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05/15/2023
Straightening Out the Straight-Through Estimator: Overcoming Optimization Challenges in Vector Quantized Networks
This work examines the challenges of training neural networks using vect...
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12/22/2021
Meta-Learning and Self-Supervised Pretraining for Real World Image Translation
Recent advances in deep learning, in particular enabled by hardware adva...
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07/15/2021
Recurrent Parameter Generators
We present a generic method for recurrently using the same parameters fo...
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03/18/2021
The Low-Rank Simplicity Bias in Deep Networks
Modern deep neural networks are highly over-parameterized compared to th...
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08/15/2020
Cautious Adaptation For Reinforcement Learning in Safety-Critical Settings
Reinforcement learning (RL) in real-world safety-critical target setting...
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10/09/2019
Word Embedding Visualization Via Dictionary Learning
Co-occurrence statistics based word embedding techniques have proved to ...
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02/14/2019
Superposition of many models into one
We present a method for storing multiple models within a single set of p...
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03/31/2018
Learning Unsupervised Learning Rules
A major goal of unsupervised learning is to discover data representation...
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03/23/2018
Generalization Challenges for Neural Architectures in Audio Source Separation
Recent work has shown that recurrent neural networks can be trained to s...
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03/23/2018
Convolutional vs. Recurrent Neural Networks for Audio Source Separation
Recent work has shown that recurrent neural networks can be trained to s...
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02/22/2018
Adversarial Examples that Fool both Human and Computer Vision
Machine learning models are vulnerable to adversarial examples: small ch...
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11/28/2016
Emergence of foveal image sampling from learning to attend in visual scenes
We describe a neural attention model with a learnable retinal sampling l...
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12/20/2014
Discovering Hidden Factors of Variation in Deep Networks
Deep learning has enjoyed a great deal of success because of its ability...
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07/31/2013
