47 research outputs found
Learning Aggregation Functions
Learning on sets is increasingly gaining attention in the machine learning
community, due to its widespread applicability. Typically, representations over
sets are computed by using fixed aggregation functions such as sum or maximum.
However, recent results showed that universal function representation by sum-
(or max-) decomposition requires either highly discontinuous (and thus poorly
learnable) mappings, or a latent dimension equal to the maximum number of
elements in the set. To mitigate this problem, we introduce a learnable
aggregation function (LAF) for sets of arbitrary cardinality. LAF can
approximate several extensively used aggregators (such as average, sum,
maximum) as well as more complex functions (e.g., variance and skewness). We
report experiments on semi-synthetic and real data showing that LAF outperforms
state-of-the-art sum- (max-) decomposition architectures such as DeepSets and
library-based architectures like Principal Neighborhood Aggregation, and can be
effectively combined with attention-based architectures.Comment: Extended version (with proof appendix) of paper that is to appear in
Proceedings of IJCAI 202
A Comprehensive Study on Knowledge Graph Embedding over Relational Patterns Based on Rule Learning
Knowledge Graph Embedding (KGE) has proven to be an effective approach to
solving the Knowledge Graph Completion (KGC) task. Relational patterns which
refer to relations with specific semantics exhibiting graph patterns are an
important factor in the performance of KGE models. Though KGE models'
capabilities are analyzed over different relational patterns in theory and a
rough connection between better relational patterns modeling and better
performance of KGC has been built, a comprehensive quantitative analysis on KGE
models over relational patterns remains absent so it is uncertain how the
theoretical support of KGE to a relational pattern contributes to the
performance of triples associated to such a relational pattern. To address this
challenge, we evaluate the performance of 7 KGE models over 4 common relational
patterns on 2 benchmarks, then conduct an analysis in theory, entity frequency,
and part-to-whole three aspects and get some counterintuitive conclusions.
Finally, we introduce a training-free method Score-based Patterns Adaptation
(SPA) to enhance KGE models' performance over various relational patterns. This
approach is simple yet effective and can be applied to KGE models without
additional training. Our experimental results demonstrate that our method
generally enhances performance over specific relational patterns. Our source
code is available from GitHub at
https://github.com/zjukg/Comprehensive-Study-over-Relational-Patterns.Comment: This paper is accepted by ISWC 202
Analyzing Granger causality in climate data with time series classification methods
Attribution studies in climate science aim for scientifically ascertaining the influence of climatic variations on natural or anthropogenic factors. Many of those studies adopt the concept of Granger causality to infer statistical cause-effect relationships, while utilizing traditional autoregressive models. In this article, we investigate the potential of state-of-the-art time series classification techniques to enhance causal inference in climate science. We conduct a comparative experimental study of different types of algorithms on a large test suite that comprises a unique collection of datasets from the area of climate-vegetation dynamics. The results indicate that specialized time series classification methods are able to improve existing inference procedures. Substantial differences are observed among the methods that were tested
Building robust prediction models for defective sensor data using Artificial Neural Networks
Predicting the health of components in complex dynamic systems such as an
automobile poses numerous challenges. The primary aim of such predictive
systems is to use the high-dimensional data acquired from different sensors and
predict the state-of-health of a particular component, e.g., brake pad. The
classical approach involves selecting a smaller set of relevant sensor signals
using feature selection and using them to train a machine learning algorithm.
However, this fails to address two prominent problems: (1) sensors are
susceptible to failure when exposed to extreme conditions over a long periods
of time; (2) sensors are electrical devices that can be affected by noise or
electrical interference. Using the failed and noisy sensor signals as inputs
largely reduce the prediction accuracy. To tackle this problem, it is
advantageous to use the information from all sensor signals, so that the
failure of one sensor can be compensated by another. In this work, we propose
an Artificial Neural Network (ANN) based framework to exploit the information
from a large number of signals. Secondly, our framework introduces a data
augmentation approach to perform accurate predictions in spite of noisy
signals. The plausibility of our framework is validated on real life industrial
application from Robert Bosch GmbH.Comment: 16 pages, 7 figures. Currently under review. This research has
obtained funding from the Electronic Components and Systems for European
Leadership (ECSEL) Joint Undertaking, the framework programme for research
and innovation Horizon 2020 (2014-2020) under grant agreement number
662189-MANTIS-2014-
Label Attention Network for sequential multi-label classification: you were looking at a wrong self-attention
Most of the available user information can be represented as a sequence of
timestamped events. Each event is assigned a set of categorical labels whose
future structure is of great interest. For instance, our goal is to predict a
group of items in the next customer's purchase or tomorrow's client
transactions. This is a multi-label classification problem for sequential data.
