A new model for efficient sequential pattern mining without specific encoding schemes.
problem Mining relevant sequential patterns efficiently and effectively.
method Subsequence interleaving model based on probabilistic sequence database.
result Efficient inference through submodular optimization, resulting in low spuriousness and redundancy.
Paper proposes a new model and methods for robustly de-interleaving HMP mixtures.
problem Lack of robustness to non-ideal situations in existing HMP mixtures de-interleaving methods.
method Designs a generative model, formulates de-interleaving as posterior inference, develops exact and approximate inference methods, derives error probability bounds.
result Proposed methods are highly effective and robust for non-ideal situations, outperforming baseline methods.
Illustrates interleaved learning with Kalman Filter for linear least squares.
problem Improving machine learning algorithms through interleaved learning.
method Simple statistical and optimization framework based on Kalman Filter.
result Demonstrates the effectiveness of interleaved learning.
Formula for interleaving distance of rectangle persistence modules.
problem Calculating distances between rectangle persistence modules.
method Formulas based on rectangle geometry, extended to decomposable modules.
result Closed formulas for interleaving and bottleneck distances.
New algorithm learns optimal ad pricing in online advertising.
problem Optimizing ad pricing in a waterfall system.
method Online learning algorithm interleaves learning and optimization.
result Sublinear regret compared to traditional methods.
The paper tightens bounds on distances between Reeb graphs.
problem Certifying quasi-universality of distances between Reeb graphs.
method Establishes tight bi-Lipschitz bounds for various distances.
result Proves strict universality of the functional contortion distance for contour trees and coincides with interleaving distance for merge trees.
Study exact Lagrangian cobordisms in cotangent bundles, proving bounds on sheaf interleaving distance and shadow distance.
problem Understanding Lagrangian cobordisms and their properties in cotangent bundles.
method Use microlocal theory of sheaves, sheaf quantization, and cone decompositions.
result Interleaving distance of sheaves is bounded by the shadow distance of the cobordism.
Study integrates attentional and spacing factors to improve category learning models.
problem Understanding the impact of training sequences on category learning.
method Introduced a novel integration of attentional factors and spacing into logistic knowledge tracing models.
result Enhanced model predicts students' learning outcomes better than existing models.
Stable density-based clustering via multiparameter persistence.
problem Density-based clustering stability to data perturbations.
method Degree-Rips construction, correspondence-interleaving distance, multiparameter stability analysis.
result Persistable pipeline yields stable, consistent density-based clustering.
Interleaved RNNs detect fraud without costly features.
problem Real-time fraud detection in payment cards.
method Use interleaved sequence RNNs for fraud detection.
result Interleaved RNNs outperform state-of-the-art models in fraud detection.
Improved hybrid acoustic model using interleaved self-attention and convolution.
problem Limited application of transformer in hybrid acoustic models.
method Proposed a model structure with interleaved self-attention and 1D convolution.
result Competitive recognition results on Librispeech dataset.
Improves text-to-speech speed by interleaving character reading and audio synthesis.
problem Latency in text-to-speech models limits their use in time-sensitive tasks.
method Reinforcement learning to train an agent to choose the order of character reading and audio synthesis.
result The proposed method successfully balances latency and audio quality.
DeepDIVE disentangles input into marginal and conditional distributions for multi-task learning.
problem Challenges in multi-task learning due to conflicting objectives.
method Inspired by probability theory, DeepDIVE uses a variational autoencoder with disentangled features and cross-attention mechanism.
result DeepDIVE disentangles input and improves forecast accuracy compared to baseline models.
Invariants measure letter interleaving in groups, detecting group dimensions.
problem Detecting group dimensions in arbitrary groups.
method Defining letter-braiding invariants from cochain models of spaces with prescribed fundamental groups.
result Letter-braiding invariants are complete invariants of group dimension series.
Pooling is not essential for image classification stability.
problem The necessity of pooling for image classification stability.
method Rigorous empirical testing of CNNs without pooling.
result Pooling is neither necessary nor sufficient for optimal deformation stability in CNNs.
Graphs represent gene segment organization, revealing complex interrelationships in a scrambled genome.
problem Understanding gene segment organization and interrelationships in a scrambled genome.
method Directed graphs representing gene segments and their relationships, with graph properties mapped to higher-dimensional space for analysis.
result Emerging star-like structures indicate complex interrelationships, including segments from multiple genes interleaving or overlapping.
