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Found 9 papers, 3 papers with code
Differential Alignment for Domain Adaptive Object Detection
Domain adaptive object detection (DAOD) aims to generalize an object detector trained on labeled source-domain data to a target domain without annotations, the core principle of which is \emph{source-target feature alignment}.
DeBaTeR: Denoising Bipartite Temporal Graph for Recommendation
Due to the difficulty of acquiring large-scale explicit user feedback, implicit feedback (e. g., clicks or other interactions) is widely applied as an alternative source of data, where user-item interactions can be modeled as a bipartite graph.
NTIRE 2024 Challenge on Low Light Image Enhancement: Methods and Results
This paper reviews the NTIRE 2024 low light image enhancement challenge, highlighting the proposed solutions and results.
Knowledge NeRF: Few-shot Novel View Synthesis for Dynamic Articulated Objects
To clearly reconstruct dynamic scenes, we propose a new framework by considering two frames at a time. We pretrain a NeRF model for an articulated object. When articulated objects moves, Knowledge NeRF learns to generate novel views at the new state by incorporating past knowledge in the pretrained NeRF model with minimal observations in the present state.
Context Matters: A Strategy to Pre-train Language Model for Science Education
However, the ideal type of data to contextualize pre-trained language model and improve the performance in automatically scoring student written responses remains unclear.
Matching Exemplar as Next Sentence Prediction (MeNSP): Zero-shot Prompt Learning for Automatic Scoring in Science Education
Developing models to automatically score students' written responses to science problems is critical for science education.
Data-driven Method for Estimating Aircraft Mass from Quick Access Recorder using Aircraft Dynamics and Multilayer Perceptron Neural Network
This paper presents a data-driven method involving use of the quick access recorder (QAR)-a digital flight-data recorder-installed on all aircrafts to record the initial aircraft climb mass during each flight.
Optimal Learning for Stochastic Optimization with Nonlinear Parametric Belief Models
We consider the problem of estimating the expected value of information (the knowledge gradient) for Bayesian learning problems where the belief model is nonlinear in the parameters.
