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relational_semantic_network [2018/05/26 00:16]
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relational_semantic_network [2018/10/08 12:37]
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 https://​arxiv.org/​abs/​1805.09354v1 Working Memory Networks: Augmenting Memory Networks with a Relational Reasoning Module https://​arxiv.org/​abs/​1805.09354v1 Working Memory Networks: Augmenting Memory Networks with a Relational Reasoning Module
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 +https://​arxiv.org/​abs/​1806.01261 ​  ​Relational inductive biases, deep learning, and graph networks
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 + We argue that combinatorial generalization must be a top priority for AI to achieve human-like abilities, and that structured representations and computations are key to realizing this objective. ​  We present a new building block for the AI toolkit with a strong relational inductive bias--the graph network--which generalizes and extends various approaches for neural networks that operate on graphs, and provides a straightforward interface for manipulating structured knowledge and producing structured behaviors. We discuss how graph networks can support relational reasoning and combinatorial generalization,​ laying the foundation for more sophisticated,​ interpretable,​ and flexible patterns of reasoning.
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 +https://​arxiv.org/​abs/​1807.03877v1 Deep Structured Generative Models
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 + In particular, the layout or structure of the scene is encoded by a stochastic and-or graph (sAOG), in which the terminal nodes represent single objects and edges represent relations between objects. ​
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 +https://​arxiv.org/​abs/​1807.08204 Towards Neural Theorem Proving at Scale
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 + We focus on the Neural Theorem Prover (NTP) model proposed by Rockt{\"​{a}}schel and Riedel (2017), a continuous relaxation of the Prolog backward chaining algorithm where unification between terms is replaced by the similarity between their embedding representations. For answering a given query, this model needs to consider all possible proof paths, and then aggregate results - this quickly becomes infeasible even for small Knowledge Bases (KBs). We observe that we can accurately approximate the inference process in this model by considering only proof paths associated with the highest proof scores. ​
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 +https://​arxiv.org/​abs/​1807.08058v1 Learning Heuristics for Automated Reasoning through Deep Reinforcement Learning
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 +We demonstrate how to learn efficient heuristics for automated reasoning algorithms through deep reinforcement learning. We consider search algorithms for quantified Boolean logics, that already can solve formulas of impressive size - up to 100s of thousands of variables. The main challenge is to find a representation which lends to making predictions in a scalable way. The heuristics learned through our approach significantly improve over the handwritten heuristics for several sets of formulas.
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 +https://​arxiv.org/​abs/​1808.02822v1 ​ Backprop Evolution
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 +https://​arxiv.org/​abs/​1808.06068 SeVeN: Augmenting Word Embeddings with Unsupervised Relation Vectors
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 +https://​arxiv.org/​abs/​1808.07980 Ontology Reasoning with Deep Neural Networks
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 +https://​arxiv.org/​abs/​1808.09333v1 Bridging Knowledge Gaps in Neural Entailment via Symbolic Models
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 +We focus on filling these knowledge gaps in the Science Entailment task, by leveraging an external structured knowledge base (KB) of science facts. Our new architecture combines standard neural entailment models with a knowledge lookup module. To facilitate this lookup, we propose a fact-level decomposition of the hypothesis, and verifying the resulting sub-facts against both the textual premise and the structured KB. Our model, NSnet, learns to aggregate predictions from these heterogeneous data formats. ​
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 +https://​arxiv.org/​pdf/​1806.01445v2.pdf Embedding Logical Queries on Knowledge Graphs
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 +https://​github.com/​williamleif/​graphqembed
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 +https://​arxiv.org/​abs/​1806.01822v2 Relational recurrent neural networks
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 +We then improve upon these deficits by using a new memory module -- a {Relational Memory Core} (RMC) -- which employs multi-head dot product attention to allow memories to interact. Finally, we test the RMC on a suite of tasks that may profit from more capable relational reasoning across sequential information,​ and show large gains in RL domains (e.g. Mini PacMan), program evaluation, and language modeling, achieving state-of-the-art results on the WikiText-103,​ Project Gutenberg, and GigaWord datasets. https://​github.com/​deepmind/​sonnet/​blob/​master/​sonnet/​python/​modules/​relational_memory.py
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 +https://​slideslive.com/​38909774/​embedding-symbolic-computation-within-neural-computation-for-ai-and-nlp
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 +https://​arxiv.org/​abs/​1809.11044 Relational Forward Models for Multi-Agent Learning