On Limitation of Transformer for Learning HMMs
Fuente:
arXiv
Saved in:
| Main Authors: | Hu, Jiachen, Liu, Qinghua, Jin, Chi |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Most Likely Sequence Generation for $n$-Grams, Transformers, HMMs, and Markov Chains, by Using Rollout Algorithms
by: Li, Yuchao, et al.
Published: (2024)
by: Li, Yuchao, et al.
Published: (2024)
Fraud Detection Through Large-Scale Graph Clustering with Heterogeneous Link Transformation
by: Liu, Chi
Published: (2025)
by: Liu, Chi
Published: (2025)
Multimodal Negative Learning
by: Gong, Baoquan, et al.
Published: (2025)
by: Gong, Baoquan, et al.
Published: (2025)
On Limitations of the Transformer Architecture
by: Peng, Binghui, et al.
Published: (2024)
by: Peng, Binghui, et al.
Published: (2024)
In-Context Learning for Non-Stationary MIMO Equalization
by: Jiang, Jiachen, et al.
Published: (2025)
by: Jiang, Jiachen, et al.
Published: (2025)
FedTrans: Efficient Federated Learning via Multi-Model Transformation
by: Zhu, Yuxuan, et al.
Published: (2024)
by: Zhu, Yuxuan, et al.
Published: (2024)
Selective Learning: Towards Robust Calibration with Dynamic Regularization
by: Han, Zongbo, et al.
Published: (2024)
by: Han, Zongbo, et al.
Published: (2024)
Beyond Frequency: The Role of Redundancy in Large Language Model Memorization
by: Zhang, Jie, et al.
Published: (2025)
by: Zhang, Jie, et al.
Published: (2025)
Fundamental Limits of Prompt Tuning Transformers: Universality, Capacity and Efficiency
by: Hu, Jerry Yao-Chieh, et al.
Published: (2024)
by: Hu, Jerry Yao-Chieh, et al.
Published: (2024)
Sci-Reasoning: A Dataset Decoding AI Innovation Patterns
by: Liu, Jiachen, et al.
Published: (2026)
by: Liu, Jiachen, et al.
Published: (2026)
Imagination-Limited Q-Learning for Offline Reinforcement Learning
by: Liu, Wenhui, et al.
Published: (2025)
by: Liu, Wenhui, et al.
Published: (2025)
Limits of Transformer Language Models on Learning to Compose Algorithms
by: Thomm, Jonathan, et al.
Published: (2024)
by: Thomm, Jonathan, et al.
Published: (2024)
Dual-perspective Cross Contrastive Learning in Graph Transformers
by: Yao, Zelin, et al.
Published: (2024)
by: Yao, Zelin, et al.
Published: (2024)
Ratio law: mathematical descriptions for a universal relationship between AI performance and input samples
by: Kang, Boming, et al.
Published: (2024)
by: Kang, Boming, et al.
Published: (2024)
Graph Q-Learning for Combinatorial Optimization
by: Dax, Victoria M., et al.
Published: (2024)
by: Dax, Victoria M., et al.
Published: (2024)
Computational Limits of Low-Rank Adaptation (LoRA) Fine-Tuning for Transformer Models
by: Hu, Jerry Yao-Chieh, et al.
Published: (2024)
by: Hu, Jerry Yao-Chieh, et al.
Published: (2024)
Agentic Transformers Provably Learn to Search via Reinforcement Learning
by: Yang, Tong, et al.
Published: (2026)
by: Yang, Tong, et al.
Published: (2026)
The Belief State Transformer
by: Hu, Edward S., et al.
Published: (2024)
by: Hu, Edward S., et al.
Published: (2024)
On The Statistical Limits of Self-Improving Agents
by: Wang, Charles L., et al.
Published: (2025)
by: Wang, Charles L., et al.
Published: (2025)
Every Node is Different: Dynamically Fusing Self-Supervised Tasks for Attributed Graph Clustering
by: Zhu, Pengfei, et al.
Published: (2024)
by: Zhu, Pengfei, et al.
Published: (2024)
Beyond Prior Limits: Addressing Distribution Misalignment in Particle Filtering
by: Shi, Yiwei, et al.
Published: (2025)
by: Shi, Yiwei, et al.
Published: (2025)
Provably Efficient Exploration in Quantum Reinforcement Learning with Logarithmic Worst-Case Regret
by: Zhong, Han, et al.
