97 research outputs found
Ask Your Neurons: A Neural-based Approach to Answering Questions about Images
We address a question answering task on real-world images that is set up as a
Visual Turing Test. By combining latest advances in image representation and
natural language processing, we propose Neural-Image-QA, an end-to-end
formulation to this problem for which all parts are trained jointly. In
contrast to previous efforts, we are facing a multi-modal problem where the
language output (answer) is conditioned on visual and natural language input
(image and question). Our approach Neural-Image-QA doubles the performance of
the previous best approach on this problem. We provide additional insights into
the problem by analyzing how much information is contained only in the language
part for which we provide a new human baseline. To study human consensus, which
is related to the ambiguities inherent in this challenging task, we propose two
novel metrics and collect additional answers which extends the original DAQUAR
dataset to DAQUAR-Consensus.Comment: ICCV'15 (Oral
NAIS: Neural Attentive Item Similarity Model for Recommendation
Item-to-item collaborative filtering (aka. item-based CF) has been long used
for building recommender systems in industrial settings, owing to its
interpretability and efficiency in real-time personalization. It builds a
user's profile as her historically interacted items, recommending new items
that are similar to the user's profile. As such, the key to an item-based CF
method is in the estimation of item similarities. Early approaches use
statistical measures such as cosine similarity and Pearson coefficient to
estimate item similarities, which are less accurate since they lack tailored
optimization for the recommendation task. In recent years, several works
attempt to learn item similarities from data, by expressing the similarity as
an underlying model and estimating model parameters by optimizing a
recommendation-aware objective function. While extensive efforts have been made
to use shallow linear models for learning item similarities, there has been
relatively less work exploring nonlinear neural network models for item-based
CF.
In this work, we propose a neural network model named Neural Attentive Item
Similarity model (NAIS) for item-based CF. The key to our design of NAIS is an
attention network, which is capable of distinguishing which historical items in
a user profile are more important for a prediction. Compared to the
state-of-the-art item-based CF method Factored Item Similarity Model (FISM),
our NAIS has stronger representation power with only a few additional
parameters brought by the attention network. Extensive experiments on two
public benchmarks demonstrate the effectiveness of NAIS. This work is the first
attempt that designs neural network models for item-based CF, opening up new
research possibilities for future developments of neural recommender systems
IMP-MARL: a Suite of Environments for Large-scale Infrastructure Management Planning via MARL
We introduce IMP-MARL, an open-source suite of multi-agent reinforcement
learning (MARL) environments for large-scale Infrastructure Management Planning
(IMP), offering a platform for benchmarking the scalability of cooperative MARL
methods in real-world engineering applications. In IMP, a multi-component
engineering system is subject to a risk of failure due to its components'
damage condition. Specifically, each agent plans inspections and repairs for a
specific system component, aiming to minimise maintenance costs while
cooperating to minimise system failure risk. With IMP-MARL, we release several
environments including one related to offshore wind structural systems, in an
effort to meet today's needs to improve management strategies to support
sustainable and reliable energy systems. Supported by IMP practical engineering
environments featuring up to 100 agents, we conduct a benchmark campaign, where
the scalability and performance of state-of-the-art cooperative MARL methods
are compared against expert-based heuristic policies. The results reveal that
centralised training with decentralised execution methods scale better with the
number of agents than fully centralised or decentralised RL approaches, while
also outperforming expert-based heuristic policies in most IMP environments.
Based on our findings, we additionally outline remaining cooperation and
scalability challenges that future MARL methods should still address. Through
IMP-MARL, we encourage the implementation of new environments and the further
development of MARL methods
Deep Q-Learning With Q-Matrix Transfer Learning for Novel Fire Evacuation Environment
Author's accepted manuscript.© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.acceptedVersio
IMP-MARL: a Suite of Environments for Large-scale Infrastructure Management Planning via MARL
We introduce IMP-MARL, an open-source suite of multi-agent reinforcement
learning (MARL) environments for large-scale Infrastructure Management Planning
(IMP), offering a platform for benchmarking the scalability of cooperative MARL
methods in real-world engineering applications. In IMP, a multi-component
engineering system is subject to a risk of failure due to its components'
damage condition. Specifically, each agent plans inspections and repairs for a
specific system component, aiming to minimise maintenance costs while
cooperating to minimise system failure risk. With IMP-MARL, we release several
environments including one related to offshore wind structural systems, in an
effort to meet today's needs to improve management strategies to support
sustainable and reliable energy systems. Supported by IMP practical engineering
environments featuring up to 100 agents, we conduct a benchmark campaign, where
the scalability and performance of state-of-the-art cooperative MARL methods
are compared against expert-based heuristic policies. The results reveal that
centralised training with decentralised execution methods scale better with the
number of agents than fully centralised or decentralised RL approaches, while
also outperforming expert-based heuristic policies in most IMP environments.
Based on our findings, we additionally outline remaining cooperation and
scalability challenges that future MARL methods should still address. Through
IMP-MARL, we encourage the implementation of new environments and the further
development of MARL methods
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