Multiple gossip steps improve decentralized optimization convergence.
problem Efficiently optimizing large-scale machine learning models with limited communication.
method Integrates multiple gossip steps between gradient descent iterations in compressed decentralized optimization.
result Convergence to within ε of the optimal value for smooth non-convex objectives.
A method for continual learning using world models in reinforcement learning.
problem Catastrophic forgetting in lifelong learning with neural networks.
method Interleaving internally generated episodes of past experiences (pseudo-rehearsal) with external environment's observations.
result Consistent reduction in temporal prediction loss compared to non-interleaved learning.
Unified model integrates text and time series for financial forecasting.
problem Challenges in integrating complementary modalities for improved forecasting.
method Modality-specific experts and cross-modal alignment framework.
result State-of-the-art performance on financial forecasting task.
Automates reparameterization in probabilistic programs for better inference.
problem Performance of inference algorithms is affected by parameterization, requiring manual and non-intuitive transformations.
method Automates non-centring and related reparameterizations using interleaved sampling and variational formulation.
result New inference algorithms yield more efficient samplers than fixed parameterizations across various models.
Paper proposes UIS-RNN for fully supervised speaker diarization.
problem Speaker diarization with unknown number of speakers and time-stamped labels.
method Parameter-sharing RNNs with interleaved states, ddCRP for clustering.
result 7.6% diarization error rate on NIST SRE 2000 CALLHOME.
Learning linear combinations of multiple kernels is an appealing strategy when the right choice of features is unknown. Previous approaches to multiple kernel learning (MKL) promote sparse kernel combinations to support interpretability and scalability. Unfortunately, this 1-norm MKL is rarely observed to outperform tr…
New algorithms learn best actions without taking bad ones in combinatorial problems.
problem Learning best actions without taking bad ones in combinatorial problems.
method Formalized as stochastic combinatorial semi-bandits with exchangeable actions, designed efficient learning algorithms, bounded n-step regret.
result Real-world experiments show algorithms can learn without violating strict constraints.
ForecastNet uses a time-variant deep feed-forward neural network for better multi-step-ahead time series forecasting.
problem Time-invariant architectures limit multi-step-ahead forecasting.
method ForecastNet employs a deep feed-forward architecture with time-variant parameters and interleaved outputs.
result ForecastNet outperforms other models on multi-step-ahead time series forecasting tasks.
ICQ improves high-dimensional similarity search without sacrificing precision.
problem High-dimensional similarity search is computationally expensive.
method Interleaved Composite Quantization (ICQ) reduces code length and quantization error.
result ICQ achieves fast similarity search without using shorter codes.
We study the explore-exploit tradeoff in distributed cooperative decision-making using the context of the multiarmed bandit (MAB) problem. For the distributed cooperative MAB problem, we design the cooperative UCB algorithm that comprises two interleaved distributed processes: (i) running consensus algorithms for estim…
Unified framework connects deformation theory and derived categories for multiparameter persistence.
problem Algebraic complexity of multiparameter persistence modules hinders classification, stability, and interpretability.
method Combines deformation theory and derived categories to study multiparameter persistence geometrically.
result Unified conjecture relating interleaving distance to derived convolution metrics established.
Proposes CLSM for better subsequence generation in music sequences.
problem Editing subsequences in music sequences without losing context.
method Context-informed prior and decoder for generative model, context position-informed encoder for inference.
result Contextual latent space is smoother in interpolation and generates higher quality samples.
We describe computationally efficient methods for learning mixtures in which each component is a directed acyclic graphical model (mixtures of DAGs or MDAGs). We argue that simple search-and-score algorithms are infeasible for a variety of problems, and introduce a feasible approach in which parameter and structure sea…
Paper proposes neural network for LDPC coded DCO-OFDM with clipping distortion.
problem Mitigating nonlinear clipping distortion in LDPC coded DCO-OFDM systems.
method Designs a neural network-aided bit-interleaved coded modulation (NN-BICM) receiver to improve log-likelihood ratio (LLR) through backpropagation training.
result Neural network-aided receiver achieves noticeable performance gains over other methods.
Generative model uses graphs to create natural-sounding code.
problem Creating semantically meaningful source code with syntactic and semantic constraints.
method Graph representation for intermediate state, interleaves grammar-driven expansion with graph augmentation and neural message passing.
result Generative model outperforms baselines in generating natural-sounding code.
