MEANTIME improves sequential recommendation by using multi-temporal embeddings and attention mechanisms.
problem Limited use of timestamp information and information bottleneck in sequential recommendation models.
method MEANTIME employs multiple types of temporal embeddings and attention mechanisms to capture diverse patterns from user behavior sequences.
result MEANTIME outperforms state-of-the-art sequential recommendation methods.
Improves text clustering by incorporating sequential features and word embeddings.
problem Lack of sequential information and synonym handling in current text clustering methods.
method SiDPMM model that models documents as joint of bags of words, sequential features, and word embeddings.
result Significant improvement in performance and accurate inference of cluster numbers.
Detects data drift in deep learning models using neural embeddings.
problem Detecting changes in data distribution in deep learning models.
method Formulates drift detection in a sequential decision framework and introduces a loss function to balance false alarms and quick detection.
result Demonstrates improved ability to balance false alarms and quick detection in change detection.
A novel signature method improves sequential data prediction accuracy.
problem Improving prediction accuracy for sequential and temporal data.
method Signature method based on rough path theory, with a specific embedding called lead-lag.
result Lead-lag embedding consistently outperforms other embeddings across various datasets and algorithms.
New method uses Fokker-Planck equation for sampling and inference.
problem Intractability of evaluating probability density in practical applications.
method Reformulates Fokker-Planck equation as a particle flow method, using velocity field.
result Turns intractable density evaluation into an advantage for variational inference, kernel mean embeddings, and sequential Monte Carlo.
Paper tackles embedding attributed sequences in unsupervised learning.
problem Mining tasks over attributed sequences with dependencies between sequences and attributes.
method Proposes a deep multimodal learning framework, NAS, for unsupervised learning of attributed sequences.
result NAS produces task-independent embeddings for various mining tasks on real-world datasets.
ComiRec framework predicts user interests for personalized recommendations.
problem Predicting user interests from sequential behavior data.
method ComiRec framework captures multiple user interests and balances recommendation accuracy and diversity.
result ComiRec achieves significant improvements over state-of-the-art models in sequential recommendation.
A new method estimates rare failure events in complex systems.
problem Estimating the probability of rare failure events in non-linear systems.
method Stochastic Spectral Embedding (SSE) combined with modifications for efficient rare event estimation.
result Rare failure probability decomposed into conditional probabilities for easier computation.
Graph classification model learns sequentially from graph structure.
problem Graph classification based on structural information.
method Sequential embedding and variational regularization.
result State-of-the-art classification results on molecular datasets.
Framework uses experience replay to prevent deep networks from forgetting past tasks.
problem Deep networks forget past tasks after learning new ones in sequential multitask learning.
method Generative model that couples current task with past learned tasks through a discriminative embedding space.
result Framework learns a shared abstract distribution across all tasks, preventing catastrophic forgetting.
A new algorithm optimizes unknown functions with noisy data and unmatched features.
problem Sequentially maximizing a function with unknown and noisy data and features not under control.
method Bayesian conditional mean embedding and Gaussian process for uncertainty.
result Empirically outperforms state-of-the-art algorithms.
SANST uses self-attentive networks with spatial and temporal embeddings for better POI recommendations.
problem Next point-of-interest (POI) recommendation for users based on their history.
method SANST incorporates spatio-temporal patterns into self-attentive networks.
result SANST outperforms state-of-the-art models by up to 13.65% in nDCG@10.
Proposes estimators for complex dose-response curves using kernel methods.
problem Estimating complex dose-response curves with continuous treatments, mediators, and covariates.
method Kernel ridge regression with sequential kernel embedding technique.
result Simple estimators for mediated and time-varying dose response curves with nonasymptotic uniform rates.
Diffusion models generate music sequences without autoregressive loops.
problem Generating music sequences from symbolic data using diffusion models.
method Parameterize discrete symbolic data in continuous latent space, train diffusion model, generate sequences through reverse process.
result Strong unconditional generation and post-hoc conditional infilling compared to autoregressive models.
In this work, a novel sequential Monte Carlo filter is introduced which aims at efficient sampling of high-dimensional state spaces with a limited number of particles. Particles are pushed forward from the prior to the posterior density using a sequence of mappings that minimizes the Kullback-Leibler divergence between…
A new method for creating simpler models from complex ones.
problem Creating accurate approximations of complex models at reduced costs.
method Sequential adaptive surrogate modeling based on locally spectral expansions.
result Stochastic spectral embedding (SSE) shows good approximation capabilities and scalability.
