permutation
Neural Functional Transformers Allan Zhou
However, developing models that can process weight-space objects is challenging due to their high dimensional nature.
MMD-FUSE: Learning and Combining Kernels for Two-Sample Testing Without Data Splitting
The fundamental problem of non-parametric two-sample testing consists in detecting the difference between any two distributions having access only to samples from these.
- North America > United States > Washington > King County > Seattle (0.04)
- North America > United States > Maryland > Baltimore (0.04)
- North America > United States > Colorado > Boulder County > Boulder (0.04)
- (7 more...)
Fast Scalable and Accurate Discovery of DAGs Using the Best Order Score Search and Grow-Shrink Trees
We introduce the best order score search (BOSS) and grow-shrink trees (GSTs) for learning directed acyclic graphs (DAGs) in this paradigm.
- North America > United States > Minnesota > Hennepin County > Minneapolis (0.29)
- North America > United States > Pennsylvania > Allegheny County > Pittsburgh (0.04)
- North America > United States > New Jersey > Essex County > Newark (0.04)
Coordinating Distributed Example Orders for Provably Accelerated Training
Whereas RR arbitrarily permutes training examples, GraB leverages stale gradients from prior epochs to order examples -- achieving a provably faster convergence rate than RR.
- North America > United States (0.04)
- Europe > Belgium > Brussels-Capital Region > Brussels (0.04)
CD_GraB_camera_ready
Whereas RR arbitrarily permutes training examples, GraB leverages stale gradients from prior epochs to order examples -- achieving a provably faster convergence rate than RR.
- North America > United States (0.04)
- Europe > Belgium > Brussels-Capital Region > Brussels (0.04)
Appendices Table of Contents
Other concurrent works address, e.g., learning from soft interventions with polynomial mixing [
- Europe > United Kingdom > England > Cambridgeshire > Cambridge (0.28)
- Asia > Japan > Honshū > Tōhoku > Iwate Prefecture > Morioka (0.04)
- Europe > Germany > Baden-Württemberg > Tübingen Region > Tübingen (0.04)
- (2 more...)
- Information Technology > Artificial Intelligence > Representation & Reasoning (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Learning Graphical Models (0.93)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (0.92)
- Asia > Japan > Honshū > Tōhoku > Iwate Prefecture > Morioka (0.04)
- North America > United States > Pennsylvania > Allegheny County > Pittsburgh (0.04)
- Europe > United Kingdom > England > Tyne and Wear > Sunderland (0.04)
- Asia > China > Tianjin Province > Tianjin (0.04)
- Asia > Singapore (0.04)
- Asia > China > Guangdong Province > Shenzhen (0.04)
- Information Technology > Artificial Intelligence > Representation & Reasoning (1.00)
- Information Technology > Artificial Intelligence > Natural Language > Large Language Model (1.00)
- Information Technology > Artificial Intelligence > Natural Language > Chatbot (0.96)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (0.96)
- Oceania > Australia > South Australia > Adelaide (0.04)
- North America > United States > New York > New York County > New York City (0.04)
- North America > United States > Minnesota > St. Louis County > Duluth (0.04)
- (2 more...)
- Overview (0.68)
- Research Report (0.46)