In more detail: Exploiting symmetry in structured data is a powerful way to improve learning and generalization ability of AI systems, and extract higher quality information, in applications from vision and NLP to robotics. This is exemplified by convolutional neural nets, which are an ubiquitous architecture. Recently, there has been a great deal of progress to develop improved equivariant and invariant learning architectures, as well as improved data augmentation methods. There has also been progress on the theoretical foundations of the area, from the perspectives of statistics and optimization. The notion of adding data via data augmentation also arises in problems such as adversarial robustness. This workshop will bring together leading researchers in the area to discuss the state of the art of the field. The activity is part of the Center for Foundations of Information Processing at Penn (https://finpenn.seas.upenn.edu/), supported by NSF TRIPODS, and jointly organized by Edgar Dobriban and Kostas Daniilidis.
Best,
Edgar
Special session on Previously published Journal Articles
August 25th, 2020 
Daniela Lopez de Luise The International Joint Conference on Biometrics (IJCB 2020) is
organizing a special session on previously published journal papers.
Authors of published journal articles on topics relevant to the
biometric community will have the opportunity to present and publicize
their work at IJCB 2020. The presentation is subject to space
availability and approval by the IJCB Program Chairs. Articles from
high-impact journals, such as TPAMI, TIFS, TIP or TBIOM will be given
preference. Journal papers published or journal manuscripts accepted
between September 1, 2019 and August 27, 2020 are eligible for
consideration.
*** Submission procedure ***
Interested authors should submit the following information to the IJCB
program chairs:
1. Copy of the journal paper
2. One paragraph description of the technical significance of their work
3. CV of the presenter
4. Other optional material
Authors, whose papers are selected for presentation, need to register
for the conference at the author rate in order to present their work.
The selected papers will not be reprinted nor archived by IJCB 2020.
For more information see:
https://ieee-biometrics.org/ijcb2020/Program.html#cfss
*** Contact information ***
Vitomir Štruc (vitomir.struc@fe.uni-lj.si)
*** Important Dates ***
Proposal submission: August 28, 2020
Notifications: September 4, 2020
AISTATS 2021 Call for Papers
August 25th, 2020 Daniela Lopez de Luise
Dear all,
(Posting on behalf of the AISTATS 2021 organizing committee)
We invite submissions to the 2021 International Conference on Artificial Intelligence and Statistics (AISTATS), and welcome paper submissions on artificial intelligence, machine learning, statistics, and related areas. ar
Key dates
The tentative dates are as follow:
· Abstract submission: October 8, 2020, 08:00 AM PDT
· Paper submission date: October 15, 2020, 08:00 AM PDT
· Reviews released: November 23, 2020
· Author rebuttals due: November 28, 2020
· Final decisions: January 08, 2021
· Conference dates: April 13-15, 2021
Summary
AISTATS is an interdisciplinary gathering of researchers at the intersection of computer science, artificial intelligence, machine learning, statistics, and related areas. Since its inception in 1985, the primary goal of AISTATS has been to broaden research in these fields by promoting the exchange of ideas among them. We encourage the submission of all papers which are in keeping with this objective at AISTATS.
Current website: https://www.aistats.org/aistats2021/
Paper Submission:
Proceedings track: This is the standard AISTATS paper submission track. Papers will be selected via a rigorous double-blind peer-review process. All accepted papers will be presented at the Conference as contributed talks or as posters and will be published in the Proceedings.
