The Experts below are selected from a list of 318 Experts worldwide ranked by ideXlab platform
Chaochun Liang - One of the best experts on this subject based on the ideXlab platform.
-
a meaning based statistical english math word Problem Solver
North American Chapter of the Association for Computational Linguistics, 2018Co-Authors: Chaochun Liang, Yushiang Wong, Yichung LinAbstract:We introduce MeSys, a meaning-based approach, for solving English math word Problems (MWPs) via understanding and reasoning in this paper. It first analyzes the text, transforms both body and question parts into their corresponding logic forms, and then performs inference on them. The associated context of each quantity is represented with proposed role-tags (e.g., nsubj, verb, etc.), which provides the flexibility for annotating an extracted math quantity with its associated context information (i.e., the physical meaning of this quantity). Statistical models are proposed to select the operator and operands. A noisy dataset is designed to assess if a Solver solves MWPs mainly via understanding or mechanical pattern matching. Experimental results show that our approach outperforms existing systems on both benchmark datasets and the noisy dataset, which demonstrates that the proposed approach understands the meaning of each quantity in the text more.
-
a tag based statistical english math word Problem Solver with understanding reasoning and explanation
International Joint Conference on Artificial Intelligence, 2016Co-Authors: Chaochun Liang, Kuangyi Hsu, Chientsung Huang, Shenyu MiaoAbstract:This demonstration presents a tag-based statistical English math word Problem (MWP) Solver with understanding, reasoning, and explanation. It analyzes the text and transforms both body and question parts into their tag-based logic forms, and then performs inference on them. The proposed tag (e.g., Agent, Verb, etc.) provides the flexibility for annotating an extracted math quantity with its associated syntactic and semantic information, which can be used to identify the desired operand and filter out irrelevant quantities (so that the answer can be obtained precisely). Since the physical meaning of each quantity is explicitly represented with those tags and used in the inference process, the proposed approach could explain how the answer is obtained in a human comprehensible way.
-
designing a tag based statistical math word Problem Solver with reasoning and explanation
International Conference on Computational Linguistics, 2015Co-Authors: Yichung Lin, Chaochun Liang, Kuangyi Hsu, Chientsung Huang, Shenyun Miao, Churnjung LiauAbstract:This paper proposes a tag-based statistical framework to solve math word Problems with understanding and reasoning. It analyzes the body and question texts into their associated tag-based logic forms, and then performs inference on them. Comparing to those rule-based approaches, the proposed statistical approach alleviates rules coverage and ambiguity resolution Problems, and our tag-based approach also provides the flexibility of handling various kinds of related questions with the same body logic form. On the other hand, comparing to those purely statistical approaches, the proposed approach is more robust to the irrelevant information and could more accurately provide the answer. The major contributions of our work are: (1) proposing a tag-based logic representation such that the system is less sensitive to the irrelevant information and could provide answer more precisely; (2) proposing a unified statistical framework for performing reasoning from the given text.
Yichung Lin - One of the best experts on this subject based on the ideXlab platform.
-
a meaning based statistical english math word Problem Solver
North American Chapter of the Association for Computational Linguistics, 2018Co-Authors: Chaochun Liang, Yushiang Wong, Yichung LinAbstract:We introduce MeSys, a meaning-based approach, for solving English math word Problems (MWPs) via understanding and reasoning in this paper. It first analyzes the text, transforms both body and question parts into their corresponding logic forms, and then performs inference on them. The associated context of each quantity is represented with proposed role-tags (e.g., nsubj, verb, etc.), which provides the flexibility for annotating an extracted math quantity with its associated context information (i.e., the physical meaning of this quantity). Statistical models are proposed to select the operator and operands. A noisy dataset is designed to assess if a Solver solves MWPs mainly via understanding or mechanical pattern matching. Experimental results show that our approach outperforms existing systems on both benchmark datasets and the noisy dataset, which demonstrates that the proposed approach understands the meaning of each quantity in the text more.
