346 research outputs found
Linear Convergence of Adaptively Iterative Thresholding Algorithms for Compressed Sensing
This paper studies the convergence of the adaptively iterative thresholding
(AIT) algorithm for compressed sensing. We first introduce a generalized
restricted isometry property (gRIP). Then we prove that the AIT algorithm
converges to the original sparse solution at a linear rate under a certain gRIP
condition in the noise free case. While in the noisy case, its convergence rate
is also linear until attaining a certain error bound. Moreover, as by-products,
we also provide some sufficient conditions for the convergence of the AIT
algorithm based on the two well-known properties, i.e., the coherence property
and the restricted isometry property (RIP), respectively. It should be pointed
out that such two properties are special cases of gRIP. The solid improvements
on the theoretical results are demonstrated and compared with the known
results. Finally, we provide a series of simulations to verify the correctness
of the theoretical assertions as well as the effectiveness of the AIT
algorithm.Comment: 15 pages, 5 figure
Inherently Explainable Reinforcement Learning in Natural Language
We focus on the task of creating a reinforcement learning agent that is
inherently explainable -- with the ability to produce immediate local
explanations by thinking out loud while performing a task and analyzing entire
trajectories post-hoc to produce causal explanations. This Hierarchically
Explainable Reinforcement Learning agent (HEX-RL), operates in Interactive
Fictions, text-based game environments in which an agent perceives and acts
upon the world using textual natural language. These games are usually
structured as puzzles or quests with long-term dependencies in which an agent
must complete a sequence of actions to succeed -- providing ideal environments
in which to test an agent's ability to explain its actions. Our agent is
designed to treat explainability as a first-class citizen, using an extracted
symbolic knowledge graph-based state representation coupled with a Hierarchical
Graph Attention mechanism that points to the facts in the internal graph
representation that most influenced the choice of actions. Experiments show
that this agent provides significantly improved explanations over strong
baselines, as rated by human participants generally unfamiliar with the
environment, while also matching state-of-the-art task performance
MegDet: A Large Mini-Batch Object Detector
The improvements in recent CNN-based object detection works, from R-CNN [11],
Fast/Faster R-CNN [10, 31] to recent Mask R-CNN [14] and RetinaNet [24], mainly
come from new network, new framework, or novel loss design. But mini-batch
size, a key factor in the training, has not been well studied. In this paper,
we propose a Large MiniBatch Object Detector (MegDet) to enable the training
with much larger mini-batch size than before (e.g. from 16 to 256), so that we
can effectively utilize multiple GPUs (up to 128 in our experiments) to
significantly shorten the training time. Technically, we suggest a learning
rate policy and Cross-GPU Batch Normalization, which together allow us to
successfully train a large mini-batch detector in much less time (e.g., from 33
hours to 4 hours), and achieve even better accuracy. The MegDet is the backbone
of our submission (mmAP 52.5%) to COCO 2017 Challenge, where we won the 1st
place of Detection task
Four Metaphors on Knowledge and Change in Construction
Refurbishment\ua0activities comprise a high proportion of construction industry output in most developed countries. There is no international consensus among statisticians as how to define\ua0refurbishment\ua0or renovation of buildings (Mansfield, 2002). The UK Office for National Statistics publishes data indicating that the volume of ‘repair and maintenance’ corresponded to about 60 per cent of new work during 2014 and 2015. ‘Repair and maintenance’ was roughly equally divided between housing and non-housing, and\ua0it\ua0is probable that much of this was\ua0refurbishment. There are no obvious reasons why ongoing investment in existing buildings should decline and the potential for increasing environmental sustainability by improving energy performance in the building stock remains considerable. The EU Energy Efficiency Directive (2012/27/EU) adopted in 2012 includes a requirement for member states to develop long-term renovation strategies for their national building stocks
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