Deep Learning
processing enables human-like zero-shot generalization to random dot stimuli
Motion is a powerful cue that humans use to detect and segment visual objects.
Towards Effective Planning Strategies for Dynamic Opinion Networks
Our experimental results demonstrate that the ranking algorithm-based classifiers provide plans that enhance infection rate control, especially with increased action budgets for small networks.
Reinforcement Learning Guided Semi-Supervised Learning
In recent years, semi-supervised learning (SSL) has gained significant attention due to its ability to leverage both labeled and unlabeled data to improve model performance, especially when labeled data is scarce. However, most current SSL methods rely on heuristics or predefined rules for generating pseudo-labels and leveraging unlabeled data.
Unitary convolutions for learning on graphs and groups
In recent years, the design of specialized machine learning architectures for structured data has received a surge of interest. Of particular interest are architectures for data domains with inherent symmetries, such as permutation-invariance in graphs and sets, translation-invariance in images, and other symmetries that arise from fundamental laws of physics in scientific data.
Unchosen Experts Can Contribute Too: Unleashing MoE Models ' Power by Self-Contrast
Mixture-of-Experts (MoE) has emerged as a prominent architecture for scaling model size while maintaining computational efficiency.
Aligning LLM Agents by Learning Latent Preference from User Edits Ge Gao Alexey T aymanov Eduardo Salinas Paul Mineiro Dipendra Misra Department of Computer Science, Cornell University
The inferred user preference descriptions are used to define prompts for generating responses in the future.
dattri: A Library for Efficient Data Attribution Junwei Deng 1* Ting-Wei Li
Data attribution methods aim to quantify the influence of individual training samples on the prediction of artificial intelligence (AI) models.
Sharpness-diversity tradeoff: improving flat ensembles with SharpBalance
Building on this, our study investigates the interplay between sharpness and diversity within deep ensembles, illustrating their crucial role in robust generalization to both in-distribution (ID) and out-of-distribution (OOD) data.