Net2Vec maps filters to vectors to reveal complex concept encoding.
problem Understanding how deep neural networks encode semantic concepts.
method Net2Vec framework that maps semantic concepts to vectorial embeddings based on filter responses.
result Multiple filters are often required to code for a concept, and filters help encode multiple concepts.
Previous work on recommender systems mainly focus on fitting the ratings provided by users. However, the response patterns, i.e., some items are rated while others not, are generally ignored. We argue that failing to observe such response patterns can lead to biased parameter estimation and sub-optimal model performanc…
Neuron Shapley identifies key neurons in deep networks, improving model accuracy and fairness.
problem Identifying responsible neurons in deep networks for better model performance and fairness.
method Neuron Shapley framework quantifies neuron contributions, accounting for interactions.
result Removing just 30 critical filters can destroy model accuracy, revealing network function.
Collaborative filtering predicts drug responses from gene expression data.
problem Predicting drug responses from large gene expression datasets with limited samples.
method Low-rank matrix factorization and latent linear regression.
result The proposed method outperforms state-of-the-art methods in predicting drug-gene associations.
Novel CNN integrates learnable FIR filters for heart sound detection.
problem Automatic detection of heart sound abnormalities for early diagnosis.
method Proposes a CNN with tConv layers to learn FIR filter-bank parameters.
result Proposed models outperform state-of-the-art systems in heart sound detection.
A new model for complex cells accounts for insensitivity to image shifts.
problem Complex cells' response to image shifts.
method Linear combination of Gaussian derivatives at a single position.
result Maximum response insensitive to small shifts of the image.
Paper presents efficient algorithms for convolutional neural networks using Winograd minimal filtering.
problem Resource-efficient implementation of convolutional neural networks.
method Winograd minimal filtering trick applied to M-tap filters (M=3,5,7,9,11) for parallel hardware implementation.
result Approximately 30% reduction in multipliers for fully parallel hardware implementation.
This work analyzes the stability of graph filters under large perturbations.
problem Stability of graph filters under large edge rewires.
method Proves a bound on stability using frequency response and community structure.
result Graph filter stability depends on perturbation to community structure.
In the co-sparse analysis model a set of filters is applied to a signal out of the signal class of interest yielding sparse filter responses. As such, it may serve as a prior in inverse problems, or for structural analysis of signals that are known to belong to the signal class. The more the model is adapted to the cla…
The paper presents a machine learning approach to multidimensional item response theory.
problem Modeling and predicting student performance from assessment data.
method Inspired by collaborative filtering, the paper defines a general class of models using penalized joint maximum likelihood (JML) for estimation and cross-validation for model selection.
result The high-dimensional model fit to large and sparse data does not lend itself well to traditional factor interpretation.
In order to interact intelligently with objects in the world, animals must first transform neural population responses into estimates of the dynamic, unknown stimuli which caused them. The Bayesian solution to this problem is known as a Bayes filter, which applies Bayes' rule to combine population responses with the pr…
New method models covariates and responses without parametric assumptions using manifold learning.
problem Losing explanatory power for responses in standard factor models applied to covariates alone.
method Anisotropic diffusion maps for learning low-dimensional embeddings.
result Kalman filtering in diffusion-map coordinates improves joint covariate-response prediction.
Bayesian model for cancer drug studies maps dose-response curves.
problem Mapping dose-response curves in cancer drug studies.
method Bayesian Tensor Filtering (BTF) with low-dimensional embeddings and structured shrinkage priors.
result BTF outperforms state-of-the-art methods in cancer drug studies.
Visualizes deep neural networks for speech recognition using learned topographic filter maps.
problem Unintuitive internal structure of deep neural networks complicates activation visualization.
method Trains a convolutional speech recognition model with filters arranged in a 2D grid, highlighting similar filters.
result Topographic filter maps visualize artificial neuron activations more intuitively.
Paper introduces a new estimator for Rasch model with exact error analysis.
problem Estimating parameters of the Rasch model with performance guarantees.
method Develops a novel L-MMSE estimator for the Rasch model with nonasymptotic analysis.
result The L-MMSE estimator provides exact error analysis and performs similarly to state-of-the-art estimators.
ARMA filters improve graph neural networks' flexibility and robustness.
problem Improving graph neural networks' performance and robustness.
method Proposes an ARMA-based graph convolutional layer for graph neural networks.
result ARMA filters outperform polynomial filters in various graph tasks.
