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 ...
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 the...
[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 ...
io.github.MazeSolver io.github.MediaWriter io.github.MedianXLOfflineTools io.github.MemoryCards io.github.MemoryPuzzle_Qt-Cpp io.github.MimeDetector io.github.MineSweep io.github.Minefield io.github.MinesweeperFloating io.github.MiniAppCalendar io.github.MiniZincIDE io.github.Molsketch io.gith...
PyTorch 1.0 shines for rapid prototyping with dynamic neural networks, auto-differentiation, deep Python integration, and strong support for GPUs
Aiming at the problems of low efficiency and poor accuracy of traditional CAPTCHA recognition methods, we have proposed a more efficient way based on deep convolutional neural network (CNN). The Dense Convolutional Network (DenseNet) has shown excellent
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
we applied random under-sampling as the balancing method. Thirty-two different combinations were used as features that have been mentioned already. To set the neural networks: the number of batch sizes for MLP and CNN were 310 and 16, respectively. Additionally, the number of epochs was determi...
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; (...