Bidirectional Long-Range Parser for Sequential Data Understanding

• URL: http://arxiv.org/abs/2404.05210v1
• Date: Mon, 8 Apr 2024 05:45:03 GMT
• Title: Bidirectional Long-Range Parser for Sequential Data Understanding
• Authors: George Leotescu, Daniel Voinea, Alin-Ionut Popa,
• Abstract summary: We introduce BLRP (Bidirectional Long-Range), a novel and versatile attention mechanism designed to increase performance and efficiency on long-sequence tasks. We show the benefits and versatility of our approach on vision and language domains by demonstrating competitive results against state-of-the-art methods.
• Score: 3.76054468268713
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The transformer is a powerful data modelling framework responsible for remarkable performance on a wide range of tasks. However, they are limited in terms of scalability as it is suboptimal and inefficient to process long-sequence data. To this purpose we introduce BLRP (Bidirectional Long-Range Parser), a novel and versatile attention mechanism designed to increase performance and efficiency on long-sequence tasks. It leverages short and long range heuristics in the form of a local sliding window approach combined with a global bidirectional latent space synthesis technique. We show the benefits and versatility of our approach on vision and language domains by demonstrating competitive results against state-of-the-art methods on the Long-Range-Arena and CIFAR benchmarks together with ablations demonstrating the computational efficiency.

Related papers

• EffiCANet: Efficient Time Series Forecasting with Convolutional Attention [12.784289506021265]
  EffiCANet is designed to enhance forecasting accuracy while maintaining computational efficiency. EffiCANet achieves the maximum reduction of 10.02% in MAE over state-of-the-art models.
  arXiv  Detail & Related papers  (2024-11-07T12:54:42Z)
• UmambaTSF: A U-shaped Multi-Scale Long-Term Time Series Forecasting Method Using Mamba [7.594115034632109]
  We propose UmambaTSF, a novel long-term time series forecasting framework. It integrates multi-scale feature extraction capabilities of U-shaped encoder-decoder multilayer perceptrons (MLP) with Mamba's long sequence representation. UmambaTSF achieves state-of-the-art performance and excellent generality on widely used benchmark datasets.
  arXiv  Detail & Related papers  (2024-10-15T04:56:43Z)
• Faster Diffusion Action Segmentation [9.868244939496678]
  Temporal Action Classification (TAS) is an essential task in video analysis, aiming to segment and classify continuous frames into distinct action segments. Recent advances in diffusion models have demonstrated substantial success in TAS tasks due to their stable training process and high-quality generation capabilities. We propose EffiDiffAct, an efficient and high-performance TAS algorithm.
  arXiv  Detail & Related papers  (2024-08-04T13:23:18Z)
• Sparser is Faster and Less is More: Efficient Sparse Attention for Long-Range Transformers [58.5711048151424]
  We introduce SPARSEK Attention, a novel sparse attention mechanism designed to overcome computational and memory obstacles. Our approach integrates a scoring network and a differentiable top-k mask operator, SPARSEK, to select a constant number of KV pairs for each query. Experimental results reveal that SPARSEK Attention outperforms previous sparse attention methods.
  arXiv  Detail & Related papers  (2024-06-24T15:55:59Z)
• Short-Long Convolutions Help Hardware-Efficient Linear Attention to Focus on Long Sequences [60.489682735061415]
  We propose CHELA, which replaces state space models with short-long convolutions and implements linear attention in a divide-and-conquer manner. Our experiments on the Long Range Arena benchmark and language modeling tasks demonstrate the effectiveness of the proposed method.
  arXiv  Detail & Related papers  (2024-06-12T12:12:38Z)
• Efficient and Economic Large Language Model Inference with Attention Offloading [11.698376311689456]
  Transformer-based large language models (LLMs) exhibit impressive performance in generative tasks but introduce significant challenges in real-world serving. This mismatch arises from the autoregressive nature of LLMs, where the generation phase comprises operators with varying resource demands. To enhance the efficiency and cost-effectiveness of LLM serving, we introduce the concept of attention offloading.
  arXiv  Detail & Related papers  (2024-05-03T02:15:15Z)
• On the Long Range Abilities of Transformers [69.3021852589771]
  We demonstrate that minimal modifications to the transformer architecture can significantly enhance performance on the Long Range Arena benchmark. We identify that two key principles for long-range tasks are (i.e. incorporating an inductive bias towards smoothness, and (ii.e.) locality. As we show, integrating these ideas into the attention mechanism improves results with a negligible amount of additional computation and without any additional trainable parameters.
  arXiv  Detail & Related papers  (2023-11-28T09:21:48Z)
• Efficient Long Sequence Modeling via State Space Augmented Transformer [92.74707853711374]
  We propose SPADE, short for $underlinetextbfS$tate sunderlinetextbfP$ace. We augment a SSM into the bottom layer of SPADE, and we employ efficient local attention methods for the other layers. Experimental results on the Long Range Arena benchmark and language modeling tasks demonstrate the effectiveness of the proposed method.
  arXiv  Detail & Related papers  (2022-12-15T20:51:27Z)
• Efficient Global-Local Memory for Real-time Instrument Segmentation of Robotic Surgical Video [53.14186293442669]
  We identify two important clues for surgical instrument perception, including local temporal dependency from adjacent frames and global semantic correlation in long-range duration. We propose a novel dual-memory network (DMNet) to relate both global and local-temporal knowledge. Our method largely outperforms the state-of-the-art works on segmentation accuracy while maintaining a real-time speed.
  arXiv  Detail & Related papers  (2021-09-28T10:10:14Z)
• Finding Action Tubes with a Sparse-to-Dense Framework [62.60742627484788]
  We propose a framework that generates action tube proposals from video streams with a single forward pass in a sparse-to-dense manner. We evaluate the efficacy of our model on the UCF101-24, JHMDB-21 and UCFSports benchmark datasets.
  arXiv  Detail & Related papers  (2020-08-30T15:38:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.