New method uses MHN for associative learning in network embedding.
problem Represent nodes in networks as low-dimensional vectors while incorporating topological and structural information.
method Introduces Modern Hopfield Networks (MHN) for associative learning between node content and neighbors.
result Competitive performance on node classification and linkage prediction tasks.
Deep Belief Network predicts lncRNA-disease associations with high accuracy.
problem Accurately identifying lncRNA-disease associations to understand lncRNA functionality and disease mechanism.
method Proposes a DBN-based model using heterogeneous networks and DBN for feature learning.
result Obtained AUC of 0.96 and AUPR of 0.967 on standard dataset.
Deep learning optimizes user association in Massive MIMO networks.
problem Optimizing user cell association for maximum sum-rate in Massive MIMO networks.
method Training a deep neural network to learn optimal association rules based on user positions.
result The neural network achieves the same performance as traditional optimization methods with reduced computational complexity.
The paper tests for association between latent community memberships in multi-view network data.
problem Evaluating the independence of latent community memberships in multi-view network data.
method Extended stochastic block model for two-view network data, developed a new hypothesis test.
result Evidence of weak association between latent community memberships in binary interaction and co-complex association data.
A novel cross-modal auto-encoder associates different data types efficiently.
problem Cross-modal data association in heterogeneous datasets.
method Bayesian inference framework with variational auto-encoders and associators.
result Successfully associates visual and auditory data with minimal paired data.
Generative diffusion models mimic biological memory networks, encoding associative dynamics in deep neural weights.
problem Understanding long-term memory mechanisms in neuroscience and AI.
method Interpreting generative diffusion models as energy-based models and comparing them to Hopfield networks.
result Generative diffusion models can encode associative dynamics of Hopfield networks in deep neural weights.
Paper proposes algorithms for embedding directed networks with text associated nodes.
problem Learning embeddings for directed networks with text associated nodes.
method PCTADW-1 and PCTADW-2 neural network algorithms.
result Embeddings improve node classification quality on software package dependency networks.
New KNN test improves association analysis of high-dimensional sequencing data.
problem Challenges in using neural networks for high-dimensional sequencing data analysis.
method Kernel-based neural network (KNN) test for complex association analysis.
result KNN test outperforms SKAT in detecting non-linear and interaction effects.
DeepDA uses LSTM to track multiple targets in clutter.
problem NP-hard combinatorial optimization in multi-target tracking with clutter.
method LSTM-based deep learning for data association.
result Significant performance on association ratio, target ID switching, and time-consuming tracking.
Graph network predicts circRNA-disease associations using multi-source similarity features.
problem Identifying circRNA-disease associations is challenging and time-consuming.
method Proposes a graph convolution network framework using multi-source similarity information.
result Framework predicts circRNA-disease associations with promising results and outperforms existing methods.
This paper introduces a hierarchical associative memory model with multiple layers.
problem Limitations of traditional associative memory models with only one hidden layer.
method Develops a fully recurrent model with arbitrary layers, including locally connected ones, and a corresponding energy function.
result The model can dynamically assemble memories using weights from lower layers and higher layers' rules.
This paper designs neural associative memories that can correct many adversarial errors.
problem Designing neural associative memories that can correct many adversarial errors.
method Mapping the learning phase and recall phase to dictionary learning with a square dictionary and iterative error correction in an expander code.
result The designed associative memories can store datasets with exp(n) vectors and tolerate Ω(mpolylognn) adversarial errors. New layer improves neural networks' flexibility and speed.
problem Designing efficient neural networks for real-time learning.
method Sparse Associative Memories for incremental learning.
result Improved Neural Networks with faster training and real-time support.
Crowdsourced gene set queries reveal protein-protein interactions and gene-gene associations.
problem Lack of integrative analysis of diverse gene set queries.
method Harnessed thousands of user-submitted gene sets to construct a global gene-gene association network.
result The constructed network recapitulates known protein-protein interactions and gene-gene functional associations.
A new method uses machine learning to optimize user pairing and association in multicell NOMA networks.
problem Optimizing user pairing and association in multicell non-orthogonal multiple access (NOMA) systems.
method Formulated as a combinatorial optimization problem, solved using a Pointer Network (PtrNet) trained with deep reinforcement learning.
result Achieves near-optimal performance in terms of aggregate data rate, outperforming random heuristics by up to 30%.
VGAE learns gene-disease associations from networks, predicting disease-genes.
problem Predicting gene-disease associations from disease-gene networks.
method Introducing VGAE, a variational graph auto-encoder for disease-gene prediction.
result VGAE and C-VGAE outperform baseline methods in disease-gene prediction.