-
designing a tag based statistical math word Problem Solver with reasoning and explanation
International Conference on Computational Linguistics, 2015Co-Authors: Yichung Lin, Chaochun Liang, Kuangyi Hsu, Chientsung Huang, Shenyun Miao, Churnjung LiauAbstract:This paper proposes a tag-based statistical framework to solve math word Problems with understanding and reasoning. It analyzes the body and question texts into their associated tag-based logic forms, and then performs inference on them. Comparing to those rule-based approaches, the proposed statistical approach alleviates rules coverage and ambiguity resolution Problems, and our tag-based approach also provides the flexibility of handling various kinds of related questions with the same body logic form. On the other hand, comparing to those purely statistical approaches, the proposed approach is more robust to the irrelevant information and could more accurately provide the answer. The major contributions of our work are: (1) proposing a tag-based logic representation such that the system is less sensitive to the irrelevant information and could provide answer more precisely; (2) proposing a unified statistical framework for performing reasoning from the given text.
Jurgen Schmidhuber - One of the best experts on this subject based on the ideXlab platform.
-
powerplay training an increasingly general Problem Solver by continually searching for the simplest still unsolvable Problem
Frontiers in Psychology, 2013Co-Authors: Jurgen SchmidhuberAbstract:Most of computer science focuses on automatically solving given computational Problems. I focus on automatically inventing or discovering Problems in a way inspired by the playful behavior of animals and humans, to train a more and more general Problem Solver from scratch in an unsupervised fashion. Consider the infinite set of all computable descriptions of tasks with possibly computable solutions. The novel algorithmic framework POWERPLAY (2011) continually searches the space of possible pairs of new tasks and modifications of the current Problem Solver, until it finds a more powerful Problem Solver that provably solves all previously learned tasks plus the new one, while the unmodified predecessor does not. Wow-effects are achieved by continually making previously learned skills more efficient such that they require less time and space. New skills may (partially) re-use previously learned skills. POWERPLAY's search orders candidate pairs of tasks and Solver modifications by their conditional computational (time & space) complexity, given the stored experience so far. The new task and its corresponding task-solving skill are those first found and validated. The computational costs of validating new tasks need not grow with task repertoire size. POWERPLAY's ongoing search for novelty keeps breaking the generalization abilities of its present Solver. This is related to Goedel's sequence of increasingly powerful formal theories based on adding formerly unprovable statements to the axioms without affecting previously provable theorems. The continually increasing repertoire of Problem solving procedures can be exploited by a parallel search for solutions to additional externally posed tasks. POWERPLAY may be viewed as a greedy but practical implementation of basic principles of creativity. A first experimental analysis can be found in separate papers [58, 56, 57].
-
Optimal Ordered Problem Solver
Machine Learning, 2004Co-Authors: Jurgen SchmidhuberAbstract:We introduce a general and in a certain sense time-optimal way of solving one Problem after another, efficiently searching the space of programs that compute solution candidates, including those programs that organize and manage and adapt and reuse earlier acquired knowledge. The Optimal Ordered Problem Solver (OOPS) draws inspiration from Levin's Universal Search designed for single Problems and universal Turing machines. It spends part of the total search time for a new Problem on testing programs that exploit previous solution-computing programs in computable ways. If the new Problem can be solved faster by copy-editing/invoking previous code than by solving the new Problem from scratch, then OOPS will find this out. If not, then at least the previous solutions will not cause much harm. We introduce an efficient, recursive, backtracking-based way of implementing OOPS on realistic computers with limited storage. Experiments illustrate how OOPS can greatly profit from metalearning or metasearching, that is, searching for faster search procedures.
-
optimal ordered Problem Solver
arXiv: Artificial Intelligence, 2002Co-Authors: Jurgen SchmidhuberAbstract:We present a novel, general, optimally fast, incremental way of searching for a universal algorithm that solves each task in a sequence of tasks. The Optimal Ordered Problem Solver (OOPS) continually organizes and exploits previously found solutions to earlier tasks, efficiently searching not only the space of domain-specific algorithms, but also the space of search algorithms. Essentially we extend the principles of optimal nonincremental universal search to build an incremental universal learner that is able to improve itself through experience. In illustrative experiments, our self-improver becomes the first general system that learns to solve all n disk Towers of Hanoi tasks (solution size 2^n-1) for n up to 30, profiting from previously solved, simpler tasks involving samples of a simple context free language.
