BiHDTrans: binary hyperdimensional transformer for efficient multivariate time series classification

arXiv:2509.24425 · cs.LG, cs.AR · Submitted 2025-09-29 · Read on arXiv

cs.LG, cs.AR

Submitted: 2025-09-29

Updated: 2026-09-10

Comments: This version has been withdrawn due to methodological limitations and flawed experimental setups. The authors are conducting a comprehensive revision and restructuring of the work

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Transformer — 73× in this paper · explained in 2 episodes
• Accuracy — 22× in this paper · explained in 1 episode
• Quantization — 11× in this paper · explained in 12 episodes
• Robustness — 5× in this paper · explained in 5 episodes
• Transformer Architecture — 3× in this paper · explained in 2 episodes
• Transformer Models — 3× in this paper · explained in 2 episodes
• Adam — 2× in this paper · explained in 2 episodes
• Adaptability — 2× in this paper · explained in 1 episode
• Associative Memory — 2× in this paper · explained in 2 episodes
• Clustering — 2× in this paper · explained in 2 episodes
• Edge Deployment — 2× in this paper · explained in 2 episodes
• Edge Devices — 2× in this paper · explained in 2 episodes
• Hamming Distance — 2× in this paper · explained in 2 episodes
• IEEE Design Automation Conference — 2× in this paper
• Representation Learning — 2× in this paper · explained in 2 episodes
• Semi-supervised Learning — 2× in this paper · explained in 4 episodes
• Sparsity — 2× in this paper · explained in 4 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Computational overhead — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• IEEE International Conference on Rebooting Computing — 1× in this paper
• Knowledge Distillation — 1× in this paper · explained in 11 episodes
• Resource Constraints — 1× in this paper · explained in 3 episodes
• Time Series Forecasting — 1× in this paper · explained in 3 episodes

Sources

• Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation

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