no code implementations • 13 Mar 2024 • SIMA Team, Maria Abi Raad, Arun Ahuja, Catarina Barros, Frederic Besse, Andrew Bolt, Adrian Bolton, Bethanie Brownfield, Gavin Buttimore, Max Cant, Sarah Chakera, Stephanie C. Y. Chan, Jeff Clune, Adrian Collister, Vikki Copeman, Alex Cullum, Ishita Dasgupta, Dario de Cesare, Julia Di Trapani, Yani Donchev, Emma Dunleavy, Martin Engelcke, Ryan Faulkner, Frankie Garcia, Charles Gbadamosi, Zhitao Gong, Lucy Gonzales, Kshitij Gupta, Karol Gregor, Arne Olav Hallingstad, Tim Harley, Sam Haves, Felix Hill, Ed Hirst, Drew A. Hudson, Jony Hudson, Steph Hughes-Fitt, Danilo J. Rezende, Mimi Jasarevic, Laura Kampis, Rosemary Ke, Thomas Keck, Junkyung Kim, Oscar Knagg, Kavya Kopparapu, Rory Lawton, Andrew Lampinen, Shane Legg, Alexander Lerchner, Marjorie Limont, YuLan Liu, Maria Loks-Thompson, Joseph Marino, Kathryn Martin Cussons, Loic Matthey, Siobhan Mcloughlin, Piermaria Mendolicchio, Hamza Merzic, Anna Mitenkova, Alexandre Moufarek, Valeria Oliveira, Yanko Oliveira, Hannah Openshaw, Renke Pan, Aneesh Pappu, Alex Platonov, Ollie Purkiss, David Reichert, John Reid, Pierre Harvey Richemond, Tyson Roberts, Giles Ruscoe, Jaume Sanchez Elias, Tasha Sandars, Daniel P. Sawyer, Tim Scholtes, Guy Simmons, Daniel Slater, Hubert Soyer, Heiko Strathmann, Peter Stys, Allison C. Tam, Denis Teplyashin, Tayfun Terzi, Davide Vercelli, Bojan Vujatovic, Marcus Wainwright, Jane X. Wang, Zhengdong Wang, Daan Wierstra, Duncan Williams, Nathaniel Wong, Sarah York, Nick Young
Building embodied AI systems that can follow arbitrary language instructions in any 3D environment is a key challenge for creating general AI.
no code implementations • 17 Nov 2022 • Nicholas A. Roy, Junkyung Kim, Neil Rabinowitz
We take a pragmatic view of the issue, and define a set of desiderata that capture both the ambitions of XAI and the practical constraints of deep learning.
no code implementations • 11 Oct 2022 • Stephanie C. Y. Chan, Ishita Dasgupta, Junkyung Kim, Dharshan Kumaran, Andrew K. Lampinen, Felix Hill
In transformers trained on controlled stimuli, we find that generalization from weights is more rule-based whereas generalization from context is largely exemplar-based.
no code implementations • NeurIPS 2021 • Drew Linsley, Girik Malik, Junkyung Kim, Lakshmi N Govindarajan, Ennio Mingolla, Thomas Serre
For this, we introduce PathTracker, a synthetic visual challenge that asks human observers and machines to track a target object in the midst of identical-looking "distractor" objects.
no code implementations • ICLR 2020 • Drew Linsley, Junkyung Kim, Alekh Ashok, Thomas Serre
We introduce a deep recurrent neural network architecture that approximates visual cortical circuits.
no code implementations • ICLR 2020 • Junkyung Kim, Drew Linsley, Kalpit Thakkar, Thomas Serre
Forming perceptual groups and individuating objects in visual scenes is an essential step towards visual intelligence.
no code implementations • NeurIPS 2018 • Drew Linsley, Junkyung Kim, Vijay Veerabadran, Charles Windolf, Thomas Serre
Progress in deep learning has spawned great successes in many engineering applications.
no code implementations • 28 Nov 2018 • Drew Linsley, Junkyung Kim, David Berson, Thomas Serre
We first demonstrate that current state-of-the-art approaches to neuron segmentation perform poorly on the challenge.
1 code implementation • NeurIPS 2018 • Drew Linsley, Junkyung Kim, Vijay Veerabadran, Thomas Serre
As a prime example, convolutional neural networks, a type of feedforward neural networks, are now approaching -- and sometimes even surpassing -- human accuracy on a variety of visual recognition tasks.
no code implementations • 9 Feb 2018 • Matthew Ricci, Junkyung Kim, Thomas Serre
The robust and efficient recognition of visual relations in images is a hallmark of biological vision.
no code implementations • ICLR 2018 • Junkyung Kim, Matthew Ricci, Thomas Serre
The robust and efficient recognition of visual relations in images is a hallmark of biological vision.