SOFAR learns large-scale association networks efficiently.
problem Efficiently understanding large-scale response-predictor association networks.
method Sparse Orthogonal Factor Regression (SOFAR) via sparse singular value decomposition with orthogonality constraints.
result SOFAR achieves statistical efficiency and scientific insights.
A new framework learns shared features from multi-view data with many-to-many associations.
problem Learning shared features from multi-view data with many-to-many associations.
method Probabilistic Multi-view Graph Embedding (PMvGE) using neural networks.
result PMvGE outperforms existing multi-view methods in large-scale datasets.
Model stores many more patterns than neurons, improving pattern recognition.
problem Storing and retrieving many more patterns than neurons in a network.
method Constructs a family of models interpolating between feature-matching and prototype modes, corresponding to neural networks with various activation functions.
result Higher rectified polynomials can be used in neural networks for improved pattern recognition.
A neural network learns word-referent associations across various contexts.
problem Learning word-referent associations in different contexts.
method A biologically inspired multi-layered architecture that takes images and phonemes as input, builds representations, and adjusts prototypes based on current context.
result The model achieves up to 78% accuracy in ambiguous situations and mimics human learning patterns.
Sigmoid autoencoders can implement associative memory with certain conditions.
problem Implementing associative memory in neural networks.
method Theoretical analysis of overparameterized sigmoid autoencoders using the NTK and iterative maps.
result Overparameterized sigmoid autoencoders can have attractors in the NTK limit, leading to associative memory.
A test for neural networks identifies genetic associations.
problem Testing complex associations in neural networks.
method Sieve quasi-likelihood ratio test for neural networks with one hidden layer.
result The test statistic has an asymptotic chi-squared distribution.
A neural network speeds up bond-associated peridynamics simulations.
problem High computational costs in bond-associated peridynamics.
method Message-passing neural network (MPNN) for bond-associated peridynamic material correspondence formulation.
result Significantly reduces computation time via GPU acceleration.
The paper analyzes how news sentiment of companies can affect market movements.
problem Understanding how news sentiment impacts market performance and volatility.
method Applied NLP techniques to analyze news sentiment of 87 companies over 7 years.
result Strong media sentiment towards one company can indicate significant changes in sentiment towards related companies.
This paper improves RNN memory capacity for long sequences through learning associative memory update rules.
problem Challenges in RNNs remembering long sequences.
method Jointly learns memory update rule with task objective and uses multiple associative memories.
result Improves memory capacity for long sequence encoding.
We describe convolutional networks using harmonic functions.
problem Understanding the function space and smoothness of convolutional networks.
method Using reproducing kernel Hilbert spaces and functional ANOVA decomposition.
result Convolutional networks can be decomposed into a sum of elementary functions.
New cooperative dynamics enhances retrieval performance in neural networks.
problem Understanding emergent computational capabilities in disordered systems.
method Leveraging statistical mechanics, extended neural network architecture for hetero-associative memory.
result Layers trained with less informative datasets develop retrieval regions of the same amplitude, leading to optimal performance.
Study improves scalability of cell-free massive MIMO networks by optimizing UE-AP association.
problem Optimizing UE-AP association in cell-free massive MIMO networks.
method Deep learning algorithm using Bidirectional Long Short-Term Memory cells and hybrid probabilistic weight updating.
result Enhanced scalability without retraining, robust against pilot contamination.
QRPNNs use quaternion-valued recurrent correlation neural networks to solve cross-talk issues.
problem Cross-talk problem in QRCNNs.
method Combining non-local projection learning with QRCNNs.
result QRPNNs exhibit greater storage capacity and noise tolerance.
Federated learning optimizes task and resource allocation in balloon networks.
problem Minimizing energy and time consumption in task computation and transmission.
method SVM-based federated learning algorithm to dynamically adjust user associations, service sequences, and task allocations.
result Reduces the weighted sum of energy and time consumption by up to 16.1%.
DAM with MRL improves relational reasoning in MANNs.
problem Limited performance of associative memory networks on complex relational reasoning tasks.
method Distributed Associative Memory architecture with Memory Refreshing Loss.
result Enhanced relation reasoning performance of MANNs on long temporal sequence data.
ARMDN forecasts retail demand by modeling associative factors and trends.
problem Accurately forecasting demand for e-retailers with many associative factors and non-stationary shifts.
method ARMDN combines feature embeddings, MLP, LSTM, and mixture of Gaussian distributions to model demand.
result ARMDN outperforms existing methods in forecasting accuracy.
