Workshops, Conferences, & Symposia

Workshops, Conferences, & Symposia

As part of the Knowledge Transfer mission of CBMM, workshops provide opportunities for disseminating knowledge, shaping new areas of scientific interest and providing fora for discussing the latest research. CBMM workshops are focused in terms of topics, invited speakers and audience while being interdisciplinary in nature. Workshops are organized in conjunction with major conferences in artificial intelligence, machine learning and neuroscience, following formal calls or proposals. These provide to CBMM the chance to participate, propose, integrate and communicate research to academia and industry. Workshops are also initiated by CBMM or partner institutions, with specific CBMM areas or Turing++ questions in mind, in order to facilitate close collaborations and shape concrete research directions for the Center and outside participants.



SVRHM 2020Shared Visual Representations in Human & Machine Intelligence | 2020 NeurIPS Workshop

December 11 - 12, 2020 | Virtual

The goal of the Shared Visual Representations in Human and Machine Intelligence (SVRHM) workshop is to disseminate relevant, parallel findings in the fields of computational neuroscience, psychology, and cognitive science that may inform modern machine learning methods.

In the past few years, machine learning methods—especially deep neural networks—have widely permeated the vision science, cognitive science, and neuroscience communities. As a result, scientific modeling in these fields has greatly benefited, producing a swath of potentially critical new insights into human learning and intelligence, which remains the gold standard for many tasks. However, the machine learning community has been largely unaware of these cross-disciplinary insights and analytical tools, which may help to solve many of the current problems that ML theorists and engineers face today (e.g., adversarial attacks, compression, continual learning, and self-supervised learning).

Genova, ItalyREGML 2020 | Regularization Methods for Machine Learning

June 29 - July 3, 2020 | Sestri Levante, Italy

RegML is a 20 hours advanced machine learning course including theory classes and practical laboratory sessions. The course covers foundations as well as recent advances in Machine Learning with emphasis on high dimensional data and a core set techniques, namely regularization methods. In many respects the course is a compressed version of the 9.520 course at MIT.

OsloMLCC 2020 @ simula | Machine Learning Crash Course

May 25-29, 2020 | Oslo, Norway

Modern machine learning (ML) is a key to develop intelligent systems and analyze data in science and engineering. Today it provides impressive results in many fields, enabling intelligent technologies such as artificial voice assistants, and smart services such as optimized energy consumption. ML systems are nowadays considered as one of the largest share of growing market. While the amount of available tools and frameworks is becoming impressive, their effective application to real-world challenges requires an appropriate expertise. However, most of ML methods leverage the same building blocks and share basic concepts. Therefore, key to applying machine learning lays in understanding the basic formulations, relating them with prototypical study cases, reasoning on the situations when they are most appropriate. This 5-day course provides an introduction to the fundamental methods at the core of modern Machine Learning, covering theoretical foundations and essential algorithms. The program includes theoretical classes, lab sessions, and an industry workshop.

Vancouver Convention CenterShared Visual Representations in Human and Machine Intelligence (SVRHM) - 2019 NeurIPS Workshop

December 13, 2019 | Vancouver, Canada

The goal of the Shared Visual Representations in Human and Machine Intelligence (SVRHM) workshop at NeurIPS 2019 is to discuss and disseminate relevant findings and parallels between the computational neuro/cognitive science and machine learning/artificial intelligence communities.

Sestri Levante, ItalyLimitations of Deep Learning Workshop

June 25-27, 2019 | Sestri Levante, Italy

The Center for Brains, Minds and Machines and The Hebrew University of Jerusalem, with generous support from the Haar family, are organizing a workshop on advances in and limitations of deep learning on June 25-27, 2019 in Sestri Levante, Italy. For three days we will bring together computer scientists, cognitive scientists, and neuroscientists to share and discuss recent advances in deep learning, with particular attention to its current limitations and how they might be overcome to develop intelligent systems and models of the human mind.

CVPR 2019 logoA workshop on language and vision at CVPR 2019

June 16-20, 2019 | Long Beach, CA

CVPR is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers.

CBMM and MIT Quest logosSymbols in the Brain Workshop

March 31, 2019 | MIT - Cambridge, MA

Prof. Tomaso Poggio, Director of the (CBMM), and Prof. Antonio Torralba, Director of MIT Quest for Intelligence, hosted an informal one-day workshop on the topic of Symbols in the Brain. The workshop organizers invited a diverse group of researchers – including, computer scientist with expertise in simulating artificial neural networks; computer science researchers with background on the theory of recursive functions and circuits; and cortical physiologists recording intracellularly and extracellularly from mammalian cortex – to discuss the still elusive questions: How do networks of neurons learn abstract concepts from the sensory world? How do networks of neurons manipulate the associate symbols?

CVPR 2018 logoA workshop on language and vision at CVPR 2018

June 18, 2018 | Salt Lake City

CVPR is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers.

