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Found 10 papers, 2 papers with code
Modulating task outcome value to mitigate real-world procrastination via noninvasive brain stimulation
A leading model posits that procrastination arises from imbalanced competing motivations: the avoidance of negative task aversiveness and the pursuit of positive task outcomes, yet this theoretical framework has not fully validated in real-world settings and not applied effectively to guide interventions.
Deep Koopman-based Control of Quality Variation in Multistage Manufacturing Systems
This paper presents a modeling-control synthesis to address the quality control challenges in multistage manufacturing systems (MMSs).
Co-Speech Gesture Video Generation via Motion-Decoupled Diffusion Model
While previous works mostly generate structural human skeletons, resulting in the omission of appearance information, we focus on the direct generation of audio-driven co-speech gesture videos in this work.
Stochastic Deep Koopman Model for Quality Propagation Analysis in Multistage Manufacturing Systems
This study introduces a stochastic deep Koopman (SDK) framework to model the complex behavior of MMSs.
DiffPrep: Differentiable Data Preprocessing Pipeline Search for Learning over Tabular Data
Data preprocessing is a crucial step in the machine learning process that transforms raw data into a more usable format for downstream ML models.
Principal Gradient Direction and Confidence Reservoir Sampling for Continual Learning
In Principal Gradient Direction, we optimize a target gradient that not only represents the major contribution of past gradients, but also retains the new knowledge of the current gradient.
Contrastive Rendering for Ultrasound Image Segmentation
However, the lack of sharp boundaries in US images still remains an inherent challenge for segmentation.
Synthesis and Edition of Ultrasound Images via Sketch Guided Progressive Growing GANs
Efficiently synthesizing realistic, editable and high resolution US images can solve the problems.
Revisiting Gradient Episodic Memory for Continual Learning
Gradient Episodic Memory (GEM) is an effective model for continual learning, where each gradient update for the current task is formulated as a quadratic program problem with inequality constraints that alleviate catastrophic forgetting of previous tasks.
Security Matters: A Survey on Adversarial Machine Learning
Adversarial machine learning is a fast growing research area, which considers the scenarios when machine learning systems may face potential adversarial attackers, who intentionally synthesize input data to make a well-trained model to make mistake.
