883 research outputs found
Online Meta-Learning for Multi-Source and Semi-Supervised Domain Adaptation
Domain adaptation (DA) is the topical problem of adapting models from
labelled source datasets so that they perform well on target datasets where
only unlabelled or partially labelled data is available. Many methods have been
proposed to address this problem through different ways to minimise the domain
shift between source and target datasets. In this paper we take an orthogonal
perspective and propose a framework to further enhance performance by
meta-learning the initial conditions of existing DA algorithms. This is
challenging compared to the more widely considered setting of few-shot
meta-learning, due to the length of the computation graph involved. Therefore
we propose an online shortest-path meta-learning framework that is both
computationally tractable and practically effective for improving DA
performance. We present variants for both multi-source unsupervised domain
adaptation (MSDA), and semi-supervised domain adaptation (SSDA). Importantly,
our approach is agnostic to the base adaptation algorithm, and can be applied
to improve many techniques. Experimentally, we demonstrate improvements on
classic (DANN) and recent (MCD and MME) techniques for MSDA and SSDA, and
ultimately achieve state of the art results on several DA benchmarks including
the largest scale DomainNet.Comment: ECCV 2020 CR versio
CFT Duals for Extreme Black Holes
It is argued that the general four-dimensional extremal Kerr-Newman-AdS-dS
black hole is holographically dual to a (chiral half of a) two-dimensional CFT,
generalizing an argument given recently for the special case of extremal Kerr.
Specifically, the asymptotic symmetries of the near-horizon region of the
general extremal black hole are shown to be generated by a Virasoro algebra.
Semiclassical formulae are derived for the central charge and temperature of
the dual CFT as functions of the cosmological constant, Newton's constant and
the black hole charges and spin. We then show, assuming the Cardy formula, that
the microscopic entropy of the dual CFT precisely reproduces the macroscopic
Bekenstein-Hawking area law. This CFT description becomes singular in the
extreme Reissner-Nordstrom limit where the black hole has no spin. At this
point a second dual CFT description is proposed in which the global part of the
U(1) gauge symmetry is promoted to a Virasoro algebra. This second description
is also found to reproduce the area law. Various further generalizations
including higher dimensions are discussed.Comment: 18 pages; v2 minor change
Strings and Branes in Nonabelian Gauge Theory
It is an old speculation that SU(N) gauge theory can alternatively be
formulated as a string theory. Recently this subject has been revived, in the
wake of the discovery of D-branes. In particular, it has been argued that at
least some conformally invariant cousins of the theory have such a string
representation. This is a pedagogical introduction to these developments for
non-string theorists. Some of the existing arguments are simplified.Comment: Reference adde
An almost sure limit theorem for super-Brownian motion
We establish an almost sure scaling limit theorem for super-Brownian motion
on associated with the semi-linear equation , where and are positive constants. In
this case, the spectral theoretical assumptions that required in Chen et al
(2008) are not satisfied. An example is given to show that the main results
also hold for some sub-domains in .Comment: 14 page
Hierarchical Temporal Representation in Linear Reservoir Computing
Recently, studies on deep Reservoir Computing (RC) highlighted the role of
layering in deep recurrent neural networks (RNNs). In this paper, the use of
linear recurrent units allows us to bring more evidence on the intrinsic
hierarchical temporal representation in deep RNNs through frequency analysis
applied to the state signals. The potentiality of our approach is assessed on
the class of Multiple Superimposed Oscillator tasks. Furthermore, our
investigation provides useful insights to open a discussion on the main aspects
that characterize the deep learning framework in the temporal domain.Comment: This is a pre-print of the paper submitted to the 27th Italian
Workshop on Neural Networks, WIRN 201
Towards a Universal Theory of Artificial Intelligence based on Algorithmic Probability and Sequential Decision Theory
Decision theory formally solves the problem of rational agents in uncertain
worlds if the true environmental probability distribution is known.