Solicited topics include, but are not limited to:
· Models and estimation: graphical models, causality, Gaussian processes, approximate inference, kernel methods, nonparametric models, statistical and computational learning theory, manifolds and embedding, sparsity and compressed sensing, …
· Classification, regression, density estimation, unsupervised and semi-supervised learning, clustering, topic models, …
· Structured prediction, relational learning, logic and probability
· Reinforcement learning, planning, control
· Game theory, no-regret learning, multi-agent systems
· Algorithms and architectures for high-performance computation in AI and statistics
· Software for and applications of AI and statistics
· Deep learning including optimization, generalization and architectures
· Trustworthy learning, including learning with privacy and fairness, interpretability, and robustness
Formatting and Supplementary Material
Submissions are limited to 8 pages excluding references using the LaTeX style file we provide. The number of pages containing citations alone is not limited. You can also submit a single file of additional supplementary material which may be either a pdf file (such as proof details) or a zip file for other formats/more files (such as code or videos). Note that reviewers are under no obligation to examine your supplementary material. If you have only one supplementary pdf file, please upload it as is; otherwise gather everything to the single zip file.
Submissions will be through CMT (https://cmt3.research.microsoft.com/AISTATS2021) and will be open a month before the abstract submission deadline.
Formatting information (including LaTeX style files) will be made available. We do not support submission in preparation systems other than LaTeX. Please do not modify the layout given by the style file. If you have questions about the style file or its usage, please contact the publications chair.
Anonymization Requirements
The AISTATS review process is double-blind. Please remove all identifying information from your submission, including author names, affiliations, and any acknowledgments. Self-citations can present a special problem: we recommend leaving in a moderate number of self-citations for published or otherwise well-known work. For unpublished or less-well-known work, or for large numbers of self-citations, it is up to the author's discretion how best to preserve anonymity. Possibilities include leaving out a citation altogether, including it but replacing the citation text with “removed for anonymous submission,” or leaving the citation as-is; authors should choose for each citation the treatment which is least likely to reveal authorship.
Previous tech-report or workshop versions of a paper can similarly present a problem for anonymization. We suggest leaving out any identifying information for such versions, but bringing them to the attention of the program committee via the submission page. Reviewers will be instructed that tech reports (including reports on sites such as arXiv) and papers in workshops without archival proceedings do not count as prior publication.
Previous or Concurrent Submissions
Submitted manuscripts should not have been previously published in a journal or in the proceedings of a conference, and should not be under consideration for publication at another conference at any point during the AISTATS review process. It is acceptable to have a substantially extended version of the submitted paper under consideration simultaneously for journal publication, so long as the journal version's planned publication date is in May 2021 or later, the journal submission does not interfere with AISTATS's right to publish the paper, and the situation is clearly described at the time of AISTATS submission. Please describe the situation in the appropriate box on the submission page (and do not include author information in the submission itself, to avoid accidental unblinding).
As mentioned above, reviewers will be instructed that tech reports (including reports on sites such as arXiv) and papers in workshops without archival proceedings do not count as prior publication.
All accepted papers will be presented at the Conference either as contributed talks or as posters, and will be published in the AISTATS Conference Proceedings in the Journal of Machine Learning Research Workshop and Conference Proceedings series. Papers for talks and posters will be treated equally in publication.
Please contact us with any questions at aistats2021pc@gmail.com.
Arindam Banerjee and Kenji Fukumizu
AICON 2020 (virtual) – Discover co-located workshops and submit your paper! Conference on Artificial Intelligence for Communications and Networks (Springer, Scopus, ISI, more) December 19-20, 2020
August 25th, 2020 Daniela Lopez de Luise
|
On-Site and Remote: September 3 Deadline for eKNOW 2020 || November 21 – 25, 2020 – Valencia, Spain
August 23rd, 2020 Daniela Lopez de Luise Greetings,
With everyone's health and safety in mind, we are adapting some of the ways in which IARIA conferences are organized:
1. The submission deadlines (and other dates such as notification and camera ready) are rather flexible in order to account for possibly limited author access to the academic/industrial premises where the research work is taking place.
2. During the conference, authors will be able to present their work via pre-recorded videos and/or conference calls (in case they opt to avoid travel).
3. Selection of awarded papers and invitations for expanded versions for IARIA journals are not affected by the presentation method (pre recorded or in person).
