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Found 10 papers, 3 papers with code
Bit-mask Robust Contrastive Knowledge Distillation for Unsupervised Semantic Hashing
In this paper, we propose an innovative Bit-mask Robust Contrastive knowledge Distillation (BRCD) method, specifically devised for the distillation of semantic hashing models.
Towards the Identifiability and Explainability for Personalized Learner Modeling: An Inductive Paradigm
However, we notice that this paradigm leads to the inevitable non-identifiability and explainability overfitting problem, which is harmful to the quantification of learners' cognitive states and the quality of web learning services.
Model-Free Market Risk Hedging Using Crowding Networks
Crowding is widely regarded as one of the most important risk factors in designing portfolio strategies.
Learning by Applying: A General Framework for Mathematical Reasoning via Enhancing Explicit Knowledge Learning
In LeAp, we perform knowledge learning in a novel problem-knowledge-expression paradigm, with a Knowledge Encoder to acquire knowledge from problem data and a Knowledge Decoder to apply knowledge for expression reasoning.
A Novel Approach for Auto-Formulation of Optimization Problems
In the Natural Language for Optimization (NL4Opt) NeurIPS 2022 competition, competitors focus on improving the accessibility and usability of optimization solvers, with the aim of subtask 1: recognizing the semantic entities that correspond to the components of the optimization problem; subtask 2: generating formulations for the optimization problem.
Monotonic Risk Relationships under Distribution Shifts for Regularized Risk Minimization
Machine learning systems are often applied to data that is drawn from a different distribution than the training distribution.
Test-Time Training Can Close the Natural Distribution Shift Performance Gap in Deep Learning Based Compressed Sensing
We show that for four natural distribution shifts, this method essentially closes the distribution shift performance gap for state-of-the-art architectures for accelerated MRI.
Combining Reinforcement Learning and Inverse Reinforcement Learning for Asset Allocation Recommendations
We suggest a simple practical method to combine the human and artificial intelligence to both learn best investment practices of fund managers, and provide recommendations to improve them.
Effective Version Space Reduction for Convolutional Neural Networks
In active learning, sampling bias could pose a serious inconsistency problem and hinder the algorithm from finding the optimal hypothesis.
Ranked #108 on
Image Classification
on STL-10
An Iterative Heuristic Method to Determine Radial Topology for Distribution System Restoration
The case studies indicate that the proposed method can determine radial topology in a few seconds and ensure the restoration capacity.
