Search for a heavy resonance decaying to a top quark and a vector-like top quark in the lepton + jets final state in pp collisions at s = 13 TeV
European Physical Journal C(2019)SCI 2区
Yerevan Physics Institute | Institut für Hochenergiephysik | Catholic Univ Louvain | Ctr Brasileiro Pesquisas Fis | Laboratoire Leprince-Ringuet | Univ Claude Bernard Lyon 1 | Inst Expt Kernphys | National Institute of Science Education and Research | Panjab University | INFN Sezione di Bari | INFN Sezione di Padova | CERN | University of California | Massachusetts Institute of Technology | Rutgers | Wayne State University | University of Wisconsin - Madison
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Neutral Naturalness from a Holographic SO(6)/SO(5) Composite Higgs Model
被引用19
The Motivation and Status of Two-Body Resonance Decays after the LHC Run 2 and Beyond
被引用20
Dijet Resonance Search with Weak Supervision Using S=13 TeV Pp Collisions in the ATLAS Detector
被引用51
被引用94
Build Complementary Models on Human Feedback for Simulation to the Real World
被引用0
Search for New Resonances Coupling to Third Generation Quarks at CMS
被引用0
被引用6