Results 21 to 30 of about 500,674 (362)
2017 IEEE International Symposium on Information Theory (ISIT), 2017 The security of code-based cryptography relies primarily on the hardness of generic decoding with linear codes. The best generic decoding algorithms are all improvements of an old algorithm due to Prange: they are known under the name of information set decoding techniques (ISD).
Debris-Alazard, Thomas +1 more
openaire +2 more sources
PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models [PDF]
Conference on Empirical Methods in Natural Language Processing, 2021 Large pre-trained language models for textual data have an unconstrained output space; at each decoding step, they can produce any of 10,000s of sub-word tokens. When fine-tuned to target constrained formal languages like SQL, these models often generate
Torsten Scholak +2 more
semanticscholar +1 more source
DExperts: Decoding-Time Controlled Text Generation with Experts and Anti-Experts [PDF]
Annual Meeting of the Association for Computational Linguistics, 2021 Despite recent advances in natural language generation, it remains challenging to control attributes of generated text. We propose DExperts: Decoding-time Experts, a decoding-time method for controlled text generation that combines a pretrained language ...
Alisa Liu +6 more
semanticscholar +1 more source
Decoding with confidence: Statistical control on decoder maps
NeuroImage, 2021 In brain imaging, decoding is widely used to infer relationships between brain and cognition, or to craft brain-imaging biomarkers of pathologies. Yet, standard decoding procedures do not come with statistical guarantees, and thus do not give confidence bounds to interpret the pattern maps that they produce.
Chevalier, Jérôme-Alexis +4 more
openaire +4 more sources
Decoding speech perception from non-invasive brain recordings [PDF]
Nature Machine Intelligence, 2022 Decoding speech from brain activity is a long-awaited goal in both healthcare and neuroscience. Invasive devices have recently led to major milestones in this regard: deep-learning algorithms trained on intracranial recordings can now start to decode ...
Alexandre D'efossez +4 more
semanticscholar +1 more source
Mechanical hierarchy in the formation and modulation of cortical folding patterns
Scientific Reports, 2023 The important mechanical parameters and their hierarchy in the growth and folding of the human brain have not been thoroughly understood. In this study, we developed a multiscale mechanical model to investigate how the interplay between initial ...
Poorya Chavoshnejad +6 more
doaj +1 more source
Decoding by linear programming [PDF]
IEEE Transactions on Information Theory, 2005 This paper considers a natural error correcting problem with real valued input/output. We wish to recover an input vector f/spl isin/R/sup n/ from corrupted measurements y=Af+e.
E. Candès, T. Tao
semanticscholar +1 more source
Accelerating LLM Inference with Staged Speculative Decoding [PDF]
arXiv.org, 2023 Recent advances with large language models (LLM) illustrate their diverse capabilities. We propose a novel algorithm, staged speculative decoding, to accelerate LLM inference in small-batch, on-device scenarios. We address the low arithmetic intensity of
B. Spector, Christal Re
semanticscholar +1 more source
(De)Coding of Abbreviations from the Last Two Decades
Педагогически форум, 2023 The publication examines abbreviations and combinations with an abbreviated element registered in the public domain in the last two decades, as well as the re-actualization of long-known abbreviations.
Teodora Ilieva
doaj +1 more source
EEG Conformer: Convolutional Transformer for EEG Decoding and Visualization
IEEE transactions on neural systems and rehabilitation engineering, 2022 Due to the limited perceptual field, convolutional neural networks (CNN) only extract local temporal features and may fail to capture long-term dependencies for EEG decoding.
Yonghao Song +3 more
semanticscholar +1 more source