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Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning
TLDR
A new algorithm MINERVA is proposed, which addresses the much more difficult and practical task of answering questions where the relation is known, but only one entity, and significantly outperforms prior methods.
Agent-Human Interactions in the Continuous Double Auction
TLDR
It is found that agents consistently obtain significantly larger gains from trade than their human counterparts, in sharp contrast to the robust convergence observed in previous all-human or all-agent CDA experiments.
Gaussian LDA for Topic Models with Word Embeddings
TLDR
Gaussian LDA is replaced with multivariate Gaussian distributions on the embedding space, which encourages the model to group words that are a priori known to be semantically related into topics into topics.
Optimal power allocation in server farms
TLDR
The analysis shows that the optimal power allocation is non-obvious and depends on many factors such as the power-to-frequency relationship in the processors, the arrival rate of jobs, the maximum server frequency, the lowest attainable server frequency and the server farm configuration.
Analyzing Complex Strategic Interactions in Multi-Agent Systems
We develop a model for analyzing complex games with repeated interactions, for which a full game-theoretic analysis is intractable. Our approach treats exogenously specified, heuristic strategies,
Utility functions in autonomic systems
TLDR
A distributed architecture, implemented in a realistic prototype data center, that demonstrates how utility functions can enable a collection of autonomic elements to continually optimize the use of computational resources in a dynamic, heterogeneous environment is presented.
High-performance bidding agents for the continuous double auction
TLDR
Under various market rules and limit price distributions, the modified Gjerstad-Dickhaut ("MGD") strategy outperforms the original GD, and generally ominates the other strategies.
Chains of Reasoning over Entities, Relations, and Text using Recurrent Neural Networks
TLDR
This paper learns to jointly reason about relations, entities, and entity-types, and uses neural attention modeling to incorporate multiple paths in a single RNN that represents logical composition across all relations.
Multi-step Retriever-Reader Interaction for Scalable Open-domain Question Answering
TLDR
A new framework for open-domain question answering in which the retriever and the reader iteratively interact with each other, agnostic to the architecture of the machine reading model, only requiring access to the token-level hidden representations of the reader.
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