20,830 research outputs found
Intensity Profile Projection: A Framework for Continuous-Time Representation Learning for Dynamic Networks
We present a new representation learning framework, Intensity Profile
Projection, for continuous-time dynamic network data. Given triples ,
each representing a time-stamped () interaction between two entities
(), our procedure returns a continuous-time trajectory for each node,
representing its behaviour over time. The framework consists of three stages:
estimating pairwise intensity functions, e.g. via kernel smoothing; learning a
projection which minimises a notion of intensity reconstruction error; and
constructing evolving node representations via the learned projection. The
trajectories satisfy two properties, known as structural and temporal
coherence, which we see as fundamental for reliable inference. Moreoever, we
develop estimation theory providing tight control on the error of any estimated
trajectory, indicating that the representations could even be used in quite
noise-sensitive follow-on analyses. The theory also elucidates the role of
smoothing as a bias-variance trade-off, and shows how we can reduce the level
of smoothing as the signal-to-noise ratio increases on account of the algorithm
`borrowing strength' across the network.Comment: 37 pages, 10 figure
Evolino for recurrent support vector machines
Traditional Support Vector Machines (SVMs) need pre-wired finite time windows
to predict and classify time series. They do not have an internal state
necessary to deal with sequences involving arbitrary long-term dependencies.
Here we introduce a new class of recurrent, truly sequential SVM-like devices
with internal adaptive states, trained by a novel method called EVOlution of
systems with KErnel-based outputs (Evoke), an instance of the recent Evolino
class of methods. Evoke evolves recurrent neural networks to detect and
represent temporal dependencies while using quadratic programming/support
vector regression to produce precise outputs. Evoke is the first SVM-based
mechanism learning to classify a context-sensitive language. It also
outperforms recent state-of-the-art gradient-based recurrent neural networks
(RNNs) on various time series prediction tasks.Comment: 10 pages, 2 figure
Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition: A Problem-Oriented Perspective
This paper takes a problem-oriented perspective and presents a comprehensive
review of transfer learning methods, both shallow and deep, for cross-dataset
visual recognition. Specifically, it categorises the cross-dataset recognition
into seventeen problems based on a set of carefully chosen data and label
attributes. Such a problem-oriented taxonomy has allowed us to examine how
different transfer learning approaches tackle each problem and how well each
problem has been researched to date. The comprehensive problem-oriented review
of the advances in transfer learning with respect to the problem has not only
revealed the challenges in transfer learning for visual recognition, but also
the problems (e.g. eight of the seventeen problems) that have been scarcely
studied. This survey not only presents an up-to-date technical review for
researchers, but also a systematic approach and a reference for a machine
learning practitioner to categorise a real problem and to look up for a
possible solution accordingly
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