Statistical Learning
On the Convergence of CART under Sufficient Impurity Decrease Condition
The decision tree is a flexible machine learning model that finds its success in numerous applications. It is usually fitted in a recursively greedy manner using CART.
Residual Alignment: Uncovering the Mechanisms of Residual Networks
C is the number of classes; and (RA4) top singular values of Residual Jacobians scale inversely with depth.
Transformers as Statisticians: Provable In-Context Learning with In-Context Algorithm Selection Yu Bai
Building on these "base" ICL algorithms, intriguingly, we show that transformers
Semi-Supervised Contrastive Learning for Deep Regression with Ordinal Rankings from Spectral Seriation
However, these methods are limited to labeled data only unlike for classification, where unlabeled data can be used for contrastive pretraining.
GenS: Generalizable Neural Surface Reconstruction from Multi-View Images
However, current methods are impeded by requiring long-time per-scene optimizations and cannot generalize to new scenes.
Disambiguated Attention Embedding for Multi-Instance Partial-Label Learning Wei T ang
In many real-world tasks, the concerned objects can be represented as a multi-instance bag associated with a candidate label set, which consists of one ground-truth label and several false positive labels.