-
optimal ordered Problem Solver
Machine Learning, 2002Co-Authors: Jurgen SchmidhuberAbstract:We present a novel, general, optimally fast, incremental way of searching for a universal algorithm that solves each task in a sequence of tasks. The Optimal Ordered Problem Solver (OOPS) continually organizes and exploits previously found solutions to earlier tasks, efficiently searching not only the space of domain-specific algorithms, but also the space of search algorithms. Essentially we extend the principles of optimal nonincremental universal search to build an incremental universal learner that is able to improve itself through experience. The initial bias is embodied by a task-dependent probability distribution on possible program prefixes. Prefixes are self-delimiting and executed in online fashion while being generated. They compute the probabilities of their own possible continuations. Let p^n denote a found prefix solving the first n tasks. It may exploit previously stored solutions p^i, i >n, by calling them as subprograms, or by copying them and editing the copies before applying them. We provide equal resources for two searches that run in parallel until p^{n+1} is discovered and stored. The first search is exhaustive; it systematically tests all possible prefixes on all tasks up to n+1. The second search is much more focused; it only searches for prefixes that start with p^n, and only tests them on task n+1, which is safe, because we already know that such prefixes solve all tasks up to n. Both searches are depth-first and bias-optimal: the branches of the search trees are program prefixes, and backtracking is triggered once the sum of the runtimes of the current prefix on all current tasks exceeds the prefix probability multiplied by the total search time so far. In illustrative experiments, our self-improver becomes the first general system that learns to solve all n disk Towers of Hanoi tasks (solution size 2^n-1) for n up to 30, profiting from previously solved, simpler tasks involving samples of a simple context free language.
Shenyu Miao - One of the best experts on this subject based on the ideXlab platform.
-
a tag based statistical english math word Problem Solver with understanding reasoning and explanation
International Joint Conference on Artificial Intelligence, 2016Co-Authors: Chaochun Liang, Kuangyi Hsu, Chientsung Huang, Shenyu MiaoAbstract:This demonstration presents a tag-based statistical English math word Problem (MWP) Solver with understanding, reasoning, and explanation. It analyzes the text and transforms both body and question parts into their tag-based logic forms, and then performs inference on them. The proposed tag (e.g., Agent, Verb, etc.) provides the flexibility for annotating an extracted math quantity with its associated syntactic and semantic information, which can be used to identify the desired operand and filter out irrelevant quantities (so that the answer can be obtained precisely). Since the physical meaning of each quantity is explicitly represented with those tags and used in the inference process, the proposed approach could explain how the answer is obtained in a human comprehensible way.
Raquel Urtasun - One of the best experts on this subject based on the ideXlab platform.
-
deep feedback inverse Problem Solver
arXiv: Computer Vision and Pattern Recognition, 2021Co-Authors: Shenlong Wang, Sivabalan Manivasagam, Antonio Torralba, Raquel UrtasunAbstract:We present an efficient, effective, and generic approach towards solving inverse Problems. The key idea is to leverage the feedback signal provided by the forward process and learn an iterative update model. Specifically, at each iteration, the neural network takes the feedback as input and outputs an update on the current estimation. Our approach does not have any restrictions on the forward process; it does not require any prior knowledge either. Through the feedback information, our model not only can produce accurate estimations that are coherent to the input observation but also is capable of recovering from early incorrect predictions. We verify the performance of our approach over a wide range of inverse Problems, including 6-DOF pose estimation, illumination estimation, as well as inverse kinematics. Comparing to traditional optimization-based methods, we can achieve comparable or better performance while being two to three orders of magnitude faster. Compared to deep learning-based approaches, our model consistently improves the performance on all metrics. Please refer to the project page for videos, animations, supplementary materials, etc.
-
deep feedback inverse Problem Solver
European Conference on Computer Vision, 2020Co-Authors: Shenlong Wang, Sivabalan Manivasagam, Antonio Torralba, Raquel UrtasunAbstract:We present an efficient, effective, and generic approach towards solving inverse Problems. The key idea is to leverage the feedback signal provided by the forward process and learn an iterative update model. Specifically, at each iteration, the neural network takes the feedback as input and outputs an update on current estimation. Our approach does not have any restrictions on the forward process; it does not require any prior knowledge either. Through the feedback information, our model not only can produce accurate estimations that are coherent to the input observation but also is capable of recovering from early incorrect predictions. We verify the performance of our model over a wide range of inverse Problems, including 6-DoF pose estimation, illumination estimation, as well as inverse kinematics. Comparing to traditional optimization-based methods, we can achieve comparable or better performance while being two to three orders of magnitude faster. Compared to deep learning-based approaches, our model consistently improves the performance on all metrics.