We consider high-dimensional distribution estimation through autoregressive networks. By combining the concepts of sparsity, mixtures and parameter sharing we obtain a simple model which is fast to train and which achieves state-of-the-art or better results on several standard benchmark datasets. Specifically, we use a…
Paper tackles deinterleaving DNS streams for malware detection.
problem Deinterleaving DNS data streams to extract malware domain sequences.
method Developed a generative model for user request generation and DNS stream interleaving. Evaluated inference strategies including augmented HMMs and LSTMs on synthetic datasets.
result State-of-the-art LSTMs outperform traditional augmented HMMs in deinterleaving DNS streams for malware detection.
A new stable edit distance for Reeb graphs is shown to be universal.
problem Stability and comparability of Reeb graphs under function similarity.
method Defined and proved stability and universality of Reeb graph edit distance.
result Reeb graph edit distance is the most stable and universal among distances.
A new federated learning framework for handling device heterogeneity.
problem Handling device heterogeneity in federated learning.
method Superquantile-based objective with parameterized levels of conformity, optimized using secure aggregation.
result The optimization algorithm converges to a stationary point.
Algorithm finds critical subsequences in ECG beats for classification.
problem Classifying ECG beats accurately and understanding why.
method Optimized implementation of nearest neighbor algorithm with DTW distance.
result Algorithm discovers important subsequences for ECG classification.
Decision Machines embeds decision trees into vector spaces for improved optimization.
problem Overfitting and difficulty in finding optimal decision tree structure.
method Embedding Boolean tests into a binary vector space and representing tree structure as matrices.
result Optimized decision trees with enhanced predictive power.
Enhances time series comparison by simplifying warping paths.
problem Lack of qualitative comparison on top of elastic distance measures.
method Proposes a technique to simplify warping paths for better visualization and interpretation.
result Clearer representation of how subsequences match between time series.
Alpha Zero adapts to continuous action spaces for real-world tasks.
problem Real-world reinforcement learning domains often have continuous action spaces.
method Interleaves tree search and deep learning, extending Alpha Zero for continuous action spaces.
result Preliminary experiments on the Pendulum task show feasibility of the approach.
CAB clusters users for better collaborative recommendation.
problem Improving recommendation performance through collaborative effects.
method Context-dependent clustering of bandits with collaborative effects.
result Proves improved prediction performance over state-of-the-art methods.
Enhances drug discovery by optimizing molecular structures.
problem Accelerate drug discovery through better optimization of precursor molecules.
method Integrates substructure components with atom-level encoding in a fully autoregressive graph decoder.
result Significantly outperforms previous state-of-the-art baselines on molecular optimization tasks.
New method finds local anomalies in time series by considering context information.
problem Finding anomalous subsequence in long time series is difficult.
method Introduces semantic discord, an approach that incorporates context information.
result Significantly outperforms state-of-the-art methods in locating anomalies.
Persistent Legendrian contact homology distinguishes knots using height functional.
problem Distinguishing Legendrian knots in R3. method Persistent homology applied to Chekanov-Eliashberg DGA, with height functional.
result Strong Morse inequalities for persistent Legendrian contact homology.
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.
PATOIS synthesizes code from natural language using learned code idioms.
problem Synthesizing general-purpose source code from natural language specifications is challenging.
method PATOIS uses a neural synthesizer that interleaves high-level and low-level reasoning, incorporating learned code idioms from a corpus.
result Using learned code idioms improves the synthesizer's accuracy on semantic parsing datasets.
Algorithm adapts to shifting domains with minimal label queries.
problem Adaptive learning in online machine learning systems with domain shifts.
method Adaptive algorithm balancing regret and label queries for hidden domains.
result Achieves lower regret compared to uniform and greedy queries.
POLA adapts learning rates for online time series prediction.
problem Adapting to changing data distributions in dynamic environments.
method Adaptive learning rate regulation for recurrent neural networks.
result POLA outperforms other online prediction methods in real-world datasets.
UniShape improves time series classification by selecting relevant subsequences.
problem Classifying time series data requires capturing interpretable shapelets.
method UniShape uses a shape-aware adapter to aggregate multiscale subsequences into class tokens.
result UniShape achieves state-of-the-art classification performance.