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Related papers: Solving Reasoning Tasks with a Slot Transformer

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Transformer, as an alternative to CNN, has been proven effective in many modalities (e.g., texts and images). For 3D point cloud transformers, existing efforts focus primarily on pushing their accuracy to the state-of-the-art level.…

Computer Vision and Pattern Recognition · Computer Science 2023-07-18 Zhijian Liu , Xinyu Yang , Haotian Tang , Shang Yang , Song Han

The emergence of deep learning has yielded noteworthy advancements in time series forecasting (TSF). Transformer architectures, in particular, have witnessed broad utilization and adoption in TSF tasks. Transformers have proven to be the…

Machine Learning · Computer Science 2023-11-01 Liyilei Su , Xumin Zuo , Rui Li , Xin Wang , Heng Zhao , Bingding Huang

It is a challenging task to learn rich and multi-scale spatiotemporal semantics from high-dimensional videos, due to large local redundancy and complex global dependency between video frames. The recent advances in this research have been…

Computer Vision and Pattern Recognition · Computer Science 2022-02-09 Kunchang Li , Yali Wang , Peng Gao , Guanglu Song , Yu Liu , Hongsheng Li , Yu Qiao

Adaptive behavior often requires predicting future events. The theory of reinforcement learning prescribes what kinds of predictive representations are useful and how to compute them. This paper integrates these theoretical ideas with work…

Artificial Intelligence · Computer Science 2024-07-12 Wilka Carvalho , Momchil S. Tomov , William de Cothi , Caswell Barry , Samuel J. Gershman

Few-shot Chain-of-Thought (CoT) prompting has demonstrated strong performance in improving the reasoning capabilities of large language models (LLMs). While theoretical investigations have been conducted to understand CoT, the underlying…

Computation and Language · Computer Science 2024-10-23 Yingqian Cui , Pengfei He , Xianfeng Tang , Qi He , Chen Luo , Jiliang Tang , Yue Xing

Unsupervised object-centric learning from videos is a promising approach towards learning compositional representations that can be applied to various downstream tasks, such as prediction and reasoning. Recently, it was shown that…

Computer Vision and Pattern Recognition · Computer Science 2024-10-22 Cristian Meo , Akihiro Nakano , Mircea Lică , Aniket Didolkar , Masahiro Suzuki , Anirudh Goyal , Mengmi Zhang , Justin Dauwels , Yutaka Matsuo , Yoshua Bengio

Transformers have had a significant impact on natural language processing and have recently demonstrated their potential in computer vision. They have shown promising results over convolution neural networks in fundamental computer vision…

Computer Vision and Pattern Recognition · Computer Science 2023-11-14 Rojina Kashefi , Leili Barekatain , Mohammad Sabokrou , Fatemeh Aghaeipoor

Large transformer models have been shown to be capable of performing in-context learning. By using examples in a prompt as well as a query, they are capable of performing tasks such as few-shot, one-shot, or zero-shot learning to output the…

Computer Vision and Pattern Recognition · Computer Science 2025-05-28 Antony Zhao , Alex Proshkin , Fergal Hennessy , Francesco Crivelli

Learning representations on large-sized graphs is a long-standing challenge due to the inter-dependence nature involved in massive data points. Transformers, as an emerging class of foundation encoders for graph-structured data, have shown…

Machine Learning · Computer Science 2024-08-19 Qitian Wu , Wentao Zhao , Chenxiao Yang , Hengrui Zhang , Fan Nie , Haitian Jiang , Yatao Bian , Junchi Yan

This paper studies interpretable and fair artificial intelligence architectures for understanding English reading. Introduced transformer-based models, integrating advanced attention mechanisms and gradient-based feature attribution. The…

Computation and Language · Computer Science 2026-04-28 Ping Li

Recent language models exhibit strong reasoning capabilities, yet the influence of long-context capacity on reasoning remains underexplored. In this work, we hypothesize that current limitations in reasoning stem, in part, from insufficient…

Artificial Intelligence · Computer Science 2025-05-26 Wang Yang , Zirui Liu , Hongye Jin , Qingyu Yin , Vipin Chaudhary , Xiaotian Han

Unneeded elements in the attention's context degrade performance. We introduce Selective Attention, a simple parameter-free change to the standard attention mechanism which reduces attention to unneeded elements. Selective attention…

Computation and Language · Computer Science 2025-04-25 Yaniv Leviathan , Matan Kalman , Yossi Matias

Object-centric slot attention is a powerful framework for unsupervised learning of structured and explainable representations that can support reasoning about objects and actions, including in surgical videos. While conventional…

Image and Video Processing · Electrical Eng. & Systems 2026-03-04 Guiqiu Liao , Matjaz Jogan , Marcel Hussing , Kenta Nakahashi , Kazuhiro Yasufuku , Amin Madani , Eric Eaton , Daniel A. Hashimoto

The transformer architecture and variants presented remarkable success across many machine learning tasks in recent years. This success is intrinsically related to the capability of handling long sequences and the presence of…

Machine Learning · Computer Science 2022-06-15 Luckeciano C. Melo

Transformer model architectures have garnered immense interest lately due to their effectiveness across a range of domains like language, vision and reinforcement learning. In the field of natural language processing for example,…

Machine Learning · Computer Science 2022-03-15 Yi Tay , Mostafa Dehghani , Dara Bahri , Donald Metzler

It has been over six years since the Transformer architecture was put forward. Surprisingly, the vanilla Transformer architecture is still widely used today. One reason is that the lack of deep understanding and comprehensive interpretation…

Machine Learning · Computer Science 2023-11-22 Zhe Chen

This paper presents CONTHER, a novel reinforcement learning algorithm designed to efficiently and rapidly train robotic agents for goal-oriented manipulation tasks and obstacle avoidance. The algorithm uses a modified replay buffer inspired…

Robotics · Computer Science 2025-03-21 Maria Makarova , Qian Liu , Dzmitry Tsetserukou

Recently, transformer-based networks have shown impressive results in semantic segmentation. Yet for real-time semantic segmentation, pure CNN-based approaches still dominate in this field, due to the time-consuming computation mechanism of…

Computer Vision and Pattern Recognition · Computer Science 2022-10-14 Jian Wang , Chenhui Gou , Qiman Wu , Haocheng Feng , Junyu Han , Errui Ding , Jingdong Wang

Coherence is an important aspect of text quality and is crucial for ensuring its readability. It is essential desirable for outputs from text generation systems like summarization, question answering, machine translation, question…

Computation and Language · Computer Science 2022-02-24 Tushar Abhishek , Daksh Rawat , Manish Gupta , Vasudeva Varma

Large language models often require costly optimization, such as reinforcement learning, to master complex reasoning tasks. This work demonstrates that reasoning ability, once learned, can be extracted and transferred between models as a…

Computation and Language · Computer Science 2025-09-03 Mohammad Zbeeb , Hasan Abed Al Kader Hammoud , Bernard Ghanem
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