Published: (2023)
by: Zhong, Han, et al.
Published: (2023)
Machine Unlearning in Low-Dimensional Feature Subspace
by: Fang, Kun, et al.
Published: (2026)
by: Fang, Kun, et al.
Published: (2026)
CLDG: Contrastive Learning on Dynamic Graphs
by: Xu, Yiming, et al.
Published: (2024)
by: Xu, Yiming, et al.
Published: (2024)
In-Context Algorithm Emulation in Fixed-Weight Transformers
by: Hu, Jerry Yao-Chieh, et al.
Published: (2025)
by: Hu, Jerry Yao-Chieh, et al.
Published: (2025)
On Computational Limits of Modern Hopfield Models: A Fine-Grained Complexity Analysis
by: Hu, Jerry Yao-Chieh, et al.
Published: (2024)
by: Hu, Jerry Yao-Chieh, et al.
Published: (2024)
Generalization Limits of Reinforcement Learning Alignment
by: Shida, Haruhi, et al.
Published: (2026)
by: Shida, Haruhi, et al.
Published: (2026)
SEBA: Sample-Efficient Black-Box Attacks on Visual Reinforcement Learning
by: Huang, Tairan, et al.
Published: (2025)
by: Huang, Tairan, et al.
Published: (2025)
In-Context Decision Transformer: Reinforcement Learning via Hierarchical Chain-of-Thought
by: Huang, Sili, et al.
Published: (2024)
by: Huang, Sili, et al.
Published: (2024)
InjectTST: A Transformer Method of Injecting Global Information into Independent Channels for Long Time Series Forecasting
by: Chi, Ce, et al.
Published: (2024)
by: Chi, Ce, et al.
Published: (2024)
Rethinking Transformers in Solving POMDPs
by: Lu, Chenhao, et al.
Published: (2024)
by: Lu, Chenhao, et al.
Published: (2024)
DeltaEvolve: Accelerating Scientific Discovery through Momentum-Driven Evolution
by: Jiang, Jiachen, et al.
Published: (2026)
by: Jiang, Jiachen, et al.
Published: (2026)
Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models
by: Hao, Yifan, et al.
Published: (2025)
by: Hao, Yifan, et al.
Published: (2025)
ElastiFormer: Learned Redundancy Reduction in Transformer via Self-Distillation
by: Liu, Junzhang, et al.
Published: (2024)
by: Liu, Junzhang, et al.
Published: (2024)
Transformer Approximations from ReLUs
by: Hu, Jerry Yao-Chieh, et al.
Published: (2026)
by: Hu, Jerry Yao-Chieh, et al.
Published: (2026)
Adaptive Graph Learning with Transformer for Multi-Reservoir Inflow Prediction
by: Hu, Pengfei, et al.
Published: (2025)
by: Hu, Pengfei, et al.
Published: (2025)
On the Learn-to-Optimize Capabilities of Transformers in In-Context Sparse Recovery
by: Liu, Renpu, et al.
Published: (2024)
by: Liu, Renpu, et al.
Published: (2024)
Is Meta-Learning Out? Rethinking Unsupervised Few-Shot Classification with Limited Entropy
by: Guan, Yunchuan, et al.
Published: (2025)
by: Guan, Yunchuan, et al.
Published: (2025)
Transformers Provably Learn Chain-of-Thought Reasoning with Length Generalization
by: Huang, Yu, et al.
Published: (2025)
by: Huang, Yu, et al.
Published: (2025)
Renaissance of RNNs in Streaming Clinical Time Series: Compact Recurrence Remains Competitive with Transformers
by: Tong, Ran, et al.
Published: (2025)
by: Tong, Ran, et al.
Published: (2025)
Similar Items
-
Most Likely Sequence Generation for $n$-Grams, Transformers, HMMs, and Markov Chains, by Using Rollout Algorithms
by: Li, Yuchao, et al.
Published: (2024) -
Fraud Detection Through Large-Scale Graph Clustering with Heterogeneous Link Transformation
by: Liu, Chi
Published: (2025) -
Multimodal Negative Learning
by: Gong, Baoquan, et al.
Published: (2025) -
On Limitations of the Transformer Architecture
by: Peng, Binghui, et al.
Published: (2024) -
In-Context Learning for Non-Stationary MIMO Equalization
by: Jiang, Jiachen, et al.
Published: (2025)