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13 papers · 1 filter
Self-Progressing Robust Training
Minhao Cheng, Pin-Yu Chen, Sijia Liu +3
Enhancing model robustness under new and even adversarial environments is a crucial milestone toward building trustworthy machine learning systems. Current robust training methods…
Supernova X-Ray Database (SNaX) Updated to Ensure Long-term Stability
Alexandra Nisenoff, Vikram V. Dwarkadas, Mathias C. Ross
The Supernova X-Ray Database (SNaX) was established a few years ago to make X-ray data on supernovae (SNe) publicly available via an elegant searchable web interface. The database…
Revealing the Formation of the Milky Way Nuclear Star Cluster via Chemo-Dynamical Modeling
Tuan Do, Gregory David Martinez, Wolfgang Kerzendorf +4
The Milky Way nuclear star cluster (MW NSC) has been used as a template to understand the origin and evolution of galactic nuclei and the interaction of nuclear star clusters with…
Obtaining Adjustable Regularization for Free via Iterate Averaging
Jingfeng Wu, Vladimir Braverman, Lin F. Yang
Regularization for optimization is a crucial technique to avoid overfitting in machine learning. In order to obtain the best performance, we usually train a model by tuning the reg…
Closed Loop Neural-Symbolic Learning via Integrating Neural Perception, Grammar Parsing, and Symbolic Reasoning
Qing Li, Siyuan Huang, Yining Hong +3
The goal of neural-symbolic computation is to integrate the connectionist and symbolist paradigms. Prior methods learn the neural-symbolic models using reinforcement learning (RL)…
mmRAPID: Machine Learning assisted Noncoherent Compressive Millimeter-Wave Beam Alignment
Han Yan, Benjamin W. Domae, Danijela Cabric
Millimeter-wave communication has the potential to deliver orders of magnitude increases in mobile data rates. A key design challenge is to enable rapid beam alignment with phased…