Meetings/Workshops on Applied Maths: Neural Networks and Artificial Intelligence, Machine Learning in the United States (USA)

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1
Workshop IV: Deep Geometric Learning of Big Data and Applications
20 May 2019 - 24 May 2019 • Institute for Pure and Applied Mathematics (IPAM),, United States
Abstract:
Deep learning techniques have achieved impressive performance in computer vision, natural language processing and speech analysis. These tasks focus on data that lie on Euclidean domains, and mathematical tools for these domains, such as convolution, downsampling, multi-scale, and locality, are well-defined and benefit from fast computational hardware like GPUs. However, many essential data and tasks deal with non-Euclidean domains for which deep learning methods were not originally designed, such as 3D point clouds and shapes, or functional MRI. The goals of this workshop are to: 1) bring together mathematicians, data scientists and domain experts to establish the current state of these emerging techniques, 2) discuss a framework for the analysis of these new deep learning techniques, 3) establish new research directions and applications of these techniques, and 4) discuss new computer processing architecture beyond GPU adapted to non-Euclidean domains
Event listing ID:
1172436
2
LPNMR 2019), — 15th International Conference on Logic Programming and Non-monotonic Reasoning
04 Jun 2019 - 07 Jun 2019 • Philadelphia PA, United States
Abstract:
LPNMR 2019 is the fifteenth in the series of international meetings on logic programming and non-monotonic reasoning. LPNMR is a forum for exchanging ideas on declarative logic programming, non-monotonic reasoning, and knowledge representation. The aim of the conference is to facilitate interactions between researchers and practitioners interested in the design and implementation of logic-based programming languages and database systems, and those working in knowledge representation and nonmonotonic reasoning. LPNMR strives to encompass theoretical and experimental studies that have led or will lead to advances in declarative programming and knowledge representation, as well as their use in practical applications. A Doctoral Consortium will also be a part of the program.
Event listing ID:
1127669
3
Machine Learning & AI Developers Conference
05 Jun 2019 - 06 Jun 2019 • Santa Clara, CA, United States
Abstract:
The Machine Learning & AI DevCon will explore the hardware and software challenges of building up and debugging the complex machine learning and artificial intelligence systems in applications such as such as medical devices, robots, drones, point-of-sale equipment, and industrial automation and others.
Topics:
Machine learning and AI algorithms, Machine learning and AI hardware – processors and systems, Applying ML and AI in automotive, industrial, medical, and other applications, Introduction to ML and AI, The future of what Machine Learning and AI and Augmented Reality will bring
Event listing ID:
1147444
Event website:
4
ICML 2019 — Thirty-sixth International Conference on Machine Learni
10 Jun 2019 - 15 Jun 2019 • Long Beach, CA, United States
Organizer:
International Machine Learning Society (IMLS)
Abstract:
ICML is the leading international machine learning conference and is supported by the International Machine Learning Society (IMLS).
Event listing ID:
1117231
5
FUZZ-IEEE — 2019 IEEE International Conference on Fuzzy Systems
23 Jun 2019 - 26 Jun 2019 • New Orleans, LA, United States
Abstract:
FUZZ-IEEE is the foremost conference in the field of fuzzy systems. It covers all topics in fuzzy systems, from theory to applications.
Event listing ID:
895530
6
Machine Learning for Physics and the Physics of Learning
04 Sep 2019 - 08 Dec 2019 • Institute for Pure and Applied Mathematics (IPAM),, United States
Abstract:
Machine Learning (ML) is quickly providing new powerful tools for physicists and other natural scientists to extract essential information from large amounts of data, either from experiments or simulations. This IPAM long program will foster nontrivial research and provoke scientific discussion at the interface between ML and Physics. We aim to go beyond simple fitting of physical models from data and move the discussion to (i) using generative ML methods and active learning in order to generate and design complex and novel physical structures and objects, (ii) obtain models that are physically understable, e.g. by maintaining relations of the predictions to the microscopic physical quantities used as an input, (iii) using ML to learn the physical principles and mathematical structures underlying the data, and (iv) developing new ML methods inspired by methods and models developed in Physics.
Event listing ID:
1065586
7
AI Conference 2019
09 Sep 2019 - 10 Sep 2019 • San Jose, California, United States
Organizer:
O’Reilly + Intel AI
Abstract:
The AI Conference attracts the brightest minds in AI: algorithm and perception engineers, research and machine learning scientists, data scientists and data engineers, program and product managers, innovation officers and business leaders who come together to share ideas and to explore the most essential—and intriguing—topics in applied AI.
Event listing ID:
1180426
8
AI Hardware Summit 2019
17 Sep 2019 - 18 Sep 2019 • Mountain View, CA, United States
Abstract:
Join 600+ senior technology leaders from AI chip start-ups, semiconductor companies, system vendors/OEMs, data centers, end users, financial services, investors and fund managers, to build a comprehensive architectural roadmap of the emerging AI chip market.
Event listing ID:
1151041
9
Workshop I — From Passive to Active: Generative and Reinforcement Learning with Physics
23 Sep 2019 - 27 Sep 2019 • Los Angeles, CA, United States
Organizer:
Institute for Pure and Applied Mathematics (IPAM), UCLA
Abstract:
Part of the Long Program Machine Learning for Physics and the Physics of Learning.

The workshop will examine how to plan experiments in order to use information in a cost-optimal way. It will also include the application of these modalities to training complex models, such as deep architectures, and the transfer of these ideas to the generation of physically-relevant complex structures such as chemical structures, molecular structures, scalar or vector fields in fluid dynamics or electrodynamics, proposal steps for Markov chain Monte Carlo of physical systems etc.

