Skip to yearly menu bar Skip to main content

( events)   Timezone:  
Fri Dec 13 08:00 AM -- 07:00 PM (PST) @ West 220 - 222
Shared Visual Representations in Human and Machine Intelligence
Arturo Deza · Joshua Peterson · Apurva Ratan Murty · Tom Griffiths

Workshop Home Page

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 unsupervised learning).

Thus we propose to invite leading cognitive scientists with strong computational backgrounds to disseminate their findings to the machine learning community with the hope of closing the loop by nourishing new ideas and creating cross-disciplinary collaborations.

See more information at the official conference website:
Follow us on twitter for announcements:

Opening Remarks
Predictable representations in humans and machines (Talk)
What is disentangling and does intelligence need it? (Talk)
Coffee Break (Break)
A "distribution mismatch" dataset for comparing representational similarity in ANNs and the brain (Talk)
Feathers, wings and the future of computer vision research (Talk)
Taxonomic structure in learning from few positive examples (Talk)
CIFAR-10H: using human-derived soft-label distributions to support more robust and generalizable classification (Talk)
Making the next generation of machine learning datasets: ObjectNet a new object recognition benchmark (Talk)
The building blocks of vision (Talk)
Poster Session
Q&A from the Audience. Ask the Grad Students (Discussion Panel)
Object representation in the human visual system (Talk)
Cognitive computational neuroscience of vision (Talk)
Perturbation-based remodeling of visual neural network representations (Talk)
Local gain control and perceptual invariances (Talk)
Panel Discussion: What sorts of cognitive or biological (architectural) inductive biases will be crucial for developing effective artificial intelligence? (Discussion Panel)
Concluding Remarks & Prizes Ceremony (Concluding Remarks)
Evening Reception (Reception)