Results 31 to 40 of about 28,651,668 (327)
Critical Noise Levels for LDPC decoding [PDF]
We determine the critical noise level for decoding low density parity check error correcting codes based on the magnetization enumerator ($\cM$), rather than on the weight enumerator ($\cW$) employed in the information theory literature.
A. Montanari+13 more
core +3 more sources
Decoding Visual Neural Representations by Multimodal Learning of Brain-Visual-Linguistic Features [PDF]
Decoding human visual neural representations is a challenging task with great scientific significance in revealing vision-processing mechanisms and developing brain-like intelligent machines.
Changde Du+3 more
semanticscholar +1 more source
On Decoding Strategies for Neural Text Generators [PDF]
When generating text from probabilistic models, the chosen decoding strategy has a profound effect on the resulting text. Yet the properties elicited by various decoding strategies do not always transfer across natural language generation tasks.
Gian Wiher+2 more
semanticscholar +1 more source
Follow the Wisdom of the Crowd: Effective Text Generation via Minimum Bayes Risk Decoding [PDF]
In open-ended natural-language generation, existing text decoding methods typically struggle to produce text which is both diverse and high-quality. Greedy and beam search are known to suffer from text degeneration and linguistic diversity issues, while ...
Mirac Suzgun+2 more
semanticscholar +1 more source
Improving Text-to-SQL with a Hybrid Decoding Method
Text-to-SQL is a task that converts natural language questions into SQL queries. Recent text-to-SQL models employ two decoding methods: sketch-based and generation-based, but each has its own shortcomings. The sketch-based method has limitations in performance as it does not reflect the relevance between SQL elements, while the generation-based method ...
Geunyeong Jeong+6 more
openaire +3 more sources
Quality-Aware Decoding for Neural Machine Translation [PDF]
Despite the progress in machine translation quality estimation and evaluation in the last years, decoding in neural machine translation (NMT) is mostly oblivious to this and centers around finding the most probable translation according to the model (MAP
Patrick Fernandes+6 more
semanticscholar +1 more source
Incremental Decoding and Training Methods for Simultaneous Translation in Neural Machine Translation [PDF]
We address the problem of simultaneous translation by modifying the Neural MT decoder to operate with dynamically built encoder and attention. We propose a tunable agent which decides the best segmentation strategy for a user-defined BLEU loss and ...
Fahim Dalvi+3 more
semanticscholar +1 more source
Neural Decoding for Intracortical Brain–Computer Interfaces
Brain–computer interfaces have revolutionized the field of neuroscience by providing a solution for paralyzed patients to control external devices and improve the quality of daily life.
Yuanrui Dong+5 more
semanticscholar +1 more source
On optimal and near-optimal turbo decoding using generalized max operator [PDF]
Motivated by a recently published robust geometric programming approximation, a generalized approach for approximating efficiently the max* operator is presented.
Martina, Maurizio+3 more
core +1 more source
Comparing decoding methods for quaternary linear codes [PDF]
Permutation decoding is a technique which involves finding a subset S, called PD-set, of the permutation automorphism group of a code C. Constructions of small PD-sets for partial decoding for two families of Z₄-linear codes (Hadamard and Kerdock) are given.
Barrolleta, Roland David+2 more
openaire +4 more sources