-wise aleatoric uncertainty.
problem Lack of quantitative assessment of image quality in diffusion models.
method Estimate pixel-wise aleatoric uncertainty during sampling phase using a perturbation scheme designed for diffusion models.
result Uncertainty-guided sampling leads to better sample generation quality as shown by FID scores.
A new particle filter uses diffusion models to improve state estimation from noisy data.
problem Sequentially estimating the state of a dynamical system from noisy and incomplete observations.
method Uses a diffusion model to simulate and predict system dynamics, incorporating noisy observations to refine predicted states.
result An unbiased particle filtering method that rigorously fuses observational data with diffusion model simulations.
This paper proposes a new portfolio allocation method using LLMs to outperform traditional strategies.
problem Persistent tradeoff between risk and return in portfolio management.
method Follow-the-leader approach with sentiment-based trade filtering and LLM-driven hedging.
result Empirical results show a 69% increase in annualized returns and 119% in Sharpe ratio compared to SPY buy-and-hold.
ePF improves PF for ITS by balancing exploration and exploitation, outperforming baselines.
problem Premature exploitation in PF leads to suboptimal solutions under constrained budgets.
method Integrates Entropic Annealing and Look-ahead Modulation to preserve diversity and evaluate potential.
result Significant improvement in task reward (up to 50% relative) on math benchmarks.
A new DR method for HSI classification improves accuracy with limited samples.
problem Challenges in DR for HSI classification with limited training samples.
method Graph-based spatial and spectral regularized local scaling cut (SSRLSC).
result Improved classification accuracy compared to spectral-only methods.
Guided spectral embedding highlights C. elegans neural network.
problem Understanding the C. elegans neural network structure and function.
method A new guided spectral embedding method that maximizes energy concentration and minimizes modified embedded distance, using a given importance weighting of nodes.
result The guided approach provides more biological insights, distinguishing somatic positions and processing functions of cells.
Improved image restoration using frequency-guided sampling.
problem Restoring high-quality images from degraded observations with known degradation processes.
method Proposed a frequency-guided sampling approach for diffusion-based image restoration, incorporating a time-varying low-pass filter.
result Significantly improved performance on challenging image restoration tasks, including motion deblurring and image dehazing.
Unified framework for self-supervised learning via latent distribution matching.
problem Lack of a unifying theoretical framework for diverse SSL methods.
method Casting SSL as latent distribution matching (LDM): maximizing alignment and uniformity.
result Derives a Bayesian filtering model and proves identifiable latent representations.
Inference-Time Scaling can be extended to domains prone to systematic failure using intrinsic statistics.
problem Scaling inference time in domains prone to systematic failure
method Intrinsic Selection (iS), Intrinsic Particle Filtering (iPF), and Particle Distillation (dPF)
result Intrinsic Selection improves engineering design selection by 20% and pass@1 by 6.1 points on average.
A new UNet variant reduces spectral artifacts in image transformations.
problem Spectral artifacts caused by traditional UNet upsampling layers.
method Introduced a Guided UNet (GUNet) architecture using a novel upsampling module.
result GUNet produces higher fidelity outputs in image transformations.
A new KD model for collaborative filtering improves top-N recommendation performance.
problem Challenges in applying KD to recommender models due to feedback sparsity and ambiguity.
method Proposes a new KD model (CD) for collaborative filtering, reformulating a loss function, using probabilistic rank-aware sampling, and developing training strategies.
result Outperforms state-of-the-art methods by 2.7-33.2% in hit rate (HR) and 2.7-29.1% in NDCG.
A new method enhances signal recovery with FDR control.
problem Challenging signal recovery in compressive sensing.
method Knockoff-guided compressive sensing framework with FDR control.
result Guaranteed FDR control leads to more accurate signal reconstruction.
Generative models use latent abstractions to create images.
problem Understanding how generative models create high-dimensional data like images.
method Developed a theoretical framework using SDE and information theory.
result Diffusion models can be seen as a non-linear filter driven by latent abstractions.
Unsupervised learning filters tweets for emergency services during crises.
problem Challenges in filtering relevant information from social web data during disasters.
method Multi-task domain adversarial attention network for unsupervised domain adaptation.
result The multi-task model outperforms single task models in filtering relevant tweets.
EGDL predicts TB outbreaks with deep learning, integrating epidemiological models.
problem Predicting TB outbreaks with complex spatiotemporal dynamics.
method Modified MN-SIR model with Bayesian inference, deep neural networks.
result EGDL delivers robust and accurate TB outbreak predictions.
Generative adversarial network synthesizes sketches into realistic images.
problem Improving the quality and realism of facial sketches.
method Hybrid GAN with quality guided encoder and identity preserving network.
result Synthesized images are more realistic and maintain identity.