Solomonoff's theory of universal induction formally solves the problem of
sequence prediction for unknown distribution. We unify both theories and give
strong arguments that the resulting universal AIXI model behaves optimal in any
computable environment. The major drawback of the AIXI model is that it is
uncomputable. To overcome this problem, we construct a modified algorithm
AIXI^tl, which is still superior to any other time t and space l bounded agent.
The computation time of AIXI^tl is of the order t x 2^l.Comment: 8 two-column pages, latex2e, 1 figure, submitted to ijca
Learning to Learn with Variational Information Bottleneck for Domain Generalization
Domain generalization models learn to generalize to previously unseen
domains, but suffer from prediction uncertainty and domain shift. In this
paper, we address both problems. We introduce a probabilistic meta-learning
model for domain generalization, in which classifier parameters shared across
domains are modeled as distributions. This enables better handling of
prediction uncertainty on unseen domains. To deal with domain shift, we learn
domain-invariant representations by the proposed principle of meta variational
information bottleneck, we call MetaVIB. MetaVIB is derived from novel
variational bounds of mutual information, by leveraging the meta-learning
setting of domain generalization. Through episodic training, MetaVIB learns to
gradually narrow domain gaps to establish domain-invariant representations,
while simultaneously maximizing prediction accuracy. We conduct experiments on
three benchmarks for cross-domain visual recognition. Comprehensive ablation
studies validate the benefits of MetaVIB for domain generalization. The
comparison results demonstrate our method outperforms previous approaches
consistently.Comment: 15 pages, 4 figures, ECCV202
Hindsight policy gradients
A reinforcement learning agent that needs to pursue different goals across
episodes requires a goal-conditional policy. In addition to their potential to
generalize desirable behavior to unseen goals, such policies may also enable
higher-level planning based on subgoals. In sparse-reward environments, the
capacity to exploit information about the degree to which an arbitrary goal has
been achieved while another goal was intended appears crucial to enable sample
efficient learning. However, reinforcement learning agents have only recently
been endowed with such capacity for hindsight. In this paper, we demonstrate
how hindsight can be introduced to policy gradient methods, generalizing this
idea to a broad class of successful algorithms. Our experiments on a diverse
selection of sparse-reward environments show that hindsight leads to a
remarkable increase in sample efficiency.Comment: Accepted to ICLR 201
Reinforcement Learning in Sparse-Reward Environments with Hindsight Policy Gradients
A reinforcement learning agent that needs to pursue different goals across episodes requires a goal-conditional policy. In addition to their potential to generalize desirable behavior to unseen goals, such policies may also enable higher-level planning based on subgoals. In sparse-reward environments, the capacity to exploit information about the degree to which an arbitrary goal has been achieved while another goal was intended appears crucial to enabling sample efficient learning. However, reinforcement learning agents have only recently been endowed with such capacity for hindsight. In this letter, we demonstrate how hindsight can be introduced to policy gradient methods, generalizing this idea to a broad class of successful algorithms. Our experiments on a diverse selection of sparse-reward environments show that hindsight leads to a remarkable increase in sample efficiency
Recurrent Neural-Linear Posterior Sampling for Nonstationary Contextual Bandits
An agent in a nonstationary contextual bandit problem should balance between
exploration and the exploitation of (periodic or structured) patterns present
in its previous experiences. Handcrafting an appropriate historical context is
an attractive alternative to transform a nonstationary problem into a
stationary problem that can be solved efficiently. However, even a carefully
designed historical context may introduce spurious relationships or lack a
convenient representation of crucial information. In order to address these
issues, we propose an approach that learns to represent the relevant context
for a decision based solely on the raw history of interactions between the
agent and the environment. This approach relies on a combination of features
extracted by recurrent neural networks with a contextual linear bandit
algorithm based on posterior sampling. Our experiments on a diverse selection
of contextual and noncontextual nonstationary problems show that our recurrent
approach consistently outperforms its feedforward counterpart, which requires
handcrafted historical contexts, while being more widely applicable than
conventional nonstationary bandit algorithms. Although it is very difficult to
provide theoretical performance guarantees for our new approach, we also prove
a novel regret bound for linear posterior sampling with measurement error that
may serve as a foundation for future theoretical work
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