1,974,837 research outputs found
Learning to Reason: Leveraging Neural Networks for Approximate DNF Counting
Weighted model counting (WMC) has emerged as a prevalent approach for
probabilistic inference. In its most general form, WMC is #P-hard. Weighted DNF
counting (weighted #DNF) is a special case, where approximations with
probabilistic guarantees are obtained in O(nm), where n denotes the number of
variables, and m the number of clauses of the input DNF, but this is not
scalable in practice. In this paper, we propose a neural model counting
approach for weighted #DNF that combines approximate model counting with deep
learning, and accurately approximates model counts in linear time when width is
bounded. We conduct experiments to validate our method, and show that our model
learns and generalizes very well to large-scale #DNF instances.Comment: To appear in Proceedings of the Thirty-Fourth AAAI Conference on
Artificial Intelligence (AAAI-20). Code and data available at:
https://github.com/ralphabb/NeuralDNF
Learning to Reason: End-to-End Module Networks for Visual Question Answering
Natural language questions are inherently compositional, and many are most
easily answered by reasoning about their decomposition into modular
sub-problems. For example, to answer "is there an equal number of balls and
boxes?" we can look for balls, look for boxes, count them, and compare the
results. The recently proposed Neural Module Network (NMN) architecture
implements this approach to question answering by parsing questions into
linguistic substructures and assembling question-specific deep networks from
smaller modules that each solve one subtask. However, existing NMN
implementations rely on brittle off-the-shelf parsers, and are restricted to
the module configurations proposed by these parsers rather than learning them
from data. In this paper, we propose End-to-End Module Networks (N2NMNs), which
learn to reason by directly predicting instance-specific network layouts
without the aid of a parser. Our model learns to generate network structures
(by imitating expert demonstrations) while simultaneously learning network
parameters (using the downstream task loss). Experimental results on the new
CLEVR dataset targeted at compositional question answering show that N2NMNs
achieve an error reduction of nearly 50% relative to state-of-the-art
attentional approaches, while discovering interpretable network architectures
specialized for each question
Learning to Reason with Adaptive Computation
Multi-hop inference is necessary for machine learning systems to successfully solve tasks such as Recognising Textual Entailment and Machine Reading. In this work, we demonstrate the effectiveness of adaptive computation for learning the number of inference steps required for examples of different complexity and that learning the correct number of inference steps is difficult. We introduce the first model involving Adaptive Computation Time which provides a small performance benefit on top of a similar model without an adaptive component as well as enabling considerable insight into the reasoning process of the model
Learning to reason over visual objects
A core component of human intelligence is the ability to identify abstract
patterns inherent in complex, high-dimensional perceptual data, as exemplified
by visual reasoning tasks such as Raven's Progressive Matrices (RPM). Motivated
by the goal of designing AI systems with this capacity, recent work has focused
on evaluating whether neural networks can learn to solve RPM-like problems.
Previous work has generally found that strong performance on these problems
requires the incorporation of inductive biases that are specific to the RPM
problem format, raising the question of whether such models might be more
broadly useful. Here, we investigated the extent to which a general-purpose
mechanism for processing visual scenes in terms of objects might help promote
abstract visual reasoning. We found that a simple model, consisting only of an
object-centric encoder and a transformer reasoning module, achieved
state-of-the-art results on both of two challenging RPM-like benchmarks (PGM
and I-RAVEN), as well as a novel benchmark with greater visual complexity
(CLEVR-Matrices). These results suggest that an inductive bias for
object-centric processing may be a key component of abstract visual reasoning,
obviating the need for problem-specific inductive biases.Comment: ICLR 202
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Learning to Reason
We introduce a new framework for the study of reasoning. The Learning (in order) to Reason approach developed here combines the interfaces to the world used by known learning models with the reasoning task and a performance criterion suitable for it. In this framework the intelligent agent is given access to her favorite learning interface, and is also given a grace period in which she can interact with this interface and construct her representation KB of the world W. Her reasoning performance is measured only after this period, when she is presented with queries a from some query language, relevant to the world, and has to answer whether W implies a. The approach is meant to overcome the main computational difficulties in the traditional treatment of reasoning which stem from its separation from the "world". First, by allowing the reasoning task to interface the world (as in the known learning models), we avoid the rigid syntactic restriction on the intermediate knowledge representation. Second, we make explicit the dependence of the reasoning performance on the input from the environment. This is possible only because the agent interacts with the world when constructing her knowledge representation. We show how previous results from learning theory and reasoning illustrate the usefulness of the Learning to Reason approach by exhibiting new results that are not possible in the traditional setting. First, we give a Learning to Reason algorithm for a class of propositional languages for which there are no efficient reasoning algorithms, when represented as a traditional (formula-based) knowledge base. Second, we exhibit a Learning to Reason Algorithm for a class of propositional languages that is not known to be learnable in the traditional sense.Engineering and Applied Science
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