We wish you health and safety during these times.
That being said, note that the submission deadline has been extended to September 3.
Please consider to contribute to and/or forward to the appropriate groups the following opportunity to submit and publish original scientific results to:
– eKNOW 2020, The Twelfth International Conference on Information, Process, and Knowledge Management
Authors of selected papers will be invited to submit extended article versions to one of the IARIA Journals: https://www.iariajournals.org
=================
============== eKNOW 2020 | Call for Papers ===============
CALL FOR PAPERS, TUTORIALS, PANELS
eKNOW 2020, The Twelfth International Conference on Information, Process, and Knowledge Management
General page: http://www.iaria.org/conferences2020/eKNOW20.html
Submission page: http://www.iaria.org/conferences2020/SubmiteKNOW20.html
Event schedule: November 21 – 25, 2020 – Valencia, Spain
Contributions:
– regular papers [in the proceedings, digital library]
– short papers (work in progress) [in the proceedings, digital library]
– ideas: two pages [in the proceedings, digital library]
– extended abstracts: two pages [in the proceedings, digital library]
– posters: two pages [in the proceedings, digital library]
– posters: slide only [slide-deck posted at www.iaria.org]
– presentations: slide only [slide-deck posted at www.iaria.org]
– demos: two pages [posted at www.iaria.org]
– doctoral forum submissions: [in the proceedings, digital library]
Proposals for:
– mini symposia: see http://www.iaria.org/symposium.html
– workshops: see http://www.iaria.org/workshop.html
– tutorials: [slide-deck posed on www.iaria.org]
– panels: [slide-deck posed on www.iaria.org]
Submission deadline: September 3, 2020
Sponsored by IARIA, www.iaria.org
Extended versions of selected papers will be published in IARIA Journals: http://www.iariajournals.org
Print proceedings will be available via Curran Associates, Inc.: http://www.proceedings.com/9769.html
Articles will be archived in the free access ThinkMind Digital Library: http://www.thinkmind.org
The topics suggested by the conference can be discussed in terms of concepts, state of the art, research, standards, implementations, running experiments, applications, and industrial case studies. Authors are invited to submit complete unpublished papers, which are not under review in any other conference or journal in the following, but not limited to, topic areas.
All tracks are open to both research and industry contributions, in terms of Regular papers, Posters, Work in progress, Technical/marketing/business presentations, Demos, Tutorials, and Panels.
Before submission, please check and comply with the editorial rules: http://www.iaria.org/editorialrules.html
eKNOW 2020 Topics (for topics and submission details: see CfP on the site)
Call for Papers: http://www.iaria.org/conferences2020/CfPeKNOW20.html
============================================================
Knowledge fundamentals
Knowledge acquisition, processing, and management; Linguistic knowledge representation; Knowledge modeling and virtualization; Types of knowledge: structural, behavioral, relationships, etc.; Knowledge representation: visual-picture, connectionist model, semi-structured [a la workflow], structured/formal; Knowledge acquisition status: potential new knowledge, guessed semantics, confirmed semantics, auditing confirmed semantics, etc.; Knowledge update: probable insertion, validated insertion, auditing the insertion periodically based on new knowledge, etc.
Advanced topics in Deep/Machine learning
Distributed and parallel learning algorithms; Image and video coding; Deep learning and Internet of Things; Deep learning and Big data; Data preparation, feature selection, and feature extraction; Error resilient transmission of multimedia data; 3D video coding and analysis; Depth map applications; Machine learning programming models and abstractions; Programming languages for machine learning; Visualization of data, models, and predictions; Hardware-efficient machine learning methods; Model training, inference, and serving; Trust and security for machine learning applications; Testing, debugging, and monitoring of machine learning applications; Machine learning for systems.
