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Found 7 papers, 0 papers with code
Contextual Conservative Q-Learning for Offline Reinforcement Learning
In this paper, we propose Contextual Conservative Q-Learning(C-CQL) to learn a robustly reliable policy through the contextual information captured via an inverse dynamics model.
TriDoNet: A Triple Domain Model-driven Network for CT Metal Artifact Reduction
Recent deep learning-based methods have achieved promising performance for computed tomography metal artifact reduction (CTMAR).
A Content-Based Approach to Email Triage Action Prediction: Exploration and Evaluation
Email has remained a principal form of communication among people, both in enterprise and social settings.
Combinatorial Topic Models using Small-Variance Asymptotics
Topic models have emerged as fundamental tools in unsupervised machine learning.
Revisiting Kernelized Locality-Sensitive Hashing for Improved Large-Scale Image Retrieval
We present a simple but powerful reinterpretation of kernelized locality-sensitive hashing (KLSH), a general and popular method developed in the vision community for performing approximate nearest-neighbor searches in an arbitrary reproducing kernel Hilbert space (RKHS).
Small-Variance Asymptotics for Hidden Markov Models
Starting with the standard HMM, we first derive a “hard” inference algorithm analogous to k-means that arises when particular variances in the model tend to zero.
Small-Variance Asymptotics for Exponential Family Dirichlet Process Mixture Models
Links between probabilistic and non-probabilistic learning algorithms can arise by performing small-variance asymptotics, i. e., letting the variance of particular distributions in a graphical model go to zero.
