New graph learning framework outperforms existing methods.
problem Learning effective representations of large graphs with high degree variability.
method Deep hierarchical decompositions and neural network template unrolling over the hierarchy.
result Empirically outperforms state-of-the-art graph classification methods on large social network datasets.
Novel method extracts hierarchical brain connectivity patterns from fMRI.
problem Functional hierarchical organization of the human brain.
method Sparse Connectivity Patterns (SCPs) with hierarchy of sparse overlapping patterns, deep factorization of correlation matrices.
result Reproducible multi-scale hierarchical SCPs more stable than single-scale patterns.
It has long been conjectured that hypotheses spaces suitable for data that is compositional in nature, such as text or images, may be more efficiently represented with deep hierarchical networks than with shallow ones. Despite the vast empirical evidence supporting this belief, theoretical justifications to date are li…
New hierarchical tensor decomposition model for complex data.
problem Lack of natural generalization of hierarchical NMF to tensors.
method Proposes a new hierarchical nonnegative tensor decomposition (HNTF) model.
result Model more naturally illuminates topic hierarchy.
Paper compresses RNNs using HT decomposition for better performance.
problem Large model sizes of RNNs in sequence analysis.
method Hierarchical Tucker (HT) tensor decomposition for model compression.
result HT-LSTM achieves better compression and accuracy than state-of-the-art methods.
New method groups genetic data into coherent topics for disease insights.
problem Analyzing large, multi-dimensional genetic data sets.
method Conditional Hierarchical Bayesian Tucker Decomposition for genetic data analysis.
result Our models are more coherent than baseline models.
The paper tackles hierarchical reinforcement learning by approximating optimal solutions for the Traveling Salesman Problem.
problem Approximating optimal solutions for the Traveling Salesman Problem using hierarchical reinforcement learning.
method Mapping the problem into a Reward Discounted Traveling Salesman Problem and deriving approximate solutions using local policies.
result Three stochastic policies are proposed that guarantee better performance than any deterministic policy.
TreeHFD algorithm explains tree ensemble models through hierarchical orthogonality.
problem Difficulty in explaining black-box tree ensemble models.
method TreeHFD algorithm using hierarchical orthogonality constraints.
result TreeHFD estimates Hoeffding decomposition from data samples.
New method explains ML performance gaps without causal knowledge.
problem Understanding why ML algorithms perform differently across domains.
method Nonparametric hierarchical decomposition framework.
result Detailed variable-level explanations for performance gaps.
Hierarchical interpretations explain neural network predictions.
problem Inability to visualize complex, non-linear relationships learned by neural networks.
method Agglomerative contextual decomposition (ACD) for feature clustering and prediction explanation.
result ACD identifies clusters of features predictive to DNN predictions and diagnoses incorrect predictions.
Outer automorphism group of hyperbolic groups is HHG under certain conditions.
problem Characterizing the outer automorphism group of hyperbolic groups.
method Proving finite-index subgroups are central extensions of orbifold mapping class groups with bounded Euler class.
result Outer automorphism group of a one-ended hyperbolic group is virtually a hierarchically hyperbolic group.
Proves deep networks can learn hierarchical structures efficiently.
problem Understanding how deep networks learn hierarchical structures in data.
method Random Hierarchy Models, gradient-based methods, layerwise training.
result Proves deep networks can efficiently learn hierarchical structures.
RICH models scenes as hierarchical tree to learn and generate complex compositions.
problem Learning compositional structures between parts and objects in natural scenes.
method RICH uses a latent scene graph to organize entities into a tree structure and employs a top-down inference approach.
result RICH learns and generates complex scene hierarchies from unlabeled data.
HCL learns shared and modality-specific latent representations for multimodal data.
problem Binary shared-private decomposition inadequately represents shared information across subsets of modalities.
method Hierarchical Contrastive Learning framework combining latent-variable formulation, structural sparsity, and contrastive objective.
result HCL accurately recovers hierarchical structure and improves predictive performance on multimodal data.
Novel graph network learns hierarchical network structure.
problem Lack of information in hierarchical network topology.
method Hierarchical clustering for multiscale decomposition, graph convolutional layers.
result Competitive performance on citation network benchmark.
Improved deep hierarchical VAE with diffusion-based VampPrior.
problem Latent variable generative modeling challenges.
method Hierarchical VAE with amortized diffusion-based VampPrior.
result Better performance with fewer parameters and improved stability.
