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- Kirilenko, Daniil E., et al.
(författare)
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Question Answering for Visual Navigation in Human-Centered Environments
- 2021
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Ingår i: Advances in Soft Computing: 20th Mexican International Conference on Artificial Intelligence, MICAI 2021, Mexico City, Mexico, October 25–30, 2021, Proceedings, Part II. - Cham : Springer Nature. ; , s. 31-45
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Konferensbidrag (refereegranskat)abstract
- In this paper, we propose an HISNav VQA dataset - a challenging dataset for a Visual Question Answering task that is aimed at the needs of Visual Navigation in human-centered environments. The dataset consists of images of various room scenes that were captured using the Habitat virtual environment and of questions important for navigation tasks using only visual information. We also propose a baseline for a HISNav VQA dataset, a Vector Semiotic Architecture, and demonstrate its performance. The Vector Semiotic Architecture is a combination of a Sign-Based World Model and Vector Symbolic Architectures. The Sign-Based World Model allows representing various aspects of an agent’s knowledge, and Vector Symbolic Architectures serve on a low computational level. The Vector Semiotic Architecture addresses the symbol grounding problem that plays an important role in the Visual Question Answering Task.
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| 2. |
- Kleyko, Denis, et al.
(författare)
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Density Encoding Enables Resource-Efficient Randomly Connected Neural Networks
- 2021
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Ingår i: IEEE Transactions on Neural Networks and Learning Systems. - : Institute of Electrical and Electronics Engineers Inc.. - 2162-237X .- 2162-2388. ; 32:8, s. 3777-3783
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Tidskriftsartikel (refereegranskat)abstract
- The deployment of machine learning algorithms on resource-constrained edge devices is an important challenge from both theoretical and applied points of view. In this brief, we focus on resource-efficient randomly connected neural networks known as random vector functional link (RVFL) networks since their simple design and extremely fast training time make them very attractive for solving many applied classification tasks. We propose to represent input features via the density-based encoding known in the area of stochastic computing and use the operations of binding and bundling from the area of hyperdimensional computing for obtaining the activations of the hidden neurons. Using a collection of 121 real-world data sets from the UCI machine learning repository, we empirically show that the proposed approach demonstrates higher average accuracy than the conventional RVFL. We also demonstrate that it is possible to represent the readout matrix using only integers in a limited range with minimal loss in the accuracy. In this case, the proposed approach operates only on small ${n}$ -bits integers, which results in a computationally efficient architecture. Finally, through hardware field-programmable gate array (FPGA) implementations, we show that such an approach consumes approximately 11 times less energy than that of the conventional RVFL.
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