Learning Disentangled Representations: from Perception to Control

December 9, 2017 | Long Beach Convention Center, CA

An important facet of human experience is our ability to break down what we observe and interact with, along characteristic lines. Visual scenes consist of separate objects, which may have different poses and identities within their category. In natural language, the syntax and semantics of a sentence can often be separated from one another. In planning and cognition plans can be broken down into immediate and long term goals. Inspired by this much research in deep representation learning has gone into finding disentangled factors of variation. However, this research often lacks a clear definition of what disentangling is or much relation to work in other branches of machine learning, neuroscience or cognitive science. In this workshop we intend to bring a wide swathe of scientists studying disentangled representations under one roof to try to come to a unified view of the problem of disentangling.

Word brainKinds of Intelligence: types, tests and meeting the needs of society

December 7, 2017 | Long Beach Convention Center, CA

This symposium explores this landscape across three main topics: a broader perspective of the possible kinds of intelligence beyond human intelligence, better measurements providing an improved understanding of research objectives and breakthroughs, and a more purposeful analysis of where progress should be made in this landscape in order to best benefit society.

CVPR 2017 logoA workshop on language and vision at CVPR 2017

July 21, 2017 | Hawaii Convention Center

CVPR is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers.

Word brainAAAI Symposium - Science of Intelligence: Computational Principles of Natural and Artificial Intelligence

March 27 - 29, 2017 | Stanford University - Palo Alto, CA

The Science of Intelligence is a new emerging field dedicated to developing a computation-based understanding of intelligence -both natural and artificial- and to establishing an engineering practice based on that understanding. This symposium is designed to bring together experts in artificial intelligence, cognitive science, and computational neuroscience to share and discuss the advances and the challenges in the scientific study of natural and artificial intelligence.

Word brainCBMM Workshop on Speech Representation, Perception and Recognition

February 2 - 3, 2017 | MIT - Cambridge, MA

The focus of the workshop is on the computations and learning involved in human speech understanding and that are required for speech-enabled machines, following CBMM’s mission to understand intelligence in brains and replicate it in machines. The workshop will bring together experts in the fields of neuroscience, perception, development, machine learning, automatic speech recognition and speech synthesis.

NIPS logoIntuitive Physics

December 9, 2016 | Barcelona, Spain

This workshop will bring together researchers in machine learning, computer vision, robotics, computational neuroscience, and cognitive development to discuss artificial systems that capture or model intuitive physics by learning from footage of, or interactions with a real or simulated environment. There will be invited talks from world leaders in the fields, presentations and poster sessions based on contributed papers, and a panel discussion.

Sestri Levante, ItalyBrains, Minds and Machines Workshop Sestri

June 20-22, 2016 | Sestri Levante, Italy

PreFor three days -- one day for each of the Brains, Minds, and Machines areas -- we will bring together computer scientists/roboticists, cognitive scientists, and neuroscientists to share and discuss advances in integrated, multimodal approaches to the study of human intelligence.

Sestri Levante, ItalyDeep Learning: Theory, Algorithms and Applications

 June 10-12, 2016 | McGovern Institute for Brain Research, MIT

The workshop aims at bringing together leading scientists in deep learning and related areas within machine learning, artificial intelligence, mathematics, statistics, and neuroscience. No formal submission is required and participation is by invitation only. Participants are invited to present their recently published work as well as work in progress, and to share their vision and perspectives for the field.

NIPS 2015Neural Information Processing Systems (NIPS) 2015

December 10, 2015 | Montréal, CANADA

Today's science, tomorrow's engineering: We will be discussing current results in the scientific understanding of intelligence and how these results enable new approaches to replicate intelligence in engineered systems.

Head posesA Turing++ Question: Who is there?

September 3rd - 5th, 2015 | McGovern Institute for Brain Research, MIT

The Center for Brains Minds and Machines (CBMM) is organizing a workshop on "Understanding Face Recognition: neuroscience, psychophysics and computation" from 3:30pm on September 3rd to 1pm on September 5th, 2015, at MIT in Cambridge. Attendance to workshop is by invitation only.

CVPR2015 Boston logoCVPR 2015 Language and Vision Workshop

Thursday, 11 June 2015 - Boston, Massachusetts

This workshop was co-organized by CBMM, Istituto Italiano di Tecnologia (IIT), MIT, Stanford University, UCLA, and the University of Surrey.

Engineering and Reverse Engineering Reinforcement LearningEngineering and Reverse Engineering Reinforcement Learning

July 18, 2014 | McGovern Institute for Brain Research, MIT

Psychologists and neuroscientists routinely borrow ideas from machine learning to understand and model reinforcement learning in humans and animals. Likewise, ideas from psychology and neuroscience filter into machine learning in a variety of ways. The goal of the workshop is to highlight some of the theoretical synergies that have arisen from this cross-pollination.

Learning Data Representation: Hierarchies and Invariance

November 22-24, 2013 | McGovern Institute for Brain Research, MIT

The goal of the meeting is to investigate advances and challenges in learning "good representations" from data, in particular representations that can reduce the complexity of later supervised learning stages. The meeting will gather experts in the field to discuss current and future challenges for the theory and applications of learning representations.