Trends on annotation and extraction
Natural Languages-based features and systems; Annotation handling (multilingual, semantic, shared, open, prosody, etc.); Annotation as a Service (AaaS); Handling argument-based knowledge; Event-based knowledge; Tagging and supertagging; Extraction patterns; Uncertain reasoning; Visual error analysis; Domain-specific paraphrase extraction; Tweets and sentence compression; Role labeling semantic; Heterogeneous annotations
Trends on news and social media
New events-based knowledge;In-context news creation; Superlative expressions; News highlights generation systems; News special summarization systems (e.g, for blind and/or visually impaired people); Sentiment classification (emotion, irony, sarcasm, rhetorical questions, opinion, etc.); Rumor dynamics and social media; Contextual pragmatic models;; Social prediction; Prediction semantic analysis; Predictability of distrust; Aspect-based sentiment analysis; Argument generation systems; Relevance of citation recommendation; Retrieval bias and retrieval performance; High-speed captioning images; Language models for images; Participative KM platforms
Trends on knowledge processing support and mechanisms
Open knowledge bases; Structured knowledge bases; Big knowledge applications; Linked knowledge objects; Knowledge datasets; Machine translation systems; Convolution neural networks; Hybrid representations and equivalent semantics; Processing bilingual information; Topic trends and temporal signatures; Cross-view features; Pattern-based knowledge; Ranking optimization in context; Concept-based classification and ranking; KM design for life long learning and long term uses
Knowledge identification and discovery
Mining for knowledge; Knowledge identification: semantic-ID, etc.; Knowledge discovery: how to express knowledge requests?, how to find knowledge?, etc.; Knowledge refinement: after many acquisitions, former knowledge can change semantically or structurally, etc.; Knowledge clustering
Knowledge management systems
Knowledge data systems; Industrial systems; Context-aware and self-management systems; Imprecision/Uncertainty/Incompleteness in databases; Cognitive science and knowledge agent-based systems; Databases and mobility in databases; Zero-knowledge systems; Expert systems; Tutoring systems; Digital libraries
Knowledge management (KM) and event processing (EP)
Methodologies and approaches to overcome technical hurdles and improve the interplay between KM and EP; Applications from various domains (e.g. financial, manufacturing, trading, telecommunication, service), which benefit from an integrated KM and EP
Knowledge semantics processing and ontology
Dynamic knowledge ontology; Collaborative knowledge ontology; Knowledge matching; Contextual reasoning; Tools for knowledge ontology; Context-based information extraction; Knowledge trading systems; Knowledge exchange portals; Cognitive sytems and knowledge processing; Human aspects in knowledge processing
Technological foresight and socio-economic evolution modelling
Anticipatory networks and decisions; Expert information management; Foresight support systems; Generating technological recommendations and rankings; Information society evolution; Online and real-time Delphi; Ontological knowledge bases of technologies and products; Roadmapping support systems; Strategic support systems; Technological information fusion; Technological policy decision support systems
Process analysis and modeling
Analysis and development of business architectures; Data mining and information retrieval for business processes; Business process modelling; Business process composition; Analysis and management lifecycle; Reasoning on business processes; Optimization of business processes; Adaptive business processes; Business process reengineering; Integration of processes; Process discovery; Business process quality; Resource allocation
Process management
Criteria for measurement of business process models; Monitoring business processes; Business process visualization; Management of business process integration; On-demand business transformation; Performance measurement; Conformance and risk management; Prediction; Business transformation; Packaged industry applications; Industry solutions
Information management
Informational mining/retrieval/classification; Geographic and spatial data Infrastructures; Information technologies; Information management systems; Information ethics and legal evaluations; Optimization and information technology; Organizational information systems
Decision support systems
Multi-criteria decision theory; Artificial intelligence; Adaptive design for decision support systems; Support technologies: knowledge-driven, data-driven, model-driven, and geographically-driven systems; Support methods: artificial neural networks, fuzzy logic, and genetic/evolutionary algorithms; Modeling, interfaces, and performance; Applications using decision support systems