Machine Translation Decoding beyond Beam Search [PDF]
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 2021 23 ...
Leblond, Rémi +7 more
openaire +4 more sources
Beyond Greedy Search: Tracking by Multi-Agent Reinforcement Learning-Based Beam Search [PDF]
IEEE Transactions on Image Processing, 2022 Accepted by IEEE TIP ...
Xiao Wang +5 more
openaire +4 more sources
Millimeter-Wave Beam Search With Iterative Deactivation and Beam Shifting [PDF]
IEEE Transactions on Wireless Communications, 2020 Millimeter Wave (mmWave) communications rely on highly directional beams to combat severe propagation loss. In this paper, an adaptive beam search algorithm based on spatial scanning, called Iterative Deactivation and Beam Shifting (IDBS), is proposed for mmWave beam alignment. IDBS does not require advance information such as the Signal-to-Noise Ratio
Chunshan Liu +5 more
openaire +4 more sources
Beam Search for Feature Selection [PDF]
arXiv.org, 2022 In this paper, we present and prove some consistency results about the performance of classification models using a subset of features. In addition, we propose to use beam search to perform feature selection, which can be viewed as a generalization of forward selection.
Fraiman, Nicolas, Li, Zichao
openaire +3 more sources
Beam Search for Automated Design and Scoring of Novel ROR Ligands with Machine Intelligence*. [PDF]
Angew Chem Int Ed Engl, 2021 Chemical language models enable de novo drug design without the requirement for explicit molecular construction rules. While such models have been applied to generate novel compounds with desired bioactivity, the actual prioritization and selection of ...
Moret M +4 more
europepmc +2 more sources
Finding syntax in human encephalography with beam search [PDF]
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2018 ACL2018
Hale, John +3 more
openaire +4 more sources
Sequence-to-Sequence Learning as Beam-Search Optimization [PDF]
Conference on Empirical Methods in Natural Language Processing, 2016 Sequence-to-Sequence (seq2seq) modeling has rapidly become an important general-purpose NLP tool that has proven effective for many text-generation and sequence-labeling tasks.
Sam Wiseman, Alexander M. Rush
openalex +3 more sources
Automatic Prompt Optimization with "Gradient Descent" and Beam Search [PDF]
Conference on Empirical Methods in Natural Language Processing, 2023 Large Language Models (LLMs) have shown impressive performance as general purpose agents, but their abilities remain highly dependent on prompts which are hand written with onerous trial-and-error effort. We propose a simple and nonparametric solution to
Reid Pryzant +5 more
semanticscholar +1 more source
Don’t Say What You Don’t Know: Improving the Consistency of Abstractive Summarization by Constraining Beam Search [PDF]
IEEE Games Entertainment Media Conference, 2022 ive summarization systems today produce fluent and relevant output, but often “hallucinate” statements not supported by the source text. We analyze the connection between hallucinations and training data, and find evidence that models hallucinate because
Daniel L. King +5 more
openalex +3 more sources
Simulation-guided Beam Search for Neural Combinatorial Optimization [PDF]
Neural Information Processing Systems, 2022 Neural approaches for combinatorial optimization (CO) equip a learning mechanism to discover powerful heuristics for solving complex real-world problems.
Jinho Choo +6 more
semanticscholar +1 more source