Search Results for author:
Found 21 papers, 4 papers with code
Active and Passive Causal Inference Learning
This paper serves as a starting point for machine learning researchers, engineers and students who are interested in but not yet familiar with causal inference.
Onchain Sports Betting using UBET Automated Market Maker
The paper underscores how decentralization in sports betting addresses the drawbacks of traditional centralized platforms, ensuring transparency, security, and lower fees.
UAMM: UBET Automated Market Maker
In this paper, we propose a new approach known as UBET AMM (UAMM), which calculates prices by considering external market prices and the impermanent loss of the liquidity pool.
Causal Effect Variational Autoencoder with Uniform Treatment
In this paper, we introduce uniform treatment variational autoencoders (UTVAE) that are trained with uniform treatment distribution using importance sampling and show that using uniform treatment over observational treatment distribution leads to better causal inference by mitigating the distribution shift that occurs from training to test time.
Online hyperparameter optimization by real-time recurrent learning
Conventional hyperparameter optimization methods are computationally intensive and hard to generalize to scenarios that require dynamically adapting hyperparameters, such as life-long learning.
Evaluation metrics for behaviour modeling
A primary difficulty with unsupervised discovery of structure in large data sets is a lack of quantitative evaluation criteria.
Are skip connections necessary for biologically plausible learning rules?
Backpropagation is the workhorse of deep learning, however, several other biologically-motivated learning rules have been introduced, such as random feedback alignment and difference target propagation.
Model-Agnostic Meta-Learning using Runge-Kutta Methods
By leveraging this refined control, we demonstrate that there are multiple principled ways to update MAML and show that the classic MAML optimization is simply a special case of second-order Runge-Kutta method that mainly focuses on fast-adaptation.
Importance Weighted Adversarial Variational Autoencoders for Spike Inference from Calcium Imaging Data
The Importance Weighted Auto Encoder (IWAE) objective has been shown to improve the training of generative models over the standard Variational Auto Encoder (VAE) objective.
Stochastic Neighbor Embedding under f-divergences
A common concern with $t$-SNE criterion is that it is optimized using gradient descent, and can become stuck in poor local minima.
Quantitatively Evaluating GANs With Divergences Proposed for Training
Generative adversarial networks (GANs) have been extremely effective in approximating complex distributions of high-dimensional, input data samples, and substantial progress has been made in understanding and improving GAN performance in terms of both theory and application.
Neural Machine Translation with Gumbel-Greedy Decoding
Previous neural machine translation models used some heuristic search algorithms (e. g., beam search) in order to avoid solving the maximum a posteriori problem over translation sentences at test time.
Generative Adversarial Parallelization
Generative Adversarial Networks have become one of the most studied frameworks for unsupervised learning due to their intuitive formulation.
An empirical analysis of the optimization of deep network loss surfaces
The success of deep neural networks hinges on our ability to accurately and efficiently optimize high-dimensional, non-convex functions.
Learning a metric for class-conditional KNN
To extend its applicability outside of image-based domains, we propose to learn a metric which captures perceptual similarity.
Generating images with recurrent adversarial networks
Gatys et al. (2015) showed that optimizing pixels to match features in a convolutional network with respect reference image features is a way to render images of high visual quality.
Denoising Criterion for Variational Auto-Encoding Framework
Denoising autoencoders (DAE) are trained to reconstruct their clean inputs with noise injected at the input level, while variational autoencoders (VAE) are trained with noise injected in their stochastic hidden layer, with a regularizer that encourages this noise injection.
Conservativeness of untied auto-encoders
We discuss necessary and sufficient conditions for an auto-encoder to define a conservative vector field, in which case it is associated with an energy function akin to the unnormalized log-probability of the data.
Understanding Minimum Probability Flow for RBMs Under Various Kinds of Dynamics
Here we propose a more general form for the sampling dynamics in MPF, and explore the consequences of different choices for these dynamics for training RBMs.
Scoring and Classifying with Gated Auto-encoders
In this work, we apply a dynamical systems view to GAEs, deriving a scoring function, and drawing connections to Restricted Boltzmann Machines.
Neural Network Regularization via Robust Weight Factorization
Regularization is essential when training large neural networks.
