383,440 research outputs found
Distral: Robust Multitask Reinforcement Learning
Most deep reinforcement learning algorithms are data inefficient in complex
and rich environments, limiting their applicability to many scenarios. One
direction for improving data efficiency is multitask learning with shared
neural network parameters, where efficiency may be improved through transfer
across related tasks. In practice, however, this is not usually observed,
because gradients from different tasks can interfere negatively, making
learning unstable and sometimes even less data efficient. Another issue is the
different reward schemes between tasks, which can easily lead to one task
dominating the learning of a shared model. We propose a new approach for joint
training of multiple tasks, which we refer to as Distral (Distill & transfer
learning). Instead of sharing parameters between the different workers, we
propose to share a "distilled" policy that captures common behaviour across
tasks. Each worker is trained to solve its own task while constrained to stay
close to the shared policy, while the shared policy is trained by distillation
to be the centroid of all task policies. Both aspects of the learning process
are derived by optimizing a joint objective function. We show that our approach
supports efficient transfer on complex 3D environments, outperforming several
related methods. Moreover, the proposed learning process is more robust and
more stable---attributes that are critical in deep reinforcement learning
The desktop interface in intelligent tutoring systems
The interface between an Intelligent Tutoring System (ITS) and the person being tutored is critical to the success of the learning process. If the interface to the ITS is confusing or non-supportive of the tutored domain, the effectiveness of the instruction will be diminished or lost entirely. Consequently, the interface to an ITS should be highly integrated with the domain to provide a robust and semantically rich learning environment. In building an ITS for ZetaLISP on a LISP Machine, a Desktop Interface was designed to support a programming learning environment. Using the bitmapped display, windows, and mouse, three desktops were designed to support self-study and tutoring of ZetaLISP. Through organization, well-defined boundaries, and domain support facilities, the desktops provide substantial flexibility and power for the student and facilitate learning ZetaLISP programming while screening the student from the complex LISP Machine environment. The student can concentrate on learning ZetaLISP programming and not on how to operate the interface or a LISP Machine
Adversarial Training Towards Robust Multimedia Recommender System
With the prevalence of multimedia content on the Web, developing recommender
solutions that can effectively leverage the rich signal in multimedia data is
in urgent need. Owing to the success of deep neural networks in representation
learning, recent advance on multimedia recommendation has largely focused on
exploring deep learning methods to improve the recommendation accuracy. To
date, however, there has been little effort to investigate the robustness of
multimedia representation and its impact on the performance of multimedia
recommendation.
In this paper, we shed light on the robustness of multimedia recommender
system. Using the state-of-the-art recommendation framework and deep image
features, we demonstrate that the overall system is not robust, such that a
small (but purposeful) perturbation on the input image will severely decrease
the recommendation accuracy. This implies the possible weakness of multimedia
recommender system in predicting user preference, and more importantly, the
potential of improvement by enhancing its robustness. To this end, we propose a
novel solution named Adversarial Multimedia Recommendation (AMR), which can
lead to a more robust multimedia recommender model by using adversarial
learning. The idea is to train the model to defend an adversary, which adds
perturbations to the target image with the purpose of decreasing the model's
accuracy. We conduct experiments on two representative multimedia
recommendation tasks, namely, image recommendation and visually-aware product
recommendation. Extensive results verify the positive effect of adversarial
learning and demonstrate the effectiveness of our AMR method. Source codes are
available in https://github.com/duxy-me/AMR.Comment: TKD
Robust Subgraph Generation Improves Abstract Meaning Representation Parsing
The Abstract Meaning Representation (AMR) is a representation for open-domain
rich semantics, with potential use in fields like event extraction and machine
translation. Node generation, typically done using a simple dictionary lookup,
is currently an important limiting factor in AMR parsing. We propose a small
set of actions that derive AMR subgraphs by transformations on spans of text,
which allows for more robust learning of this stage. Our set of construction
actions generalize better than the previous approach, and can be learned with a
simple classifier. We improve on the previous state-of-the-art result for AMR
parsing, boosting end-to-end performance by 3 F on both the LDC2013E117 and
LDC2014T12 datasets.Comment: To appear in ACL 201
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