580 research outputs found
Incorporating prior financial domain knowledge into neural networks for implied volatility surface prediction
In this paper we develop a novel neural network model for predicting implied
volatility surface. Prior financial domain knowledge is taken into account. A
new activation function that incorporates volatility smile is proposed, which
is used for the hidden nodes that process the underlying asset price. In
addition, financial conditions, such as the absence of arbitrage, the
boundaries and the asymptotic slope, are embedded into the loss function. This
is one of the very first studies which discuss a methodological framework that
incorporates prior financial domain knowledge into neural network architecture
design and model training. The proposed model outperforms the benchmarked
models with the option data on the S&P 500 index over 20 years. More
importantly, the domain knowledge is satisfied empirically, showing the model
is consistent with the existing financial theories and conditions related to
implied volatility surface.Comment: 8 pages, SIGKDD 202
Zero-Shot Domain Adaptation via Kernel Regression on the Grassmannian
Most visual recognition methods implicitly assume the data distribution
remains unchanged from training to testing. However, in practice domain shift
often exists, where real-world factors such as lighting and sensor type change
between train and test, and classifiers do not generalise from source to target
domains. It is impractical to train separate models for all possible situations
because collecting and labelling the data is expensive. Domain adaptation
algorithms aim to ameliorate domain shift, allowing a model trained on a source
to perform well on a different target domain. However, even for the setting of
unsupervised domain adaptation, where the target domain is unlabelled,
collecting data for every possible target domain is still costly. In this
paper, we propose a new domain adaptation method that has no need to access
either data or labels of the target domain when it can be described by a
parametrised vector and there exits several related source domains within the
same parametric space. It greatly reduces the burden of data collection and
annotation, and our experiments show some promising results.Comment: Accepted to BMVC 2015 Workshop on Differential Geometry in Computer
Vision (DIFF-CV
Transductive Multi-label Zero-shot Learning
Zero-shot learning has received increasing interest as a means to alleviate
the often prohibitive expense of annotating training data for large scale
recognition problems. These methods have achieved great success via learning
intermediate semantic representations in the form of attributes and more
recently, semantic word vectors. However, they have thus far been constrained
to the single-label case, in contrast to the growing popularity and importance
of more realistic multi-label data. In this paper, for the first time, we
investigate and formalise a general framework for multi-label zero-shot
learning, addressing the unique challenge therein: how to exploit multi-label
correlation at test time with no training data for those classes? In
particular, we propose (1) a multi-output deep regression model to project an
image into a semantic word space, which explicitly exploits the correlations in
the intermediate semantic layer of word vectors; (2) a novel zero-shot learning
algorithm for multi-label data that exploits the unique compositionality
property of semantic word vector representations; and (3) a transductive
learning strategy to enable the regression model learned from seen classes to
generalise well to unseen classes. Our zero-shot learning experiments on a
number of standard multi-label datasets demonstrate that our method outperforms
a variety of baselines.Comment: 12 pages, 6 figures, Accepted to BMVC 2014 (oral
Deep Multi-task Representation Learning: A Tensor Factorisation Approach
Most contemporary multi-task learning methods assume linear models. This
setting is considered shallow in the era of deep learning. In this paper, we
present a new deep multi-task representation learning framework that learns
cross-task sharing structure at every layer in a deep network. Our approach is
based on generalising the matrix factorisation techniques explicitly or
implicitly used by many conventional MTL algorithms to tensor factorisation, to
realise automatic learning of end-to-end knowledge sharing in deep networks.
This is in contrast to existing deep learning approaches that need a
user-defined multi-task sharing strategy. Our approach applies to both
homogeneous and heterogeneous MTL. Experiments demonstrate the efficacy of our
deep multi-task representation learning in terms of both higher accuracy and
fewer design choices.Comment: 9 pages, Accepted to ICLR 2017 Conference Track. This is a conference
version of the paper. For the multi-domain learning part (not in this
version), please refer to https://arxiv.org/pdf/1605.06391v1.pd
A Unified Perspective on Multi-Domain and Multi-Task Learning
In this paper, we provide a new neural-network based perspective on
multi-task learning (MTL) and multi-domain learning (MDL). By introducing the
concept of a semantic descriptor, this framework unifies MDL and MTL as well as
encompassing various classic and recent MTL/MDL algorithms by interpreting them
as different ways of constructing semantic descriptors. Our interpretation
provides an alternative pipeline for zero-shot learning (ZSL), where a model
for a novel class can be constructed without training data. Moreover, it leads
to a new and practically relevant problem setting of zero-shot domain
adaptation (ZSDA), which is the analogous to ZSL but for novel domains: A model
for an unseen domain can be generated by its semantic descriptor. Experiments
across this range of problems demonstrate that our framework outperforms a
variety of alternatives.Comment: 9 pages, Accepted to ICLR 2015 Conference Trac
Trace Norm Regularised Deep Multi-Task Learning
We propose a framework for training multiple neural networks simultaneously.
The parameters from all models are regularised by the tensor trace norm, so
that each neural network is encouraged to reuse others' parameters if possible
-- this is the main motivation behind multi-task learning. In contrast to many
deep multi-task learning models, we do not predefine a parameter sharing
strategy by specifying which layers have tied parameters. Instead, our
framework considers sharing for all shareable layers, and the sharing strategy
is learned in a data-driven way.Comment: Submission to Workshop track - ICLR 201
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