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11/12/2021
Neural Population Geometry Reveals the Role of Stochasticity in Robust Perception
Adversarial examples are often cited by neuroscientists and machine lear...
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12/21/2020
Object-Centric Diagnosis of Visual Reasoning
When answering questions about an image, it not only needs knowing what ...
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10/25/2020
Improving the Reconstruction of Disentangled Representation Learners via Multi-Stage Modelling
Current autoencoder-based disentangled representation learning methods a...
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09/09/2020
not-so-BigGAN: Generating High-Fidelity Images on a Small Compute Budget
BigGAN is the state-of-the-art in high-resolution image generation, succ...
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11/19/2019
SimVAE: Simulator-Assisted Training forInterpretable Generative Models
This paper presents a simulator-assisted training method (SimVAE) for va...
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06/03/2018
Minnorm training: an algorithm for training over-parameterized deep neural networks
In this work, we propose a new training method for finding minimum weigh...
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06/03/2018
Minnorm training: an algorithm for training overcomplete deep neural networks
In this work, we propose a new training method for finding minimum weigh...
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02/17/2015
Measuring and Understanding Sensory Representations within Deep Networks Using a Numerical Optimization Framework
A central challenge in sensory neuroscience is describing how the activi...
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06/14/2013