220 citations · 819 across the 39 of their papers we have counts for
7 papers · 1 filter
Pressure-induced superconductivity in itinerant antiferromagnet CrB2
Cuiying Pei, Pengtao Yang, Chunsheng Gong +11
The recent discovery of superconductivity up to 32 K in the pressurized MoB2 revives the interests in exploring novel superconductors in transition-metal diborides isostructural to…
ST-MAML: A Stochastic-Task based Method for Task-Heterogeneous Meta-Learning
Zhe Wang, Jake Grigsby, Arshdeep Sekhon +1
Optimization-based meta-learning typically assumes tasks are sampled from a single distribution - an assumption oversimplifies and limits the diversity of tasks that meta-learning…
Perturbing Inputs for Fragile Interpretations in Deep Natural Language Processing
Sanchit Sinha, Hanjie Chen, Arshdeep Sekhon +2
Interpretability methods like Integrated Gradient and LIME are popular choices for explaining natural language model predictions with relative word importance scores. These interpr…
Towards Improving Adversarial Training of NLP Models
Jin Yong Yoo, Yanjun Qi
Adversarial training, a method for learning robust deep neural networks, constructs adversarial examples during training. However, recent methods for generating NLP adversarial exa…
Charge density wave orders and enhanced superconductivity under pressure in the kagome metal CsV3Sb5
Qi Wang, Pengfei Kong, Wujun Shi +12
Superconductivity in topological kagome metals has recently received great research interests. Here, charge density wave (CDW) orders and the evolution of superconductivity under v…
Relate and Predict: Structure-Aware Prediction with Jointly Optimized Neural DAG
Arshdeep Sekhon, Zhe Wang, Yanjun Qi
Understanding relationships between feature variables is one important way humans use to make decisions. However, state-of-the-art deep learning studies either focus on task-agnost…