This paper introduces structure learning for autoencoder recommenders to improve performance and generalization.
problem Efficient training and generalization in sparse collaborative filtering data.
method Learn groups of related items and use this information to determine the connectivity structure of an auto-encoding neural network.
result The proposed structure learning method results in a sparse network that converges to a local optimum with smaller spectral norm and generalization error.
Adaptive spectral RL method enhances RL performance and interpretability.
problem Balancing interpretability and performance in reinforcement learning.
method Spectral based linear RL approach with adaptive regularization.
result Near-optimal bounds for parameter estimation and generalization error.
System uses machine learning and automated reasoning to speed up PBE synthesis.
problem Slow synthesis in PBE due to domain-specific knowledge and large training datasets.
method Preprocess SyGuS PBE problems with a neural network to reduce search space, then use automated reasoning for faster solution.
result System outperforms all competing tools in the 2019 SyGuS Competition for the PBE Strings track by 47.65%.
The paper tackles stable maxima optimization for expensive functions.
problem Finding stable maxima of expensive functions with input variations.
method Uses multiple gradient Gaussian Process models to estimate stability and guide optimization.
result Demonstrates effective finding of stable maxima on synthetic and real-world problems.
In order to achieve state-of-the-art performance, modern machine learning techniques require careful data pre-processing and hyperparameter tuning. Moreover, given the ever increasing number of machine learning models being developed, model selection is becoming increasingly important. Automating the selection and tuni…
Transformer models can approximate smooth functions with prompts, enhancing LLMs' dynamic capabilities.
problem Lack of theoretical framework for prompt engineering in transformer models.
method Formal framework demonstrating transformer models can approximate β-times differentiable functions with prompts. result Transformer models can approximate β-times differentiable functions with arbitrary precision using appropriately structured prompts. New approach improves cross-domain recommendation for sparse target domains.
problem Cross-domain recommendation challenges with sparse target domains.
method Guided neural collaborative filtering with domain-invariant components across dense and sparse domains.
result Effective and scalable approach demonstrated on public and Visa datasets.
Support vector machines (SVMs) rely on the inherent geometry of a data set to classify training data. Because of this, we believe SVMs are an excellent candidate to guide the development of an analytic feature selection algorithm, as opposed to the more commonly used heuristic methods. We propose a filter-based feature…
Paper proves spectral filters can be transferred between graphs.
problem Proving spectral filters can be transferred between graphs.
method Introducing the Cayley smoothness space and proving filters in this space are linearly stable.
result Graph spectral filters are transferable if they are in the Cayley smoothness space.
MOPO-LSI offers a user guide for sustainable investments.
problem Sustainable investment optimization challenges.
method Open-source library for multi-objective portfolio optimization.
result User-friendly guide for MOPO-LSI version 1.0.
This work prunes CNN filters based on their functionality, not just size.
problem Redundant filters in CNNs waste computation resources.
method Functionality-oriented filter pruning method.
result Pruning based on functionality optimizes computation and interprets filter importance.
A new SOHP filter improves trend estimation in economic time series.
problem Improving trend estimation in nonlinear economic time series.
method Recursive application of one-sided HP filter on updated cyclical components, combined with an incremental HP filtering algorithm.
result Better performance of SOHP filter compared to other HP-type filters on real economic data.
GUIDE-VAE generates user-guided data with improved realism and performance.
problem Generating data points for multi-user datasets while considering user information.
method Conditional generative model that integrates user embeddings and a pattern dictionary-based covariance composition.
result GUIDE-VAE outperforms conventional VAEs in multi-user settings, especially under data imbalance.
Deep density methods improve filtering in high-dimensional systems.
problem Nonlinear filtering in high-dimensional systems.
method Two deep density methods based on Feynman-Kac formulas and neural networks.
result Logarithmic deep backward stochastic differential equation filter outperforms classical methods in high dimensions.
The paper explores modifications to filter banks for speech recognition.
problem Improving speech recognition accuracy using modified filter banks.
method The authors investigate replacing triangular filters with Gabor or Gammatone filters, and rearranging filter bank computations to integrate features over smaller time scales.
result No significant improvements in phone error rate were observed with the modifications.
Guided dropout improves neural network performance by selectively dropping nodes.
problem Overfitting in deep neural networks.
method Guided selection of nodes for dropout based on their strength.
result Guided dropout leads to better generalization compared to conventional dropout.
Gradient filters track moving parameters under noisy data and misspecification.
problem Tracking multidimensional time-varying parameters under noisy observations and model misspecification.
method Gradient-based filters update parameters using the gradient of a postulated objective function, evaluated at either the predicted or updated parameters.
result Novel sufficient conditions for exponential stability of the filtered parameter path, and finite-sample and asymptotic mean squared error bounds.