Education
Advancing Training Efficiency of Deep Spiking Neural Networks through Rate-based Backpropagation
Recent insights have revealed that rate-coding is a primary form of information representation captured by surrogate-gradient-based Backpropagation Through Time (BPTT) in training deep Spiking Neural Networks (SNNs).
LFME: A Simple Framework for Learning from Multiple Experts in Domain Generalization
Extensive experiments on benchmarks from different DG tasks demonstrate that LFME is consistently beneficial to the baseline and can achieve comparable performance to existing arts.
MixEval: Deriving Wisdom of the Crowd from LLM Benchmark Mixtures Jinjie Ni
Evaluating large language models (LLMs) is challenging.
ProtGO: Function-Guided Protein Modeling for Unified Representation Learning
In addition to sequence-based approaches, structure-based encoders have emerged to leverage the 3D structural information of proteins.
Dynamic Subgroup Identification in Covariate-adjusted Response-adaptive Randomization Experiments
While some adaptive experimental strategies exist for the identification of the single best subgroup, they commonly do not enable the identification of the best set of subgroups.