1,525 research outputs found
A Wholistic View of Continual Learning with Deep Neural Networks: Forgotten Lessons and the Bridge to Active and Open World Learning
Current deep learning research is dominated by benchmark evaluation. A method
is regarded as favorable if it empirically performs well on the dedicated test
set. This mentality is seamlessly reflected in the resurfacing area of
continual learning, where consecutively arriving sets of benchmark data are
investigated. The core challenge is framed as protecting previously acquired
representations from being catastrophically forgotten due to the iterative
parameter updates. However, comparison of individual methods is nevertheless
treated in isolation from real world application and typically judged by
monitoring accumulated test set performance. The closed world assumption
remains predominant. It is assumed that during deployment a model is guaranteed
to encounter data that stems from the same distribution as used for training.
This poses a massive challenge as neural networks are well known to provide
overconfident false predictions on unknown instances and break down in the face
of corrupted data. In this work we argue that notable lessons from open set
recognition, the identification of statistically deviating data outside of the
observed dataset, and the adjacent field of active learning, where data is
incrementally queried such that the expected performance gain is maximized, are
frequently overlooked in the deep learning era. Based on these forgotten
lessons, we propose a consolidated view to bridge continual learning, active
learning and open set recognition in deep neural networks. Our results show
that this not only benefits each individual paradigm, but highlights the
natural synergies in a common framework. We empirically demonstrate
improvements when alleviating catastrophic forgetting, querying data in active
learning, selecting task orders, while exhibiting robust open world application
where previously proposed methods fail.Comment: 32 page
The Impact of Unemployment Insurance on Job Search
macroeconomics, unemployment insurance, job search
Enhancing Next Active Object-based Egocentric Action Anticipation with Guided Attention
Short-term action anticipation (STA) in first-person videos is a challenging
task that involves understanding the next active object interactions and
predicting future actions. Existing action anticipation methods have primarily
focused on utilizing features extracted from video clips, but often overlooked
the importance of objects and their interactions. To this end, we propose a
novel approach that applies a guided attention mechanism between the objects,
and the spatiotemporal features extracted from video clips, enhancing the
motion and contextual information, and further decoding the object-centric and
motion-centric information to address the problem of STA in egocentric videos.
Our method, GANO (Guided Attention for Next active Objects) is a multi-modal,
end-to-end, single transformer-based network. The experimental results
performed on the largest egocentric dataset demonstrate that GANO outperforms
the existing state-of-the-art methods for the prediction of the next active
object label, its bounding box location, the corresponding future action, and
the time to contact the object. The ablation study shows the positive
contribution of the guided attention mechanism compared to other fusion
methods. Moreover, it is possible to improve the next active object location
and class label prediction results of GANO by just appending the learnable
object tokens with the region of interest embeddings.Comment: Accepted to IEEE ICIP 2023, see project page here :
https://sanketsans.github.io/guided-attention-egocentric.htm
COMPUTING APPROXIMATE CUSTOMIZED RANKING
As the amount of information grows and as users become more
sophisticated, ranking techniques become important building blocks
to meet user needs when answering queries. PageRank is one of the
most successful link-based ranking methods, which iteratively
computes the importance scores for web pages based on the importance scores of incoming pages. Due to its success, PageRank has been applied in a number of applications that require customization.
We address the scalability challenges for two types of customized
ranking. The first challenge is to compute the ranking of a
subgraph. Various Web applications focus on identifying a
subgraph, such as focused crawlers and localized search engines.
The second challenge is to compute online personalized ranking.
Personalized search improves the quality of search results for each
user. The user needs are represented by a personalized set of pages
or personalized link importance in an entity relationship graph.
This requires an efficient online computation.
To solve the subgraph ranking problem efficiently, we estimate the
ranking scores for a subgraph. We propose a framework of an exact
solution (IdealRank) and an approximate solution (ApproxRank) for
computing ranking on a subgraph. Both IdealRank and ApproxRank
represent the set of external pages with an external node
and modify the PageRank-style transition matrix with respect to . The IdealRank algorithm assumes that the scores of external pages are known. We prove that the IdealRank scores for pages in the subgraph converge to the true PageRank scores. Since the PageRank-style scores of external pages may not typically be available, we propose the ApproxRank algorithm to estimate scores for the subgraph. We analyze the distance between IdealRank scores and ApproxRank scores of the subgraph and show that it is within a
constant factor of the distance of the external pages. We demonstrate with real and synthetic data that ApproxRank provides a good approximation to PageRank for a variety of subgraphs.
We consider online personalization using ObjectRank; it is an
authority flow based ranking for entity relationship graphs. We formalize the concept of an aggregate surfer on a data graph; the surfer's behavior is controlled by multiple personalized rankings. We prove a linearity
theorem over these rankings which can be used as a tool to scale
this type of personalization. DataApprox uses a repository of precomputed rankings for a given set of link weights assignments. We define DataApprox as an optimization problem; it selects a subset of the precomputed rankings from the repository and produce a weighted combination of these rankings. We analyze the distance between the DataApprox scores and the real authority flow ranking scores and show that DataApprox has a theoretical bound. Our experiments on the DBLP data graph show that DataApprox performs well in practice and allows fast and accurate personalized authority flow ranking
Traffic matrix estimation on a large IP backbone: a comparison on real data
This paper considers the problem of estimating the point-to-point
traffic matrix in an operational IP backbone. Contrary to previous studies, that have used a partial traffic matrix or demands estimated from aggregated Netflow traces, we use a unique data set of complete traffic matrices from a global IP network measured over five-minute intervals. This allows us to do an accurate data analysis on the time-scale of typical link-load measurements and enables us to make a balanced evaluation of different traffic matrix estimation techniques. We describe the data collection infrastructure, present spatial and temporal demand distributions, investigate the stability of fan-out factors, and analyze the mean-variance relationships between demands. We perform a critical evaluation of existing and novel methods for traffic matrix estimation, including recursive fanout estimation, worst-case bounds, regularized estimation techniques, and methods that rely on mean-variance relationships. We discuss the weaknesses and strengths of the various methods, and highlight differences in the results for the European and American subnetworks
- β¦