New model preserves graph structure in large datasets.
problem Lack of permutation invariance in graph generation models for large graphs.
method Uses graph embeddings to create a scalable generative model.
result Model maintains structure in large graphs without losing invariance.
Proposes a new model for online anomaly detection in multivariate time series.
problem Inaccurate anomaly detection in multivariate time series due to spurious correlations and lack of temporal causality.
method Clusters channels based on correlations, embeds each cluster, and integrates information through a causal mixer while maintaining temporal causality.
result Consistently superior performance across six public benchmark datasets.
This paper proposes an out-of-sample extension framework for a global manifold learning algorithm (Isomap) that uses temporal information in out-of-sample points in order to make the embedding more robust to noise and artifacts. Given a set of noise-free training data and its embedding, the proposed framework extends t…
E-commerce system predicts customer interest in sequentially ordered products.
problem Modeling customer interest in sequentially ordered products.
method Embed purchased items with Word2Vec and model the sequence with LSTM RNN.
result The recommender system outperforms its predecessor in click-through rate.
Combines PCA and CEM for fast clustering and embedding.
problem Dimensionality and slow EM algorithm convergence for clustering.
method Combines PCA and CEM for simultaneous clustering and data embedding.
result Demonstrates improved clustering and embedding performance.
SOR-Mamba improves Mamba for robust time series forecasting by minimizing channel order bias.
problem Robust time series forecasting with Mamba's sequential order bias.
method SOR-Mamba incorporates regularization to minimize channel order discrepancy and introduces CCM for channel correlation preservation.
result SOR-Mamba enhances robustness to channel order and improves forecasting accuracy.
This article presents the use of Answer Set Programming (ASP) to mine sequential patterns. ASP is a high-level declarative logic programming paradigm for high level encoding combinatorial and optimization problem solving as well as knowledge representation and reasoning. Thus, ASP is a good candidate for implementing p…
ODE2VAE learns latent dynamics for sequential data.
problem Learning latent dynamics for high-dimensional sequential data.
method Deep generative second order ODE model with Bayesian neural networks.
result State-of-the-art performance in long-term motion prediction and imputation.
D-ETM models document topics over time using embeddings and variational inference.
problem Capturing evolving topic patterns in sequential documents.
method Combines D-LDA and word embeddings, using random walk priors and variational inference.
result D-ETM outperforms D-LDA on document completion tasks, learning more diverse and coherent topics.
Acoustic Neighbor Embeddings map speech and text to fixed dimensions for phonetic confusability.
problem Mapping speech and text to fixed dimensions for phonetic confusability.
method Adapting SNE to sequential inputs, training two encoder neural networks.
result More accurate results with low-dimensional embeddings in word recognition tasks.
Attributes, such as metadata and profile, carry useful information which in principle can help improve accuracy in recommender systems. However, existing approaches have difficulty in fully leveraging attribute information due to practical challenges such as heterogeneity and sparseness. These approaches also fail to c…
Improved IDS using LSTM and embedding for network traffic data.
problem Network security threats and need for IDS to detect attacks.
method Proposes IDS models using LSTM for time-series and embedding for categorical network traffic data.
result Improves binary classification accuracy to 99.72%.
This paper proposes an efficient method to train word embeddings for large corpora without synchronization.
problem Training word embeddings for large text corpora is computationally expensive and requires synchronization.
method Partition the input space instead of the vocabulary size, using asynchronous training without parameter synchronization.
result Comparable and up to 45% performance improvement in NLP benchmarks with 1/10 the training time.
New method improves reinforcement learning generalization.
problem Few environments lead to poor generalization in reinforcement learning.
method Integrates sequential structure into representation learning, using a policy similarity metric (PSM) and contrastive embeddings (PSEs).
result PSEs improve generalization across various benchmarks.
DARTS optimizes covariate selection in trials with limited data.
problem Limited budget for high-dimensional pretreatment data.
method Dynamic Adaptive Rerandomization via Thompson Sampling (DARTS).
result DARTS efficiently concentrates budget on informative features.
A simple algorithm improves model generalization in expert advice settings.
problem Improving model generalization in expert advice settings.
method A naive aggregation algorithm for point estimations of high-dimensional nonlinear functions.
result Conditions for optimal parameter estimates through sequential mixing distribution strategies.
Expected signatures map data streams to lower dimensions, improving ML performance.
problem Leveraging model-free embeddings for domain-agnostic machine learning.
method Expected signatures map data streams to lower dimensions, with convergence results bridging empirical and theoretical estimators.
result A modified expected signature estimator with lower mean squared error for martingale processes.
