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Found 13 papers, 4 papers with code
Robustly overfitting latents for flexible neural image compression
Finally, we show how refinement of the latents with our best-performing method improves the compression performance on the Tecnick dataset and how it can be deployed to partly move along the rate-distortion curve.
Multi-Agent Reinforcement Learning for Power Grid Topology Optimization
Recent challenges in operating power networks arise from increasing energy demands and unpredictable renewable sources like wind and solar.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
The Berkelmans-Pries Feature Importance Method: A Generic Measure of Informativeness of Features
We prove that our method has many useful properties, and accurately predicts the correct FI values for several cases where the ground truth FI can be derived in an exact manner.
The Optimal Input-Independent Baseline for Binary Classification: The Dutch Draw
The goal of this paper is to examine all baseline methods that are independent of feature values and determine which model is the `best' and why.
RangL: A Reinforcement Learning Competition Platform
The RangL project hosted by The Alan Turing Institute aims to encourage the wider uptake of reinforcement learning by supporting competitions relating to real-world dynamic decision problems.
The Dutch Draw: Constructing a Universal Baseline for Binary Prediction Models
Summarizing, the DD baseline is: (1) general, as it is applicable to all binary classification problems; (2) simple, as it is quickly determined without training or parameter-tuning; (3) informative, as insightful conclusions can be drawn from the results.
The BP Dependency Function: a Generic Measure of Dependence between Random Variables
To this end, we also provide Python code to determine the dependency function for use in practice.
Job Recommender Systems: A Review
This paper provides a review of the job recommender system (JRS) literature published in the past decade (2011-2021).
The Generalized Cascade Click Model: A Unified Framework for Estimating Click Models
To arrive at that conclusion, we will present the Generalized Cascade Model (GCM) and show how this model can be estimated using the IO-HMM EM framework, and provide two examples of how existing click models can be mapped to GCM.
Jasmine: A New Active Learning Approach to Combat Cybercrime
These approaches choose speci? fic unlabeled instances by a query function that are expected to improve overall classi? cation performance.
Personalized Stopping Rules in Bayesian Adaptive Mastery Assessment
We propose a new model to assess the mastery level of a given skill efficiently.
Decision Making
Optimization and Control
Invertible DenseNets with Concatenated LipSwish
Furthermore, we propose a learnable weighted concatenation, which not only improves the model performance but also indicates the importance of the concatenated weighted representation.
Invertible DenseNets
We introduce Invertible Dense Networks (i-DenseNets), a more parameter efficient alternative to Residual Flows.