This paper improves neural network explanations by quantifying and visualizing semantic compositions.
problem Improving neural network explanations for natural language processing tasks.
method Proposes a formal way to quantify word and phrase importance, introduces SCD and SOC algorithms.
result Our algorithms outperform prior methods in explaining neural network predictions.
Improved spectral methods of moments for robust latent variable model learning.
problem Limited robustness of spectral methods of moments to model misspecification.
method Hierarchical approach using approximate joint diagonalization instead of tensor decomposition.
result Our method outperforms previous tensor decomposition methods in speed and model quality.
Enhances exploration in hierarchical networks using mutual information.
problem Limited exploration in hierarchical Deep Q-Networks.
method Adversarial Soft Actor-Critic with mutual information optimization.
result Improves hierarchical network exploration through mutual information maximization.
Unified algorithm for tensor decomposition supports multiple loss functions and models.
problem Efficient tensor decomposition for various models and loss functions.
method Hierarchical combination of ADMM and MM for optimization.
result Wide-range applications can be solved by the proposed algorithm.
This paper explains how deep learning performs hierarchical learning efficiently.
problem How deep learning can perform hierarchical learning efficiently.
method Backward feature correction principle and SGD training.
result Deep learning can efficiently train complex hierarchical tasks using SGD.
Extends deep learning for hierarchical data to improve classification accuracy.
problem Classification with costly features in hierarchical data.
method Extended deep reinforcement learning with hierarchical deep sets and softmax.
result Superior performance on seven datasets, including malicious web domain classification.
Model learns sub-goals and low-level policies for hierarchical reinforcement learning.
problem Determining appropriate low-level policies in hierarchical reinforcement learning.
method Unsupervised learning scheme based on asymmetric self-play.
result Obtains performance gains over non-hierarchical approaches.
NCPF model improves traffic data imputation with neural and tensor methods.
problem Pervasive missing data in traffic analysis due to sensor failures and gaps.
method Neural Canonical Polyadic Factorization (NCPF) integrating CP decomposition and deep learning.
result NCPF outperforms state-of-the-art baselines in urban traffic datasets.
HCRL learns hierarchical embeddings from deep embeddings of hierarchy components.
problem Flat clustering limits cohesive instance relations in hierarchical data.
method Simultaneously optimizes representation learning and hierarchical clustering in the embedding space.
result HCRL achieves best hierarchical clustering and data reconstruction.
The paper explains implicit regularization in hierarchical tensor factorization and deep CNNs.
problem Understanding implicit regularization in complex neural network architectures.
method Theoretical analysis using dynamical systems to overcome challenges in hierarchy.
result Established implicit regularization towards low hierarchical tensor rank, equivalent to locality in CNNs.
HIP-NN models molecular energies using a deep neural network with hierarchical terms.
problem Accurately predicting molecular energies from quantum calculations.
method HIP-NN decomposes molecular properties into a sum of hierarchical terms generated by a neural network.
result Achieves state-of-the-art performance with 0.26 kcal/mol mean absolute error.
A deep learning approach classifies medical images hierarchically.
problem Limitations of traditional supervised classifiers in medical image classification.
method Hierarchical Medical Image Classification (HMIC) using deep learning models.
result HMIC achieved better performance in classifying medical images hierarchically.
Hierarchical CNNs improve image recognition with fewer parameters.
problem Difficulty in analyzing deep neural networks.
method Structured deep convolutional networks with progressively higher dimensional attributes learned from data.
result Hierarchical networks achieve comparable precision to state-of-the-art networks with fewer parameters.
Tree-CNN adapts to new data by growing hierarchically.
problem Incremental learning for evolving datasets.
method Hierarchical deep convolutional neural network.
result Significant reduction in training effort with competitive accuracy.
Linear RC shows hierarchical temporal patterns in state signals.
problem Understanding hierarchical temporal representations in deep RNNs.
method Used linear recurrent units and frequency analysis on state signals.
result Linear RC reveals intrinsic hierarchical temporal structure.
Efficiently annotates hierarchical structure in images using 2AFC testing and deep metric learning.
problem Lack of efficient methods for hierarchical annotation of high-dimensional data like images.
method Two-alternative-forced-choice (2AFC) testing and deep metric learning for embedding data in semantic space.
result Successfully hierarchically clusters data, achieving finer granularity than original labels.