Mining frequent sequential patterns consists in extracting recurrent behaviors, modeled as patterns, in a big sequence dataset. Such patterns inform about which events are frequently observed in sequences, i.e. what does really happen. Sometimes, knowing that some specific event does not happen is more informative than…
Paper proposes a new method for learning compact representations of sequential data.
problem Learning compact representations of sequential data capturing spatio-temporal cues.
method Contrastive representation learning via adversarial optimal transport on the Grassmann manifold.
result Empirical results show competitive performance in human action recognition.
Motivated by the hinge structure present in protein chains and other molecular conformations, we study the singularities of certain maps associated to body-and-hinge and panel-and-hinge chains. These are sequentially articulated systems where two consecutive rigid pieces are connected by a hinge, that is, a codimension…
PERCEPT detects changes in high-dimensional data streams using topological data analysis.
problem Detecting changes in high-dimensional data streams, especially when embedded in a low-dimensional space.
method Leverages topological data analysis to learn embedded topology as a point cloud via persistence diagrams, then applies non-parametric monitoring for detecting changes.
result Demonstrates efficient detection of online changes from high-dimensional data streams.
Proposes SNML for selecting word2vec Skip-gram dimensionality.
problem Selecting optimal dimensionality for word2vec Skip-gram models.
method Information criteria (AIC, BIC, SNML) applied to SG and SG Negative Sampling models.
result SNML outperforms AIC and BIC, selecting closer optimal dimensionality.
HyFAD improves time series imputation by combining time and frequency diffusion.
problem Improve time series imputation by handling frequency-sensitive denoising and balancing global and local dynamics.
method HyFAD is a hybrid time-frequency diffusion model with frequency-aware embedding, built on DDPM paradigm.
result HyFAD achieves state-of-the-art performance in time series imputation.
The Variational Autoencoder (VAE) has proven to be an effective model for producing semantically meaningful latent representations for natural data. However, it has thus far seen limited application to sequential data, and, as we demonstrate, existing recurrent VAE models have difficulty modeling sequences with long-te…
This paper presents a new framework for distributed training of embeddings using graph analytics.
problem Training embeddings is computationally intensive and challenging to distribute.
method Formulated Any2Vec training algorithm as a graph application, leveraging D-Galois for dynamic graph generation and repartitioning, and incorporating novel communication optimizations.
result GraphAny2Vec matches state-of-the-art shared-memory implementations in accuracy and provides significant speedups.
Efficient RL method uses SSL for small labeled data in health outcomes.
problem Lack of precise health outcome data for reinforcement learning.
method Semi-supervised learning approach for Q-learning and value estimation.
result Method efficiently estimates Q-function and value function, robust to mis-specification.
MPE models traffic trajectory data to predict next locations.
problem Predicting next locations from traffic trajectory data.
method Mobility pattern embedding model MPE.
result MPE significantly outperforms state-of-the-art methods in next location prediction.
CILF learns adaptive embeddings for class-incremental learning with novel class detection and model update.
problem Handling unknown classes and model update in streaming data with new classes.
method CILF uses decoupled prototype based loss for intra-class and inter-class structure improvement, and a learnable curriculum clustering operator for adaptive embedding.
result CILF effectively detects multiple novel classes and mitigates embedding confusion, while updating the model without catastrophic forgetting.
Hyperbolic spaces have recently gained momentum in the context of machine learning due to their high capacity and tree-likeliness properties. However, the representational power of hyperbolic geometry is not yet on par with Euclidean geometry, mostly because of the absence of corresponding hyperbolic neural network lay…
New kernels for set optimization improve combinatorial optimization without jitter.
problem Optimizing with set inputs, especially in combinatorial problems.
method Developed new set kernels based on Reproducing Kernel Hilbert Space embeddings.
result Subclasses of Deep Embedding kernels are strictly positive definite, enabling combinatorial optimization.
Calendar graph neural networks model user behavior with location and time data.
problem Modeling user behavior with location and time information for demographic prediction.
method Graph neural networks with a tripartite network of items, sessions, and locations, and a hierarchical calendar network.
result User embeddings preserve spatial and temporal patterns of various periodicity.
Novel method interprets black box predictions using Fisher kernels and SBQ.
problem Interpreting black box models' predictions.
method Fisher kernels as feature embeddings, Sequential Bayesian Quadrature for selection.
result Method efficiently handles any subset of test predictions.