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Found 12 papers, 2 papers with code
Noise Diffusion for Enhancing Semantic Faithfulness in Text-to-Image Synthesis
Diffusion models have achieved impressive success in generating photorealistic images, but challenges remain in ensuring precise semantic alignment with input prompts.
Reliable Interval Prediction of Minimum Operating Voltage Based on On-chip Monitors via Conformalized Quantile Regression
Predicting the minimum operating voltage ($V_{min}$) of chips is one of the important techniques for improving the manufacturing testing flow, as well as ensuring the long-term reliability and safety of in-field systems.
MRGazer: Decoding Eye Gaze Points from Functional Magnetic Resonance Imaging in Individual Space
Compared to the previous method, the proposed framework skips the fMRI co-registration step, simplifies the processing protocol and achieves end-to-end eye gaze regression.
Opportunistic Episodic Reinforcement Learning
In this paper, we propose and study opportunistic reinforcement learning - a new variant of reinforcement learning problems where the regret of selecting a suboptimal action varies under an external environmental condition known as the variation factor.
Causal Explanation for Reinforcement Learning: Quantifying State and Temporal Importance
Explainability plays an increasingly important role in machine learning.
FINETUNA: Fine-tuning Accelerated Molecular Simulations
Finally, we demonstrate a technique for leveraging the interactive functionality built in to VASP to efficiently compute single point calculations within our online active learning framework without the significant startup costs.
Attention module improves both performance and interpretability of 4D fMRI decoding neural network
Finally, the model was retrained to regress individual traits within the HCP and to classify viewing images from the BOLD5000 dataset, respectively.
BWCNN: Blink to Word, a Real-Time Convolutional Neural Network Approach
The system uses a Convolutional Neural Network (CNN) to find the blinking pattern, which is defined as a series of Open and Closed states.
SPSTracker: Sub-Peak Suppression of Response Map for Robust Object Tracking
Modern visual trackers usually construct online learning models under the assumption that the feature response has a Gaussian distribution with target-centered peak response.
RGB-T Image Saliency Detection via Collaborative Graph Learning
In this paper, we propose an effective approach for RGB-T image saliency detection.
AdaLinUCB: Opportunistic Learning for Contextual Bandits
In this paper, we propose and study opportunistic contextual bandits - a special case of contextual bandits where the exploration cost varies under different environmental conditions, such as network load or return variation in recommendations.
Kernel-based Multi-Task Contextual Bandits in Cellular Network Configuration
We evaluate the effectiveness of our algorithm based on a simulator built by real traces.