Combines CNN and RNN for hierarchical image classification.
problem Hierarchical relations between image categories are not captured by flat classifiers.
method Uses a CNN for feature extraction and an RNN for capturing hierarchical class relations. Incorporates residual learning.
result Hierarchical networks outperform state-of-the-art CNNs on a real-world dataset.
Hybrid models combine deep hierarchical and deep neural networks for spatio-temporal data.
problem Complex spatio-temporal data and challenges in modeling process complexity.
method Integrates deep hierarchical models and deep neural networks for spatio-temporal data.
result Illustrates recent hybrid approaches combining elements from DH-DSTMs and DN-DSTMs.
SRHM explains deep learning's hierarchy and insensitivity to transformations.
problem Understanding how deep networks learn hierarchical and invariant representations.
method Introducing sparsity to generative hierarchical models of data.
result Hierarchical representations and insensitivity to transformations correlate strongly with deep network performance.
Deep MF extracts hierarchical features from large data sets.
problem Mining complex, interleaved features in large data sets.
method Deep matrix factorization models and algorithms.
result Deep MF achieves outstanding performance on unsupervised tasks.
Deep learning method for comparing hierarchical models.
problem Intractability of Bayesian model comparison for hierarchical models.
method Amortized inference deep learning method for probabilistic programs.
result Excellent amortized inference across all BMC settings.
New method approximates high-dimensional probability densities efficiently.
problem Approximating high-dimensional probability densities accurately and efficiently.
method Hierarchical tensor-network approach using randomized SVD and linear equations.
result The method effectively approximates high-dimensional densities with linear complexity.
Convolutional networks outperform shallow classifiers on certain tasks due to hierarchical structure.
problem Understanding why convolutional networks outperform shallow classifiers on specific tasks.
method Approximation theory, visual tasks with deterministic scrambling, and network performance evaluation.
result Hierarchical structure is crucial for convolutional networks' performance on certain tasks, but not all.
The paper uses deep learning to detect financial market regimes from correlation matrices.
problem Detecting financial market regimes from correlation dynamics.
method Representation learning on block hierarchical SPD correlation matrices using SPDNet, SPD-NetBN, and U-SPDNet models.
result Deep learning models overfit in financial market data, misleading performance metrics.
DPGDS models sequential count data with deep hierarchical structure and temporal dependencies.
problem Modeling sequentially observed multivariate count data with hierarchical and temporal dependencies.
method Developed deep Poisson-gamma dynamical systems with data augmentation and MCMC inference.
result Demonstrated excellent predictive performance and interpretable latent structure.
MDMA provides closed-form marginals and conditionals for deep networks.
problem Lack of closed-form marginals and conditionals in deep neural models.
method MDMA architecture combining deep scalar representations and hierarchical tensor decompositions.
result MDMA outperforms state-of-the-art models in tasks requiring marginalization and conditional inference.
Deep learning uses complex networks for high-dimensional data.
problem Computational inefficiency in training deep learning models.
method Use of hierarchical latent variables, efficient linear algebra, SGD optimization, and batch sampling.
result Efficient training and inference possible with optimized algorithms.
Paper proposes a GPU-based system for training massive deep learning models in ads systems.
problem Training massive deep learning models with terabyte-scale parameters in ads systems.
method Hierarchical GPU parameter server with 3-layer storage (GPU High-Bandwidth Memory, CPU main memory, SSD).
result 4-node hierarchical GPU parameter server trains a model 2X faster than a 150-node in-memory system.
New algorithm B++&C improves hierarchical clustering on large deep embedding datasets.
problem Scaling up hierarchical clustering to massive datasets of deep embeddings.
method Proposes B++&C algorithm for practical hierarchical clustering, introduces B2SAT&C for theoretical approximation.
result Achieves 5%/20% improvement on MW/CKMM objectives compared to classic methods.
KT combines treelets with kernel functions for hierarchical clustering.
problem Hierarchical clustering of non-numeric data.
method Combines treelets and kernel functions to handle non-numeric data.
result KT effectively clusters non-numeric data.
Generalizes information theory for hierarchical partitions.
problem Understanding hierarchical decomposition of complex systems.
method Introducing a generalization of information theory for hierarchical partitions, revisiting Hierarchical Mutual Information (HMI), and proving its bounds and transformations.
result Derives hierarchical generalizations of information-theoretic quantities, including a non-metric variation of information.
The paper explores tensor decompositions in deep learning models.
problem Compressing parameter space and creating richer representations.
method Tensor decompositions applied to deep learning models.
result Tensor methods can yield richer adaptive representations of complex data.