| Challenge: | Recurrent neural networks have difficulty in parallelization because of their recurrent structure. |
| Approach: | They propose sliced recurrent neural networks (SRNNs) which can be parallelized by slicing sequences into many subsequences. |
| Outcome: | The proposed recurrent neural networks perform better than standard RNNs on six large-scale sentiment analysis datasets. |
Similar Papers
Efficient Sequence Learning with Group Recurrent Networks (N18-1)
Copied to clipboard
| Challenge: | Recurrent neural networks have achieved state-of-the-art results in many artificial intelligence tasks, such as language modeling, neural machine translation and speech recognition. |
| Approach: | They propose an efficient architecture to improve the efficiency of such RNN model training by adopting the group strategy for recurrent layers while exploiting the representation rearrangement strategy between layers as well as time steps. |
| Outcome: | The proposed architecture achieves comparable or better accuracy compared with baselines, with a much smaller number of parameters and at a lower computational cost. |
The Importance of Being Recurrent for Modeling Hierarchical Structure (D18-1)
Copied to clipboard
| Challenge: | Recent work shows that recurrent neural networks can implicitly capture hierarchical information when trained to solve common natural language processing tasks. |
| Approach: | They propose a convolutional sequence-to-sequence model that exploits hierarchical information implicitly. |
| Outcome: | The proposed model is recurrent and non-recurrent, and it can model hierarchical structure implicitly. |
Convolutional Neural Networks with Recurrent Neural Filters (D18-1)
Copied to clipboard
| Challenge: | Convolutional neural networks (CNNs) use recurrent neural networks as convolution filters to capture language compositionality and long-term dependencies. |
| Approach: | They propose to use recurrent neural networks (RNNs) as convolution filters to capture language compositionality and long-term dependencies. |
| Outcome: | The proposed convolutional neural networks achieve state-of-the-art on two sentences and the Stanford Sentiment Treebank. |
Simple Recurrent Units for Highly Parallelizable Recurrence (D18-1)
Copied to clipboard
| Challenge: | recurrent neural networks scale poorly due to the intrinsic difficulty in parallelizing their state computations. |
| Approach: | They propose a simple recurrent unit that provides expressive recurrence and allows highly parallel implementation. |
| Outcome: | The proposed model achieves 5—9x speed-up over cuDNN-optimized LSTM on classification and question answering datasets and delivers stronger results than LS and convolutional models. |
Advancing Regular Language Reasoning in Linear Recurrent Neural Networks (2024.naacl-short)
Copied to clipboard
| Challenge: | Existing linear recurrent neural networks have been used for natural language and long-range modeling for decades. |
| Approach: | They propose a linear recurrent neural network with a block-diagonal transition matrix and a transition matrix for LRNNs. |
| Outcome: | The proposed model is the only one capable of performing length extrapolation on regular language tasks such as Sum, Even Pair, and Modular Arithmetic. |
On the Practical Computational Power of Finite Precision RNNs for Language Recognition (P18-2)
Copied to clipboard
| Challenge: | Recurrent Neural Networks (RNNs) are famously known to be Turing complete, but this relies on infinite precision in the states and unbounded computation time. |
| Approach: | They propose to use LSTM and Elman-RNN with ReLU activation to study RNNs . they show that LS and ReLU-RNns can easily implement counting behavior . |
| Outcome: | The LSTM and the Elman-RNN with ReLU activation are stronger than the RNN with squashing activation and the GRU. |
A Lightweight Recurrent Network for Sequence Modeling (P19-1)
Copied to clipboard
| Challenge: | Recent studies show that recurrent networks suffer from severe computational inefficiency due to weak parallelization. |
| Approach: | They propose a lightweight recurrent network (LRN) that uses input and forget gates to handle long-range dependencies and gradient vanishing and explosion. |
| Outcome: | The proposed recurrent network yields the best running efficiency on six NLP tasks. |
Neural Tensor Networks with Diagonal Slice Matrices (N18-1)
Copied to clipboard
| Challenge: | A large number of parameters can cause overfitting and a long training time for neural tensor networks (NTNs). |
| Approach: | They propose two new parameter reduction techniques to reduce the number of parameters in an NTN without diminishing its expressiveness. |
| Outcome: | The proposed models learn better and faster than the original (R)NTNs. |
RWKV: Reinventing RNNs for the Transformer Era (2023.findings-emnlp)
Copied to clipboard
Bo Peng, Eric Alcaide, Quentin Anthony, Alon Albalak, Samuel Arcadinho, Stella Biderman, Huanqi Cao, Xin Cheng, Michael Chung, Leon Derczynski, Xingjian Du, Matteo Grella, Kranthi Gv, Xuzheng He, Haowen Hou, Przemyslaw Kazienko, Jan Kocon, Jiaming Kong, Bartłomiej Koptyra, Hayden Lau, Jiaju Lin, Krishna Sri Ipsit Mantri, Ferdinand Mom, Atsushi Saito, Guangyu Song, Xiangru Tang, Johan Wind, Stanisław Woźniak, Zhenyuan Zhang, Qinghua Zhou, Jian Zhu, Rui-Jie Zhu
| Challenge: | recurrent neural networks struggle to match the performance of Transformers due to limitations in parallelization and scalability. |
| Approach: | They propose a model architecture that combines the efficient parallelizable training of transformers with the efficient inference of RNNs. |
| Outcome: | The proposed model performs on par with similarly sized RNNs, suggesting future work can leverage this architecture to create more efficient models. |
lrnnx: A library for Linear RNNs (2026.eacl-srw)
Copied to clipboard
Karan Bania, Soham Kalburgi, Manit Tanwar, null Dhruthi, Aditya Nagarsekar, Harshvardhan Mestha, Naman Chibber, Raj Deshmukh, Anish Sathyanarayanan, Aarush Rathore, Pratham Chheda
| Challenge: | Existing implementations of linear recurrent neural networks are fragmented across different software frameworks . existing implementations often require custom CUDA kernels or lack publicly available code altogether . |
| Approach: | a unified software library implements several modern LRNN architectures under a common interface. lrnnx aims to improve accessibility, reproducibility, and extensibility of LRnn research and applications. |
| Outcome: | lrnnx aims to improve accessibility, reproducibility, and extensibility of LRNN research and applications. |