Search Results for author:
Found 27 papers, 14 papers with code
Implicit Regularization via Neural Feature Alignment
We approach the problem of implicit regularization in deep learning from a geometrical viewpoint.
Representation Learning with Video Deep InfoMax
DeepInfoMax (DIM) is a self-supervised method which leverages the internal structure of deep networks to construct such views, forming prediction tasks between local features which depend on small patches in an image and global features which depend on the whole image.
Data-Efficient Reinforcement Learning with Self-Predictive Representations
We further improve performance by adding data augmentation to the future prediction loss, which forces the agent's representations to be consistent across multiple views of an observation.
Ranked #9 on
Atari Games 100k
on Atari 100k
Deep Reinforcement and InfoMax Learning
We begin with the hypothesis that a model-free agent whose representations are predictive of properties of future states (beyond expected rewards) will be more capable of solving and adapting to new RL problems.
Object-Centric Image Generation from Layouts
In this paper, we start with the idea that a model must be able to understand individual objects and relationships between objects in order to generate complex scenes well.
Ranked #1 on
Layout-to-Image Generation
on COCO-Stuff 64x64
Generative Adversarial Network
Layout-to-Image Generation
+1
An end-to-end approach for the verification problem: learning the right distance
In this contribution, we augment the metric learning setting by introducing a parametric pseudo-distance, trained jointly with the encoder.
Attraction-Repulsion Actor-Critic for Continuous Control Reinforcement Learning
Continuous control tasks in reinforcement learning are important because they provide an important framework for learning in high-dimensional state spaces with deceptive rewards, where the agent can easily become trapped into suboptimal solutions.
Unsupervised State Representation Learning in Atari
State representation learning, or the ability to capture latent generative factors of an environment, is crucial for building intelligent agents that can perform a wide variety of tasks.
Learning Representations by Maximizing Mutual Information Across Views
Following our proposed approach, we develop a model which learns image representations that significantly outperform prior methods on the tasks we consider.
Ranked #21 on
Image Classification
on STL-10
Batch weight for domain adaptation with mass shift
We also provide rigorous probabilistic setting for domain transfer and new simplified objective for training transfer networks, an alternative to complex, multi-component loss functions used in the current state-of-the art image-to-image translation models.
Leveraging exploration in off-policy algorithms via normalizing flows
The ability to discover approximately optimal policies in domains with sparse rewards is crucial to applying reinforcement learning (RL) in many real-world scenarios.
Unsupervised one-to-many image translation
We perform completely unsupervised one-sided image to image translation between a source domain $X$ and a target domain $Y$ such that we preserve relevant underlying shared semantics (e. g., class, size, shape, etc).
Prediction of Progression to Alzheimer's disease with Deep InfoMax
Arguably, unsupervised learning plays a crucial role in the majority of algorithms for processing brain imaging.
Spatio-Temporal Deep Graph Infomax
Spatio-temporal graphs such as traffic networks or gene regulatory systems present challenges for the existing deep learning methods due to the complexity of structural changes over time.
On Adversarial Mixup Resynthesis
In this paper, we explore new approaches to combining information encoded within the learned representations of auto-encoders.
Deep Graph Infomax
We present Deep Graph Infomax (DGI), a general approach for learning node representations within graph-structured data in an unsupervised manner.
Ranked #47 on
Node Classification
on Citeseer
Learning deep representations by mutual information estimation and maximization
In this work, we perform unsupervised learning of representations by maximizing mutual information between an input and the output of a deep neural network encoder.
On-line Adaptative Curriculum Learning for GANs
We argue that less expressive discriminators are smoother and have a general coarse grained view of the modes map, which enforces the generator to cover a wide portion of the data distribution support.
MINE: Mutual Information Neural Estimation
We argue that the estimation of mutual information between high dimensional continuous random variables can be achieved by gradient descent over neural networks.
Boundary Seeking GANs
We introduce a method for training GANs with discrete data that uses the estimated difference measure from the discriminator to compute importance weights for generated samples, thus providing a policy gradient for training the generator.
ACtuAL: Actor-Critic Under Adversarial Learning
This makes it fundamentally difficult to train GANs with discrete data, as generation in this case typically involves a non-differentiable function.
Variance Regularizing Adversarial Learning
We hypothesize that this approach ensures a non-zero gradient to the generator, even in the limit of a perfect classifier.
Boundary-Seeking Generative Adversarial Networks
We introduce a method for training GANs with discrete data that uses the estimated difference measure from the discriminator to compute importance weights for generated samples, thus providing a policy gradient for training the generator.
Maximum-Likelihood Augmented Discrete Generative Adversarial Networks
Despite the successes in capturing continuous distributions, the application of generative adversarial networks (GANs) to discrete settings, like natural language tasks, is rather restricted.
Spatio-temporal Dynamics of Intrinsic Networks in Functional Magnetic Imaging Data Using Recurrent Neural Networks
We introduce a novel recurrent neural network (RNN) approach to account for temporal dynamics and dependencies in brain networks observed via functional magnetic resonance imaging (fMRI).
Variational Autoencoders for Feature Detection of Magnetic Resonance Imaging Data
Independent component analysis (ICA), as an approach to the blind source-separation (BSS) problem, has become the de-facto standard in many medical imaging settings.
Iterative Refinement of the Approximate Posterior for Directed Belief Networks
Variational methods that rely on a recognition network to approximate the posterior of directed graphical models offer better inference and learning than previous methods.
