Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

This paper aims to tackle the challenging problem of one-shot object detection.

The modernize result, our Second, the connects Ontheother77], theoutput BiLSTM. Weadopt AdamWoptimizer [wingthepreviousstudy [weadopt ratebatchsizesfor Sequencer2D-S, Sequencer2D-M, are 2048, 1536, and 1024, respectively.

SR, uses zero-shot queries to a large language model (LLM) to discover and evolve abstract concepts occurring in known high-performing hypotheses.

Atmospheric turbulence, caused by random fluctuations in the atmosphere's refractive index, introduces complex spatio-temporal distortions in imagery captured

Neural Information Processing Systems http://nips.cc/

Weconsider the pool-based activelearning problem, where only asubset ofthe training data is labeled, and the goal is to query a batch of unlabeled samples to be labeled so as to maximally improve model performance.

For labels taking values in a finite metric space, we introduce techniques new to weak supervision based on pseudo-Euclidean embeddings andtensor decompositions, providing anearly-consistent noise rate estimator.