Asia
TowardsVersatileEmbodiedNavigation
VIENNA performs robust on unseen environments, through learning task-shared, general representations.
ReEvo: Large Language Models as Hyper-Heuristics with Reflective Evolution Haoran Y e
The omnipresence of NP-hard combinatorial optimization problems (COPs) compels domain experts to engage in trial-and-error heuristic design. The long-standing endeavor of design automation has gained new momentum with the rise of large language models (LLMs).
Latency-awareSpatial-wiseDynamicNetworks
The key challenge is that the existing literature has only focused on designing algorithms with minimalcomputation, ignoring the fact that the practical latency can also be influenced byscheduling strategiesand hardware properties.
The Implicit Metropolis-Hastings Algorithm
Kirill Neklyudov, Evgenii Egorov, Dmitry P. Vetrov
Neural Information Processing Systems http://nips.cc/
Learnable Tree Filter for Structure-preserving Feature Transform
Lin Song, Yanwei Li, Zeming Li, Gang Yu, Hongbin Sun, Jian Sun, Nanning Zheng
Neural Information Processing Systems http://nips.cc/
Toddler-Inspired Visual Object Learning
Sven Bambach, David Crandall, Linda Smith, Chen Yu
You Only Look Once: ime Object Detection.
ef0c0a23a1a8219c4fc381614664df3e-Paper-Conference.pdf
Thefamily of f-divergences includes Kullback-Leibler divergence [23] and Total Variation distance.