Bayesian Neural Networks help quantify uncertainty in deep learning predictions.
problem Uncertainty quantification in deep learning predictions.
method Bayesian statistics applied to neural networks.
result Design, implementation, training, and evaluation of Bayesian Neural Networks.
A new theory explains large associative memory with biological plausibility.
problem Large associative memory in neurobiology and machine learning.
method Microscopic theory with hidden neurons and two-body interactions.
result Valid model of large associative memory with biological plausibility.
SEISM tests neural network features for regulatory genomics.
problem Testing neural network features for regulatory genomics.
method Selective inference procedure for sequence motifs.
result Sampling under specific parameters characterizes composite null hypothesis.
This paper proposes using flying platforms for efficient small-cell base station association in 5G networks.
problem Efficient association and placement of backhaul hubs for small-cell base stations in 5G networks.
method Formulates the association problem considering backhaul link and NFP limitations, then presents a distributed greedy search solution.
result Observes favorable performance via numerical comparison with optimal exhaustive search algorithm.
We present a procedure for effective estimation of entropy and mutual information from small-sample data, and apply it to the problem of inferring high-dimensional gene association networks. Specifically, we develop a James-Stein-type shrinkage estimator, resulting in a procedure that is highly efficient statistically …
Develops hyperparameter transfer methods for Dense Associative Memories.
problem Challenges in transferring hyperparameters for DenseAMs due to unique architecture and activation functions.
method Derives explicit prescriptions for hyperparameter transfer from small to large models.
result Excellent agreement between theoretical and empirical results.
Extends RCNNs to handle hypercomplex-valued data.
problem Processing high-dimensional data.
method Develops mathematical background and conditions for hypercomplex-valued RCNNs.
result Hypercomplex-valued RCNNs can always settle at an equilibrium.
Exponential neural networks store many patterns, mapping cues to targets.
problem Storing many patterns in a neural network efficiently and accurately.
method Introduced an exponential neural network with multiple layers, each storing a dataset.
result The network can store an exponential number of patterns, and it generalizes well to unseen data.
The paper predicts edge weights in weighted directed networks using metric geometry.
problem Predicting edge weights in weighted directed networks.
method Introducing new types of weighted directed networks (AWDNs), constructing metrics, and proposing modified kNN and SVM methods.
result The proposed methods outperform traditional approaches in predicting edge weights.
Sharp limits found for storing and retrieving input-output associations in linear associative memories.
problem Understanding the fundamental limits of storing and retrieving input-output associations in neural networks.
method Study of a minimal linear associative memory model, introducing a decoupled model and using statistical physics to characterize storage capacity.
result Linear associative memory can store up to 1/2 log(p) associations, providing a sharp statistical-physics characterization.
Graph Convolutional Networks improved with topological features for better accuracy.
problem Improving Graph Convolutional Networks for node classification.
method Using topological features of nodes and adjacency matrices with distant nodes of similar topology.
result Adding topological features to GCN significantly improves accuracy over state-of-the-art methods.
The Unlearning algorithm improves neural network performance in memory tasks.
problem Improving neural network performance in memory tasks.
method Simplified Unlearning algorithm, structured training data, and new regularization technique for Boltzmann Machines.
result The Unlearning rule outperforms traditional Boltzmann-Machine learning in learning hidden probability distributions.
Meta-learning approach for fast and compressive energy-based memory models.
problem Learning associative memory with fast and compressive models for complex data.
method Meta-learning approach to energy-based memory models (EBMM) using arbitrary neural architectures.
result Demonstrated associative retrieval outperforming existing systems in reconstruction error and compression rate.
Discovering causal genetic variants from large genetic association studies poses many difficult challenges. Assessing which genetic markers are involved in determining trait status is a computationally demanding task, especially in the presence of gene-gene interactions. A non-parametric Bayesian approach in the form o…
Study shows deterministic equivalent for neural network kernel convergence.
problem Understanding convergence of neural network kernels.
method Analyzes empirical spectral distribution of Conjugate Kernel, proving convergence to a deterministic limit.
result Obtains a deterministic equivalent for the Stieltjes transform and resolvent of the Conjugate Kernel.
Statistical network modeling has focused on representing the graph as a discrete structure, namely the adjacency matrix, and considering the exchangeability of this array. In such cases, the Aldous-Hoover representation theorem (Aldous, 1981;Hoover, 1979} applies and informs us that the graph is necessarily either dens…