35 research outputs found
Label-similarity Curriculum Learning
Curriculum learning can improve neural network training by guiding the
optimization to desirable optima. We propose a novel curriculum learning
approach for image classification that adapts the loss function by changing the
label representation. The idea is to use a probability distribution over
classes as target label, where the class probabilities reflect the similarity
to the true class. Gradually, this label representation is shifted towards the
standard one-hot-encoding. That is, in the beginning minor mistakes are
corrected less than large mistakes, resembling a teaching process in which
broad concepts are explained first before subtle differences are taught.
The class similarity can be based on prior knowledge. For the special case of
the labels being natural words, we propose a generic way to automatically
compute the similarities. The natural words are embedded into Euclidean space
using a standard word embedding. The probability of each class is then a
function of the cosine similarity between the vector representations of the
class and the true label. The proposed label-similarity curriculum learning
(LCL) approach was empirically evaluated using several popular deep learning
architectures for image classification tasks applied to five datasets including
ImageNet, CIFAR100, and AWA2. In all scenarios, LCL was able to improve the
classification accuracy on the test data compared to standard training.Comment: Accepted as a conference paper at ECCV 202
The Extended Dawid-Skene Model:Fusing Information from Multiple Data Schemas
While label fusion from multiple noisy annotations is a well understood
concept in data wrangling (tackled for example by the Dawid-Skene (DS) model),
we consider the extended problem of carrying out learning when the labels
themselves are not consistently annotated with the same schema. We show that
even if annotators use disparate, albeit related, label-sets, we can still draw
inferences for the underlying full label-set. We propose the Inter-Schema
AdapteR (ISAR) to translate the fully-specified label-set to the one used by
each annotator, enabling learning under such heterogeneous schemas, without the
need to re-annotate the data. We apply our method to a mouse behavioural
dataset, achieving significant gains (compared with DS) in out-of-sample
log-likelihood (-3.40 to -2.39) and F1-score (0.785 to 0.864).Comment: Updated with Author-Preprint version following Publication in P.
Cellier and K. Driessens (Eds.): ECML PKDD 2019 Workshops, CCIS 1167, pp. 121
- 136, 202
Federated Ensemble Regression Using Classification
Ensemble learning has been shown to significantly improve predictive accuracy in a variety of machine learning problems. For a given predictive task, the goal of ensemble learning is to improve predictive accuracy by combining the predictive power of multiple models. In this paper, we present an ensemble learning algorithm for regression problems which leverages the distribution of the samples in a learning set to achieve improved performance. We apply the proposed algorithm to a problem in precision medicine where the goal is to predict drug perturbation effects on genes in cancer cell lines. The proposed approach significantly outperforms the base case
Canonicalizing Knowledge Base Literals
Ontology-based knowledge bases (KBs) like DBpedia are very valuable resources, but their usefulness and usability is limited by various quality issues. One such issue is the use of string literals instead of semantically typed entities. In this paper we study the automated canonicalization of such literals, i.e., replacing the literal with an existing entity from the KB or with a new entity that is typed using classes from the KB. We propose a framework that combines both reasoning and machine learning in order to predict the relevant entities and types, and we evaluate this framework against state-of-the-art baselines for both semantic typing and entity matching
Multi-Target Prediction: A Unifying View on Problems and Methods
Multi-target prediction (MTP) is concerned with the simultaneous prediction
of multiple target variables of diverse type. Due to its enormous application
potential, it has developed into an active and rapidly expanding research field
that combines several subfields of machine learning, including multivariate
regression, multi-label classification, multi-task learning, dyadic prediction,
zero-shot learning, network inference, and matrix completion. In this paper, we
present a unifying view on MTP problems and methods. First, we formally discuss
commonalities and differences between existing MTP problems. To this end, we
introduce a general framework that covers the above subfields as special cases.
As a second contribution, we provide a structured overview of MTP methods. This
is accomplished by identifying a number of key properties, which distinguish
such methods and determine their suitability for different types of problems.
Finally, we also discuss a few challenges for future research
A Review of Supervised Machine Learning Applied to Ageing Research
Broadly speaking, supervised machine learning is the computational task of learning correlations between variables in annotated data (the training set), and using this information to create a predictive model capable of inferring annotations for new data, whose annotations are not known. Ageing is a complex process that affects nearly all animal species. This process can be studied at several levels of abstraction, in different organisms and with different objectives in mind. Not surprisingly, the diversity of the supervised machine learning algorithms applied to answer biological questions reflects the complexities of the underlying ageing processes being studied. Many works using supervised machine learning to study the ageing process have been recently published, so it is timely to review these works, to discuss their main findings and weaknesses.
In summary, the main findings of the reviewed papers are: the link between specific types of DNA repair and ageing, ageing-related proteins tend to be highly connected and seem to play a central role in molecular pathways; ageing/longevity is linked with autophagy and apoptosis, nutrient receptor genes, and copper and iron ion transport. Additionally, several biomarkers of ageing were found by machine learning. Despite some interesting machine learning results, we also identified a weakness of current works on this topic: only one of the reviewed papers has corroborated the computational results of machine learning algorithms through wet-lab experiments. In conclusion, supervised machine learning has contributed to advance our knowledge and has provided novel insights on ageing, yet future work should have a greater emphasis in validating the predictions