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Found 22 papers, 4 papers with code
XTransCT: Ultra-Fast Volumetric CT Reconstruction using Two Orthogonal X-Ray Projections via a Transformer Network
Computed tomography (CT) scans offer a detailed, three-dimensional representation of patients' internal organs.
A Diffusion Probabilistic Prior for Low-Dose CT Image Denoising
Subsequently, the probabilistic priors of the pre-trained diffusion model are incorporated into a Maximum A Posteriori (MAP) estimation framework for iteratively solving the image denoising problem.
Hierarchical Optimization-Derived Learning
In recent years, by utilizing optimization techniques to formulate the propagation of deep model, a variety of so-called Optimization-Derived Learning (ODL) approaches have been proposed to address diverse learning and vision tasks.
Optimization-Derived Learning with Essential Convergence Analysis of Training and Hyper-training
Recently, Optimization-Derived Learning (ODL) has attracted attention from learning and vision areas, which designs learning models from the perspective of optimization.
Towards Extremely Fast Bilevel Optimization with Self-governed Convergence Guarantees
Gradient methods have become mainstream techniques for Bi-Level Optimization (BLO) in learning and vision fields.
Revisiting GANs by Best-Response Constraint: Perspective, Methodology, and Application
In past years, the minimax type single-level optimization formulation and its variations have been widely utilized to address Generative Adversarial Networks (GANs).
Value-Function-based Sequential Minimization for Bi-level Optimization
We also extend BVFSM to address BLO with additional functional constraints.
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.
A Value-Function-based Interior-point Method for Non-convex Bi-level Optimization
Bi-level optimization model is able to capture a wide range of complex learning tasks with practical interest.
Adaptive Epidemic Forecasting and Community Risk Evaluation of COVID-19
Pandemic control measures like lock-down, restrictions on restaurants and gatherings, social-distancing have shown to be effective in curtailing the spread of COVID-19.
Cross-lingual Text Classification with Heterogeneous Graph Neural Network
Cross-lingual text classification aims at training a classifier on the source language and transferring the knowledge to target languages, which is very useful for low-resource languages.
Blockchain for Decentralized Multi-Drone to Combat COVID-19
Currently, drones represent a promising technology for combating Coronavirus disease 2019 (COVID-19) due to the transport of goods, medical supplies to a given target location in the quarantine areas experiencing an epidemic outbreak.
Distributed, Parallel, and Cluster Computing Systems and Control Systems and Control
Self-Supervised Image Prior Learning With GMM From a Single Noisy Image
It can simultaneously achieve the noise level estimation and the image prior learning directly from only a single noisy image.
Defending Against Adversarial Attacks in Transmission- and Distribution-level PMU Data
Phasor measurement units (PMUs) provide high-fidelity data that improve situation awareness of electric power grid operations.
Learning to Locomote with Deep Neural-Network and CPG-based Control in a Soft Snake Robot
Inspired by biological snakes, our control architecture is composed of two key modules: A deep reinforcement learning (RL) module for achieving adaptive goal-tracking behaviors with changing goals, and a central pattern generator (CPG) system with Matsuoka oscillators for generating stable and diverse locomotion patterns.
Improving the Interpretability of Deep Neural Networks with Knowledge Distillation
To tackle this problem, we apply the Knowledge Distillation technique to distill Deep Neural Networks into decision trees in order to attain good performance and interpretability simultaneously.
Compositional planning in Markov decision processes: Temporal abstraction meets generalized logic composition
Thus, a synthesis algorithm is developed to compute optimal policies efficiently by planning with primitive actions, policies for sub-tasks, and the compositions of sub-policies, for maximizing the probability of satisfying temporal logic specifications.
Binarized LSTM Language Model
Although excellent performance is obtained for large vocabulary tasks, tremendous memory consumption prohibits the use of LSTM LM in low-resource devices.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Interpretable Deep Convolutional Neural Networks via Meta-learning
We attempt to address this challenge by proposing a technique called CNN-INTE to interpret deep Convolutional Neural Networks (CNN) via meta-learning.
DuReader: a Chinese Machine Reading Comprehension Dataset from Real-world Applications
Experiments show that human performance is well above current state-of-the-art baseline systems, leaving plenty of room for the community to make improvements.
A Graph Traversal Based Approach to Answer Non-Aggregation Questions Over DBpedia
We present a question answering system over DBpedia, filling the gap between user information needs expressed in natural language and a structured query interface expressed in SPARQL over the underlying knowledge base (KB).
