GSHAC performs exact HAC on large geographic point sets by building a sparse geodesic graph and proving that connected-component subproblems yield identical results to the dense algorithm for all standard linkages at cut heights below the sparsity threshold.
A transformer-based framework for multivariate time series representation learning,
6 Pith papers cite this work, alongside 66 external citations. Polarity classification is still indexing.
6
Pith papers citing it
66
external citations · external index
citation-role summary
method 1
citation-polarity summary
roles
method 1polarities
use method 1representative citing papers
RoMAE applies rotary positional embeddings to masked autoencoders to enable representation learning and interpolation on continuous positional data across irregular time-series, images, and audio without modality-specific modifications.
A frozen LSTM backbone with FiLM-based few-shot context adaptation estimates tip-level contact forces for deformable swabbing tools across nine surface-tool regimes using only wrist-mounted proprioception.
A TSC framework separates historical attendance sequences from future labels and uses LSTM-FCN with BFL or G-Mean loss to achieve approximately 80% balanced accuracy for proactive absenteeism prediction on simulated data.
AdMem introduces a unified bi-level memory framework with multi-agent automatic generation and reward-based long-term management that improves success on long-horizon LLM agent tasks.
Transformer models classify seven wildlife species from daily GPS trajectories, outperforming LSTM, CNN, and TCN baselines by 8-22 percentage points in balanced accuracy under region-holdout evaluation.
citing papers explorer
-
Scalable Exact Hierarchical Agglomerative Clustering via Sparse Geographic Distance Graphs
GSHAC performs exact HAC on large geographic point sets by building a sparse geodesic graph and proving that connected-component subproblems yield identical results to the dense algorithm for all standard linkages at cut heights below the sparsity threshold.
-
Rotary Masked Autoencoders are Versatile Learners
RoMAE applies rotary positional embeddings to masked autoencoders to enable representation learning and interpolation on continuous positional data across irregular time-series, images, and audio without modality-specific modifications.
-
Context-Aware Force Estimation for Deformable Tool Manipulation in Robotic Environmental Swabbing via Few-Shot Continual Adaptation
A frozen LSTM backbone with FiLM-based few-shot context adaptation estimates tip-level contact forces for deformable swabbing tools across nine surface-tool regimes using only wrist-mounted proprioception.
-
A time-series classification framework for individual-level absenteeism prediction under severe class imbalance
A TSC framework separates historical attendance sequences from future labels and uses LSTM-FCN with BFL or G-Mean loss to achieve approximately 80% balanced accuracy for proactive absenteeism prediction on simulated data.
-
AdMem: Advanced Memory for Task-solving Agents
AdMem introduces a unified bi-level memory framework with multi-agent automatic generation and reward-based long-term management that improves success on long-horizon LLM agent tasks.
-
Transformer-Based Wildlife Species Classification from Daily Movement Trajectories
Transformer models classify seven wildlife species from daily GPS trajectories, outperforming LSTM, CNN, and TCN baselines by 8-22 percentage points in balanced accuracy under region-holdout evaluation.