Deep Learning
Dream the Impossible: Outlier Imagination with Diffusion Models
Automating outlier data generation has thus been a long-desired alternative.
Enhancing Efficiency of Generated Code via Self-Optimization Dong Huang
These profiles are fed back to the LLM, which then revises the code to reduce overhead.
FAST opic: Pretrained Transformer is a Fast, Adaptive, Stable, and Transferable Topic Model Xiaobao Wu1 Thong Nguyen
Topic models have been evolving rapidly over the years, from conventional to recent neural models.
Leveraging an ECG Beat Diffusion Model for Morphological Reconstruction from Indirect Signals
Electrocardiogram (ECG) signals provide essential information about the heart's
Scaling MLPs: AT ale of Inductive Bias
Empirical insights into MLPs are important for multiple reasons.
Generalized Semi-Supervised Learning via Self-Supervised Feature Adaptation
Under this setting, previous SSL methods tend to predict wrong pseudo-labels with the model fitted on labeled data, resulting in noise accumulation.
Aiming towards the minimizers: fast convergence of SGD for overparameterized problems
Recent advances in machine learning and artificial intelligence have relied on fitting highly overparam-eterized models, notably deep neural networks, to observed data; e.g.