Event listing ID:
1170986
10
Workshop II — Interpretable Learning in Physical Sciences
14 Oct 2019 - 18 Oct 2019 • Los Angeles, CA, United States
Abstract:
Part of the Long Program Machine Learning for Physics and the Physics of Learning.

The workshop will include methods to summarize and interpret a complicated learned model (e.g. deep neural network) by interrogating this model about what and why it has learned (e.g. relevance propagation and sensitivity analysis).

Event listing ID:
1170944
11
ADT 2019 — 6th International Conference on Algorithmic Decision Theory
25 Oct 2019 - 27 Oct 2027 • Durham, NC, United States
Abstract:
The ADT 2019 conference focus is on algorithmic decision theory broadly defined, seeking to bring together researchers and practitioners coming from diverse areas of Computer Science, Economics and Operations Research in order to improve the theory and practice of modern decision support.
Topics:
Algorithms, Argumentation Theory, Artificial Intelligence, Computational Social Choice, Database Systems, Decision Analysis, Discrete Mathematics, Game Theory, Machine Learning, Matching, Multi-agent Systems, Multiple Criteria Decision Aiding, Networks, Optimization, Risk Management, and Utility Theory
Event listing ID:
1193926
12
Workshop III: Validation and Guarantees in Learning Physical Models: from Patterns to Governing Equations to Laws of Nature
28 Oct 2019 - 01 Nov 2019 • UCLA, Los Angeles, California, United States
Organizer:
Institute for Pure and Applied Mathematics (IPAM), UCLA
Abstract:
Part of the Long Program Machine Learning for Physics and the Physics of Learning.

This workshop will showcase how to employ mathematical aspects of statistical / information theoretic approaches in ML for the discovery of physical laws from data. Offering statistical guarantees along with the learned models is critical in physics and in areas such as aeronautics, climate science, chemistry, biology, and robotics. We will consider model selection, robust statistics, model-free and adaptive learning, and model validation in the context of both static and dynamic models, such as equations of motion.

Event listing ID:
1170902
13
ICTAI 2019 — 31st International Conference on Tools with Artificial Intelligence
04 Nov 2019 - 06 Nov 2019 • Portland, Oregon, United States
Abstract:
ICTAI 2019: The IEEE International Conference on Tools with Artificial Intelligence (ICTAI) is a leading Conference of AI in the Computer Society providing a major international forum where the creation and exchange of ideas related to artificial intelligence are fostered among academia, industry, and government agencies.
Event listing ID:
1226432
Event website:
14
Workshop IV: Using Physical Insights for Machine Learning
18 Nov 2019 - 22 Nov 2019 • Los Angeles, CA, United States
Organizer:
Institute for Pure and Applied Mathematics (IPAM), UCLA
Abstract:
Part of the Long Program Machine Learning for Physics and the Physics of Learning.

In this workshop we will explore how to use physical intuition and ideas to design new classes of machine learning (ML) algorithms. Physics-inspired sampling algorithms could be used to train ML structures or sample the hyper-parameter space (e.g. deep Neural Networks). Additionally, physics-based models such as Ising/Potts models or energy-based models have influenced ML inference frameworks such as Markov Random Fields and Restricted Boltzmann Machines, and we want to continue the discussion to facilitate this innovation transfer. Finally, physical insight could be used to enhance learning in the situation of scarce data by enforcing smoothness, differentiability or other physical properties relevant to a given problem.

Event listing ID:
1170873
15
ICMLA 2019 — 18th International Conference on Machine Learning and Applications
16 Dec 2019 - 19 Dec 2019 • Boca Raton, FL, United States
Abstract:
The conference provides a leading international forum for the dissemination of original research in ML, with emphasis on applications as well as novel algorithms and systems. Following the success of previous ICMLA conferences, the conference aims to attract researchers and application developers from a wide range of ML related areas, and the recent emergence of Big Data processing brings an urgent need for machine learning to address these new challenges. The conference will cover both machine learning theoretical research and its applications. Contributions describing machine learning techniques applied to real-world problems and interdisciplinary research involving machine learning, in fields like medicine, biology, industry, manufacturing, security, education, virtual environments, games, are especially encouraged.
Event listing ID:
1226506
16
Workshop — Deep Learning and Medical Applications
27 Jan 2020 - 31 Jan 2020 • Los Angeles, CA, United States
Organizer:
Institute for Pure and Applied Mathematics (IPAM), UCLA
Abstract:
Rapid advances in deep learning techniques are starting to revolutionize medical imaging. Radiology, disease detection, and tissue imaging are all expected to be facilitated by automated image analysis programs in the near future. Many new interdisciplinary research questions arise; finding solutions with practical significance requires input from mathematicians, bio-physicists, and computational engineers. This workshop aims to bring together researchers from different backgrounds to explore this new frontier of science.
Event listing ID:
1172933
17
Computational Psychiatry
18 Feb 2020 - 21 Feb 2020 • Los Angeles, CA, United States
Organizer:
Institute for Pure and Applied Mathematics (IPAM), UCLA
Abstract:
Psychiatric disorders are typically diagnosed and evaluated using subjective psychological exams that assess symptoms, thoughts, feelings and behavioral patterns. Ongoing and recent advances in measurements provide EEG, functional MRI, optogenetic, genomic, and metabolic data. Along with mathematical methods developed to analyze these data, a more physiological and quantitative approach for diagnosis and treatment can be envisioned. This workshop will explore how modern computational tools and mathematical modeling can be integrated with measurements to improve psychiatric diagnosis and treatment.
Event listing ID:
1172920

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AIP Conference Proceedings
Last updated: 10 May 2019