Energy
Stochastic Normalizing Flows
By invoking ideas from non-equilibrium statistical mechanics we derive an efficient training procedure by which both the sampler's and the flow's parameters can be optimized end-to-end, and by which we can compute
Text-Based Interactive Recommendation via Constraint-Augmented Reinforcement Learning
Ruiyi Zhang, Tong Yu, Yilin Shen, Hongxia Jin, Changyou Chen
However, recommendations can easily violate preferences of users from their past natural-language feedback, since the recommender needs to explore new items for further improvement.
Export Reviews, Discussions, Author Feedback and Meta-Reviews
First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. The paper under review, Optimizing Energy Production Using Policy Search describes a policy search algorithm for optimizing the energy production in a hydroelectric power plant. First, the problem is specified with a model of the system, the goal and the constraints. Afterwards, a predictive state representation is introduced for the inflow process. Finally, a policy search algorithm based on a random local search is presented and evaluated on a dataset of a real power-plant.
Convolutional Networks on Graphs for Learning Molecular Fingerprints
We introduce a convolutional neural network that operates directly on graphs. These networks allow end-to-end learning of prediction pipelines whose inputs are graphs of arbitrary size and shape. The architecture we present generalizes standard molecular feature extraction methods based on circular fingerprints. We show that these data-driven features are more interpretable, and have better predictive performance on a variety of tasks.
Shape and Time Distortion Loss for Training Deep Time Series Forecasting Models
Vincent LE GUEN, Nicolas THOME
This paper addresses the problem of time series forecasting for non-stationary signals and multiple future steps prediction.
Learning nonlinear level sets for dimensionality reduction in function approximation
Guannan Zhang, Jiaxin Zhang, Jacob Hinkle
Neural Information Processing Systems http://nips.cc/
Mixed Robust/Average Submodular Partitioning: Fast Algorithms, Guarantees, and Applications
Kai Wei, Rishabh K. Iyer, Shengjie Wang, Wenruo Bai, Jeff A. Bilmes
Neural Information Processing Systems http://nips.cc/
Modelling the Dynamics of Multiagent Q-Learning in Repeated Symmetric Games: a Mean Field Theoretic Approach
Shuyue Hu, Chin-wing Leung, Ho-fung Leung
Neural Information Processing Systems http://nips.cc/
Probabilistic Time Series Forecasting with Structured Shape and Temporal Diversity Vincent Le Guen
Probabilistic forecasting consists in predicting a distribution of possible future outcomes. In this paper, we address this problem for non-stationary time series, which is very challenging yet crucially important. We introduce the STRIPE model for representing structured diversity based on shape and time features, ensuring both probable predictions while being sharp and accurate.