Designing an automatic solver for math word problems has been considered as a crucial step towards general AI, with the ability of natural language understanding and logical inference. The state-of-the-art performance was achieved by enumerating all the possible expressions from the quantities in ...
[AllArith]Unit dependency graph and its application to arithmetic word problem solving, AAAI 2017 [paper] [DRAW-1K]Annotating Derivations: A New Evaluation Strategy and Dataset for Algebra Word Problems, ACL 2017 [paper] 🔥 [Math23K]Deep neural solver for math word problems, EMNLP 2017 [pape...
"Instead of formally defining the optimization problem and deriving the update step with a programmed solver," the researchers write, "we describe the optimization problem in natural language, then instruct the LLM to iteratively generate new solutions based on the problem ...
To test and select the appropriate deep neural network for our problem, we tested two deep neural networks: MLP and CNN. The experiment was performed once on the balance data and once on the imbalance data. For these experiments, we applied random under-sampling as the balancing method. Thirt...
Several “deep” neural network architectures for predicting a patient’s risk of readmission to the ICU were implemented and compared. To make this comparison as fair as possible, all architectures shared a similar high level structure: (1) timestamped codes were mapped to vector embeddings; (...
Wang Y, Liu X, Shi S (2017) Deep neural solver for math word problems. In: Proceedings of the 2017 conference on empirical methods in natural language processing, pp 845–854. Association for Computational Linguistics, Copenhagen, Denmark. https://doi.org/10.18653/v1/D17-1088 Ling W, Yoga...
Safety concerns on the deep neural networks (DNNs) have been raised when they are applied to critical sectors. In this paper, we define safety risks by req
Math Word Problems (MWPs)Convolutional Neural Network (CNN)In mathematics, closed-domain systems for Question Answering (QA) have shown a distinct advantage over open-domain systems, primarily due to their focused use of supporting knowledge bases. This advantage is particularly salient in the era ...
Deep Neural Solver for Math Word Problems. In Proceedings of the Empirical Methods in Natural Language Processing (EMNLP), Copenhagen, Denmark, 9–11 September 2017. [Google Scholar] Ling, W.; Yogatama, D.; Dyer, C.; Blunsom, P. Program Induction by Rationale Generation: Learning to Solve...
This framework offers a robust and scalable solution for complex industrial scheduling problems, enhancing production efficiency and adaptability. Keywords: deep reinforcement learning; flexible job-shop scheduling problem; heterogeneous graph neural network; manufacturing job-shop scheduling...