PDE-Net learns PDEs from data using neural networks.
problem Learning PDEs from complex system dynamics.
method Proposes PDE-Net, a feed-forward deep network to learn differential operators and nonlinear responses.
result PDE-Net can accurately predict dynamics and uncover hidden PDE models.
One of the cornerstones of the field of signal processing on graphs are graph filters, direct analogues of classical filters, but intended for signals defined on graphs. This work brings forth new insights on the distributed graph filtering problem. We design a family of autoregressive moving average (ARMA) recursions,…
The vast majority of the neural network literature focuses on predicting point values for a given set of response variables, conditioned on a feature vector. In many cases we need to model the full joint conditional distribution over the response variables rather than simply making point predictions. In this paper, we …
Develops an inverse particle filter for cognitive systems.
problem Tracking cognitive adversaries in counter-adversarial applications.
method Global filtering approach using Monte Carlo methods and differentiable I-PF.
result Demonstrates convergence to optimal inverse filter and improved estimation performance.
Plants monitor their surrounding environment and control their physiological functions by producing an electrical response. We recorded electrical signals from different plants by exposing them to Sodium Chloride (NaCl), Ozone (O3) and Sulfuric Acid (H2SO4) under laboratory conditions. After applying pre-processing tec…
BankGCN improves graph convolution networks by handling multi-channel signals with adaptive filter banks.
problem Handling multi-channel graph signals with limited architectures.
method BankGCN decomposes multi-channel signals into subspaces and uses adapted filters for each subspace.
result BankGCN achieves excellent performance in graph classification on benchmark datasets.
Non-negative matrix factorization models based on a hierarchical Gamma-Poisson structure capture user and item behavior effectively in extremely sparse data sets, making them the ideal choice for collaborative filtering applications. Hierarchical Poisson factorization (HPF) in particular has proved successful for scala…
Method detects errors in numerical data using regression models.
problem Noise and errors in numerical datasets.
method Introduced veracity scores and a filtering procedure for error detection.
result Method outperforms other approaches in identifying incorrect values.
Enhanced conformal methods improve validity of LLM outputs.
problem Lack of conditional validity and high false rejection rates in LLM validity guarantees.
method Adaptive conditional conformal procedure and improved scoring function differentiation.
result Demonstrated improved validity and utility on real datasets.
Stable algebraic filters improve neural network performance.
problem Improving neural network stability to deformations.
method Analyzed stability of algebraic filters and neural networks under deformations of the homomorphism.
result Stable algebraic filters have frequency responses whose derivative is inversely proportional to frequency.
New method quantifies market shocks and their effects.
problem Quantifying the impact and response to market shocks.
method Sparse probabilistic elliptical model with L0-norm regularization. result Shock patterns are related to network structure and sector diversification.
This paper extends a Kyle model to include price-responsive traders, revealing new dynamics and equilibria.
problem Real-world market dynamics involve price-responsive traders, affecting market equilibrium and insider profits.
method Developed a continuous-time Kyle model with two types of price-responsive traders (momentum and contrarian), leading to a forward-backward Riccati system for equilibrium.
result The model shows that feedback effects can lead to multiple equilibria and amplify price informativeness.
CNNs reveal retinal ganglion cell features, linking visual processing to neuroscience.
problem Understanding what CNNs learn about retinal neuronal circuits.
method Trained CNNs on white noise images to predict neural responses from salamander retinas.
result CNN filters resemble biological retinal components and ganglion cell receptive fields.
Accurately predicting drug responses to cancer is an important problem hindering oncologists' efforts to find the most effective drugs to treat cancer, which is a core goal in precision medicine. The scientific community has focused on improving this prediction based on genomic, epigenomic, and proteomic datasets measu…
Rotation-equivariant CNN reveals common features in V1 neurons.
problem V1 models fail to predict natural stimuli responses accurately.
method Rotation-equivariant convolutional neural network model.
result Rotation-equivariant network outperforms regular CNN and reveals common features.
New training algorithm enhances SNNs for temporal signal processing.
problem Lack of robust training algorithms for large-scale SNNs.
method Formulated SNN as IIR filters, proposed training algorithm for optimal synapse filter kernels and weights.
result Model and training algorithm outperform state-of-the-art approaches in accuracy.
