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08/18/2023
Active and Passive Causal Inference Learning
This paper serves as a starting point for machine learning researchers, ...
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08/11/2023
UAMM: UBET Automated Market Maker
Automated market makers (AMMs) are pricing mechanisms utilized by decent...
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02/15/2021
Online hyperparameter optimization by real-time recurrent learning
Conventional hyperparameter optimization methods are computationally int...
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07/23/2020
Evaluation metrics for behaviour modeling
A primary difficulty with unsupervised discovery of structure in large d...
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12/04/2019
Are skip connections necessary for biologically plausible learning rules?
Backpropagation is the workhorse of deep learning, however, several othe...
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10/16/2019
Model-Agnostic Meta-Learning using Runge-Kutta Methods
Meta-learning has emerged as an important framework for learning new tas...
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06/07/2019
Importance Weighted Adversarial Variational Autoencoders for Spike Inference from Calcium Imaging Data
The Importance Weighted Auto Encoder (IWAE) objective has been shown to ...
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11/03/2018
Stochastic Neighbor Embedding under f-divergences
The t-distributed Stochastic Neighbor Embedding (t-SNE) is a powerful an...
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03/02/2018
Quantitatively Evaluating GANs With Divergences Proposed for Training
Generative adversarial networks (GANs) have been extremely effective in ...
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06/22/2017
Neural Machine Translation with Gumbel-Greedy Decoding
Previous neural machine translation models used some heuristic search al...
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07/11/2016
Learning a metric for class-conditional KNN
Naive Bayes Nearest Neighbour (NBNN) is a simple and effective framework...
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02/16/2016
Generating images with recurrent adversarial networks
Gatys et al. (2015) showed that optimizing pixels to match features in a...
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12/20/2014
Neural Network Regularization via Robust Weight Factorization
Regularization is essential when training large neural networks. As deep...
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12/20/2014