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Found 21 papers, 8 papers with code
Neural Compression of Atmospheric States
Atmospheric states derived from reanalysis comprise a substantial portion of weather and climate simulation outputs.
Evaluating Model Bias Requires Characterizing its Mistakes
We demonstrate that characterizing (as opposed to simply quantifying) model mistakes across subgroups is pivotal to properly reflect model biases, which are ignored by standard metrics such as worst-group accuracy or accuracy gap.
Solving MaxSAT with Matrix Multiplication
Since matrix multiplication is the main computational primitive for block Gibbs sampling in an RBM, our approach leads to an elegantly simple algorithm (40 lines of JAX) well-suited for neural network accelerators.
Entropic Desired Dynamics for Intrinsic Control
An agent might be said, informally, to have mastery of its environment when it has maximised the effective number of states it can reliably reach.
Learning more skills through optimistic exploration
However, an inherent exploration problem lingers: when a novel state is actually encountered, the discriminator will necessarily not have seen enough training data to produce accurate and confident skill classifications, leading to low intrinsic reward for the agent and effective penalization of the sort of exploration needed to actually maximize the objective.
Relative Variational Intrinsic Control
In the absence of external rewards, agents can still learn useful behaviors by identifying and mastering a set of diverse skills within their environment.
Q-Learning in enormous action spaces via amortized approximate maximization
Applying Q-learning to high-dimensional or continuous action spaces can be difficult due to the required maximization over the set of possible actions.
Fast Task Inference with Variational Intrinsic Successor Features
It has been established that diverse behaviors spanning the controllable subspace of an Markov decision process can be trained by rewarding a policy for being distinguishable from other policies \citep{gregor2016variational, eysenbach2018diversity, warde2018unsupervised}.
Unsupervised Control Through Non-Parametric Discriminative Rewards
Learning to control an environment without hand-crafted rewards or expert data remains challenging and is at the frontier of reinforcement learning research.
Variational Approaches for Auto-Encoding Generative Adversarial Networks
In this paper, we develop a principle upon which auto-encoders can be combined with generative adversarial networks by exploiting the hierarchical structure of the generative model.
Theano: A Python framework for fast computation of mathematical expressions
Since its introduction, it has been one of the most used CPU and GPU mathematical compilers - especially in the machine learning community - and has shown steady performance improvements.
Blocks and Fuel: Frameworks for deep learning
We introduce two Python frameworks to train neural networks on large datasets: Blocks and Fuel.
Brain Tumor Segmentation with Deep Neural Networks
Finally, we explore a cascade architecture in which the output of a basic CNN is treated as an additional source of information for a subsequent CNN.
Ranked #1 on
Brain Tumor Segmentation
on BRATS-2013 leaderboard
EmoNets: Multimodal deep learning approaches for emotion recognition in video
The task of the emotion recognition in the wild (EmotiW) Challenge is to assign one of seven emotions to short video clips extracted from Hollywood style movies.
Self-informed neural network structure learning
We study the problem of large scale, multi-label visual recognition with a large number of possible classes.
Generative Adversarial Nets
We propose a new framework for estimating generative models via adversarial nets, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake.
Generative Adversarial Networks
We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake.
Super-Resolution
Time-Series Few-Shot Learning with Heterogeneous Channels
An empirical analysis of dropout in piecewise linear networks
The recently introduced dropout training criterion for neural networks has been the subject of much attention due to its simplicity and remarkable effectiveness as a regularizer, as well as its interpretation as a training procedure for an exponentially large ensemble of networks that share parameters.
Maxout Networks
We consider the problem of designing models to leverage a recently introduced approximate model averaging technique called dropout.
Ranked #31 on
Image Classification
on MNIST
Theano: new features and speed improvements
Theano is a linear algebra compiler that optimizes a user's symbolically-specified mathematical computations to produce efficient low-level implementations.