Modern approaches focus on transformer architecture for sequential data
introducing self-attention for the elements in a sequence. In that case, we
take into account events' time interactions but lose information on label
inter-dependencies. Motivated by this shortcoming, we propose leveraging a
self-attention mechanism over labels preceding the predicted step. As our
approach is a Label-Attention NETwork, we call it LANET. Experimental evidence
suggests that LANET outperforms the established models' performance and greatly
captures interconnections between labels. For example, the micro-AUC of our
approach is compared to for a vanilla transformer. We provide
an implementation of LANET to facilitate its wider usage
Branched Variational Autoencoder Classifiers
This paper introduces a modified variational autoencoder (VAEs) that contains
an additional neural network branch. The resulting branched VAE (BVAE)
contributes a classification component based on the class labels to the total
loss and therefore imparts categorical information to the latent
representation. As a result, the latent space distributions of the input
classes are separated and ordered, thereby enhancing the classification
accuracy. The degree of improvement is quantified by numerical calculations
employing the benchmark MNIST dataset for both unrotated and rotated digits.
The proposed technique is then compared to and then incorporated into a VAE
with fixed output distributions. This procedure is found to yield improved
performance for a wide range of output distributions
AdaCC: Cumulative Cost-Sensitive Boosting for Imbalanced Classification
Class imbalance poses a major challenge for machine learning as most
supervised learning models might exhibit bias towards the majority class and
under-perform in the minority class. Cost-sensitive learning tackles this
problem by treating the classes differently, formulated typically via a
user-defined fixed misclassification cost matrix provided as input to the
learner. Such parameter tuning is a challenging task that requires domain
knowledge and moreover, wrong adjustments might lead to overall predictive
performance deterioration. In this work, we propose a novel cost-sensitive
boosting approach for imbalanced data that dynamically adjusts the
misclassification costs over the boosting rounds in response to model's
performance instead of using a fixed misclassification cost matrix. Our method,
called AdaCC, is parameter-free as it relies on the cumulative behavior of the
boosting model in order to adjust the misclassification costs for the next
boosting round and comes with theoretical guarantees regarding the training
error. Experiments on 27 real-world datasets from different domains with high
class imbalance demonstrate the superiority of our method over 12
state-of-the-art cost-sensitive boosting approaches exhibiting consistent
improvements in different measures, for instance, in the range of [0.3%-28.56%]
for AUC, [3.4%-21.4%] for balanced accuracy, [4.8%-45%] for gmean and
[7.4%-85.5%] for recall.Comment: 30 page
Videoprompter: an ensemble of foundational models for zero-shot video understanding
Vision-language models (VLMs) classify the query video by calculating a
similarity score between the visual features and text-based class label
representations. Recently, large language models (LLMs) have been used to
enrich the text-based class labels by enhancing the descriptiveness of the
class names. However, these improvements are restricted to the text-based
classifier only, and the query visual features are not considered. In this
paper, we propose a framework which combines pre-trained discriminative VLMs
with pre-trained generative video-to-text and text-to-text models. We introduce
two key modifications to the standard zero-shot setting. First, we propose
language-guided visual feature enhancement and employ a video-to-text model to
convert the query video to its descriptive form. The resulting descriptions
contain vital visual cues of the query video, such as what objects are present
and their spatio-temporal interactions. These descriptive cues provide
additional semantic knowledge to VLMs to enhance their zeroshot performance.
Second, we propose video-specific prompts to LLMs to generate more meaningful
descriptions to enrich class label representations. Specifically, we introduce
prompt techniques to create a Tree Hierarchy of Categories for class names,
offering a higher-level action context for additional visual cues, We
demonstrate the effectiveness of our approach in video understanding across
three different zero-shot settings: 1) video action recognition, 2)
video-to-text and textto-video retrieval, and 3) time-sensitive video tasks.
Consistent improvements across multiple benchmarks and with various VLMs
demonstrate the effectiveness of our proposed framework. Our code will be made
publicly available