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Found 15 papers, 3 papers with code
Guided Safe Shooting: model based reinforcement learning with safety constraints
In the last decade, reinforcement learning successfully solved complex control tasks and decision-making problems, like the Go board game.
Knothe-Rosenblatt transport for Unsupervised Domain Adaptation
Knothe-Rosenblatt Domain Adaptation (KRDA) is based on the Knothe-Rosenblatt transport: we exploit autoregressive density estimation algorithms to accurately model the different sources by an autoregressive model using a mixture of Gaussians.
The guide and the explorer: smart agents for resource-limited iterated batch reinforcement learning
Iterated batch reinforcement learning (RL) is a growing subfield fueled by the demand from systems engineers for intelligent control solutions that they can apply within their technical and organizational constraints.
Model-based micro-data reinforcement learning: what are the crucial model properties and which model to choose?
We contribute to micro-data model-based reinforcement learning (MBRL) by rigorously comparing popular generative models using a fixed (random shooting) control agent.
EMTL: A Generative Domain Adaptation Approach
We propose an unsupervised domain adaptation approach based on generative models.
Tropical Cyclone Track Forecasting using Fused Deep Learning from Aligned Reanalysis Data
The forecast of tropical cyclone trajectories is crucial for the protection of people and property.
InsectUp: Crowdsourcing Insect Observations to Assess Demographic Shifts and Improve Classification
Insects play such a crucial role in ecosystems that a shift in demography of just a few species can have devastating consequences at environmental, social and economic levels.
Spurious samples in deep generative models: bug or feature?
Traditional wisdom in generative modeling literature is that spurious samples that a model can generate are errors and they should be avoided.
Similarity encoding for learning with dirty categorical variables
We show that a simple approach that exposes the redundancy to the learning algorithm brings significant gains.
Machine learning for classification and quantification of monoclonal antibody preparations for cancer therapy
Monoclonal antibodies constitute one of the most important strategies to treat patients suffering from cancers such as hematological malignancies and solid tumors.
Digits that are not: Generating new types through deep neural nets
For an artificial creative agent, an essential driver of the search for novelty is a value function which is often provided by the system designer or users.
Correlation-based construction of neighborhood and edge features
Next, we connect neighborhood features that correlate, and construct edge features by subtracting the correlated neighborhood features of each other.
The return of AdaBoost.MH: multi-class Hamming trees
Within the framework of AdaBoost. MH, we propose to train vector-valued decision trees to optimize the multi-class edge without reducing the multi-class problem to $K$ binary one-against-all classifications.
Algorithms for Hyper-Parameter Optimization
Random search has been shown to be sufficiently efficient for learning neural networks for several datasets, but we show it is unreliable for training DBNs.
Learning the 2-D Topology of Images
We study the following question: is the two-dimensional structure of images a very strong prior or is it something that can be learned with a few examples of natural images?