Researchers adaptively analyze market regimes to reveal investor behavior shifts.
problem Market relationships shift across different regimes, affecting investor behavior.
method Combining Kalman filtering, Markov-switching, and asymmetric response estimation.
result Foreign investors' predictive power increases during crises, while individual investors react more strongly to positive shocks.
We propose SPARFA-Trace, a new machine learning-based framework for time-varying learning and content analytics for education applications. We develop a novel message passing-based, blind, approximate Kalman filter for sparse factor analysis (SPARFA), that jointly (i) traces learner concept knowledge over time, (ii) an…
Training models to prefer certain responses can unintentionally shift probability to harmful ones.
problem Likelihood displacement in DPO models, leading to unintended unalignment.
method Characterized and mitigated likelihood displacement using CHES score.
result Training models to prefer certain responses can unintentionally shift probability mass to harmful responses.
Paper tackles fault tolerance in distributed linear regression.
problem Fault tolerance in distributed linear regression with Byzantine faulty agents.
method Robustified distributed gradient descent using norm-based filters.
result Server can determine linear relationship deterministically in a log-linear computation cost.
Novel digital twin for complex systems improves performance.
problem Lack of practical implementation details for stochastic nonlinear MDOF systems.
method Decouples time-scales, uses physics-based model, Bayesian filtering, and machine learning.
result Excellent performance of proposed digital twin framework validated by examples.
Paper proposes PNN and PIN models for better user response prediction.
problem User response prediction in multi-field categorical data with sparse representations.
method Kernel product for field-aware feature interactions, Product-based Neural Network (PNN) for DNN-based models, and Product-network In Network (PIN) for generalization.
result PNN and PIN models consistently outperform 8 baselines on AUC and log loss.
Researchers use FIR filters to predict COVID-19 infections and recoveries.
problem Predicting the spread of COVID-19 without a vaccine.
method Modified numerical method using FIR filters and ridge regression.
result Modified algorithm yields better approximation errors.
Deep learning classifies animal behavior from wearable accelerometers.
problem Classifying animal behavior from accelerometer data.
method End-to-end deep neural network with IIR and FIR filters.
result Outperforms state-of-the-art algorithms in real-time classification.
Improved robust regression for heavy-tailed and contaminated data.
problem Linear regression with heavy-tailed and adversarially contaminated covariates and responses.
method Applying a filtering algorithm to covariates and then using Huber regression, least trimmed squares, or least absolute deviation estimators on the remaining data.
result Near-optimal error rates achieved for the Huber regression estimator.
Paper uses NLP and IRT to score ESG factors from news articles.
problem Lack of precise ESG metrics in finance.
method Combines NLP and IRT models on ESG-related news data.
result Method offers more precise ESG metrics with temporal dynamics.
A new method improves SSVEP BCI to recognize responses in sub-second time with high accuracy.
problem Achieving high accuracy in SSVEP BCI with short response times.
method CSTA (Combining Spatial-Filtering and Temporal Alignment) method.
result CSTA achieves maximum mean accuracy of 97.43% in sub-second response time.
A minimalist approach improves LLM reasoning by filtering incorrect responses.
problem Improving large language model (LLM) reasoning on complex tasks.
method Revisit GRPO from a reinforce-like algorithm perspective, proposing Reinforce-Rej.
result RAFT, a simple rejection sampling baseline, outperforms GRPO and PPO.
Neural EKF improves structural dynamics prediction.
problem Accurately predicting structural response for health monitoring.
method Neural Extended Kalman Filter (Neural EKF) for learning dynamics.
result Significant predictive capabilities demonstrated on simulated and real-world data.
Local unsupervised learning outperforms end-to-end training for image classification.
problem Local unsupervised learning is believed to be inferior to end-to-end training.
method Designing a local algorithm to learn convolutional filters at scale on large image datasets using local Hebbian learning.
result Convolutional networks with patch normalization significantly outperform standard networks on image classification tasks.
We prove the correspondence between the information geometry of a signal filter and a Kähler manifold. The information geometry of a minimum-phase linear system with a finite complex cepstrum norm is a Kähler manifold. The square of the complex cepstrum norm of the signal filter corresponds to the Kähler potential. The…
Machine learning offers novel ways and means to design personalized learning systems wherein each student's educational experience is customized in real time depending on their background, learning goals, and performance to date. SPARse Factor Analysis (SPARFA) is a novel framework for machine learning-based learning a…