Poster "sequence modeling" Papers
29 papers found
Conference
BlockScan: Detecting Anomalies in Blockchain Transactions
Jiahao Yu, Xian Wu, Hao Liu et al.
Competition Dynamics Shape Algorithmic Phases of In-Context Learning
Core Francisco Park, Ekdeep Singh Lubana, Hidenori Tanaka
Controllable Generation via Locally Constrained Resampling
Kareem Ahmed, Kai-Wei Chang, Guy Van den Broeck
DeltaProduct: Improving State-Tracking in Linear RNNs via Householder Products
Julien Siems, Timur Carstensen, Arber Zela et al.
Drama: Mamba-Enabled Model-Based Reinforcement Learning Is Sample and Parameter Efficient
Wenlong Wang, Ivana Dusparic, Yucheng Shi et al.
Enhancing the Maximum Effective Window for Long-Term Time Series Forecasting
Jiahui Zhang, Zhengyang Zhou, Wenjie Du et al.
Evolutionary Reasoning Does Not Arise in Standard Usage of Protein Language Models
Yasha Ektefaie, Andrew Shen, Lavik Jain et al.
Hankel Singular Value Regularization for Highly Compressible State Space Models
Paul Schwerdtner, Jules Berman, Benjamin Peherstorfer
Learning Video-Conditioned Policy on Unlabelled Data with Joint Embedding Predictive Transformer
Hao Luo, Zongqing Lu
Limits of Deep Learning: Sequence Modeling through the Lens of Complexity Theory
Nikola Zubic, Federico Soldà, Aurelio Sulser et al.
Longhorn: State Space Models are Amortized Online Learners
Bo Liu, Rui Wang, Lemeng Wu et al.
MeshAnything V2: Artist-Created Mesh Generation with Adjacent Mesh Tokenization
Yiwen Chen, Yikai Wang, Yihao Luo et al.
Neural Attention Search
Difan Deng, Marius Lindauer
Parallel Sequence Modeling via Generalized Spatial Propagation Network
Hongjun Wang, Wonmin Byeon, Jiarui Xu et al.
Plug, Play, and Generalize: Length Extrapolation with Pointer-Augmented Neural Memory
Svetha Venkatesh, Kien Do, Hung Le et al.
Scaling Up Liquid-Resistance Liquid-Capacitance Networks for Efficient Sequence Modeling
Mónika Farsang, Radu Grosu
SeerAttention: Self-distilled Attention Gating for Efficient Long-context Prefilling
Yizhao Gao, Zhichen Zeng, DaYou Du et al.
Selective induction Heads: How Transformers Select Causal Structures in Context
Francesco D'Angelo, francesco croce, Nicolas Flammarion
State Space Models are Provably Comparable to Transformers in Dynamic Token Selection
Naoki Nishikawa, Taiji Suzuki
Unsupervised Meta-Learning via In-Context Learning
Anna Vettoruzzo, Lorenzo Braccaioli, Joaquin Vanschoren et al.
An Information-Theoretic Analysis of In-Context Learning
Hong Jun Jeon, Jason Lee, Qi Lei et al.
Efficient World Models with Context-Aware Tokenization
Vincent Micheli, Eloi Alonso, François Fleuret
How Transformers Learn Causal Structure with Gradient Descent
Eshaan Nichani, Alex Damian, Jason Lee
Mamba-ND: Selective State Space Modeling for Multi-Dimensional Data
Shufan Li, Aditya Grover, Harkanwar Singh
Reinformer: Max-Return Sequence Modeling for Offline RL
Zifeng Zhuang, Dengyun Peng, Jinxin Liu et al.
Repeat After Me: Transformers are Better than State Space Models at Copying
Samy Jelassi, David Brandfonbrener, Sham Kakade et al.
Rethinking Decision Transformer via Hierarchical Reinforcement Learning
Yi Ma, Jianye Hao, Hebin Liang et al.
Timer: Generative Pre-trained Transformers Are Large Time Series Models
Yong Liu, Haoran Zhang, Chenyu Li et al.
Universality of Linear Recurrences Followed by Non-linear Projections: Finite-Width Guarantees and Benefits of Complex Eigenvalues
Antonio Orvieto, Soham De, Caglar Gulcehre et al.