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Found 13 papers, 1 papers with code
A Survey of Classical And Quantum Sequence Models
Our primary objective is to conduct a brief survey of various classical and quantum neural net sequence models, which includes self-attention and recurrent neural networks, with a focus on recent quantum approaches proposed to work with near-term quantum devices, while exploring some basic enhancements for these quantum models.
An Optimistic-Robust Approach for Dynamic Positioning of Omnichannel Inventories
Using a real-world dataset from a large American omnichannel retail chain, a business value assessment during a peak period indicates over a 15% profitability gain for BIO over RO and other baselines while also preserving the (practical) worst case performance.
Hierarchical Proxy Modeling for Improved HPO in Time Series Forecasting
To address this test-validation mismatch, we propose a novel technique, H-Pro to drive HPO via test proxies by exploiting data hierarchies often associated with time series datasets.
Towards Creativity Characterization of Generative Models via Group-based Subset Scanning
We propose group-based subset scanning to identify, quantify, and characterize creative processes by detecting a subset of anomalous node-activations in the hidden layers of the generative models.
Deep Policy Iteration with Integer Programming for Inventory Management
Finally, to make RL algorithms more accessible for inventory management researchers, we also discuss a modular Python library developed that can be used to test the performance of RL algorithms with various supply chain structures.
Learning to shortcut and shortlist order fulfillment deciding
Essentially, those assignments that can be predicted with high confidence can be used to shortcut, or bypass, the expensive deciding process, or else a set of most likely assignments can be used for shortlisting -- sending a much smaller set of candidates for consideration by the fulfillment deciding system.
Predicting Deep Neural Network Generalization with Perturbation Response Curves
However, despite these successes, the recent Predicting Generalization in Deep Learning (PGDL) NeurIPS 2020 competition suggests that there is a need for more robust and efficient measures of network generalization.
Gi and Pal Scores: Deep Neural Network Generalization Statistics
The field of Deep Learning is rich with empirical evidence of human-like performance on a variety of regression, classification, and control tasks.
Towards creativity characterization of generative models via group-based subset scanning
We propose group-based subset scanning to quantify, detect, and characterize creative processes by detecting a subset of anomalous node-activations in the hidden layers of generative models.
Temporal Latent Auto-Encoder: A Method for Probabilistic Multivariate Time Series Forecasting
Probabilistic forecasting of high dimensional multivariate time series is a notoriously challenging task, both in terms of computational burden and distribution modeling.
Practical application improvement to Quantum SVM: theory to practice
For the quantum SVM under NISQ, we use quantum feature maps to translate data into quantum states and build the SVM kernel out of these quantum states, and further compare with classical SVM with radial basis function (RBF) kernels.
Machine learning based co-creative design framework
We propose a flexible, co-creative framework bringing together multiple machine learning techniques to assist human users to efficiently produce effective creative designs.
Toward A Neuro-inspired Creative Decoder
Creativity, a process that generates novel and meaningful ideas, involves increased association between task-positive (control) and task-negative (default) networks in the human brain.
