山海星月:人工智能前沿进展(1):learning interactive real-world simulators3 赞同 · 2 评论文章 在本期中,我们将详细介绍 2024 年国际机器学习会议(ICML)评选出的杰出论文:《Probabilistic Inference in Language Models via Twisted Sequential Monte Carlo》。 由于
Language comprehension and production involve probabilistic inference in such models; and acquisition involves choosing the best model, given innate constraints and linguistic and other input. Probabilistic models can account for the learning and processing of language, while maintaining the sophistication of...
We have now discussed all parameters required to complete the model, and note that standard methods forprobabilistic inferencein graphical models [Pearl, 1988; Dawid, 1992] can be applied, which calculates probabilities for all variables and events at all points in time (i.e. for all nodes in...
Large Language Models (LLMs) have shown promise in clinical applications through prompt engineering, allowing flexible clinical predictions. However, they struggle to produce reliable prediction probabilities, which are crucial for transparency and decision-making. While explicit prompts can lead LLMs to ...
4.4.3 Cook’s Grammatical Inference by Hill Climbing 92 4.5 Evaluation 93 4.5.1 Formal language benchmarks 93 4.5.2 Natural language syntax 96 4.5.3 Sample ordering 101 CONTENTS v 4.5.4 Summary and Discussion 103 5 Probabilistic AttributeGrammars 104 ...
Specifically, we propose that probabilistic inference tuned to the statistics of natural images can explain the properties of response variability in visual cortex. Although normative models have typically been used to explain trial-averaged responses, they can also be used to explain response ...
[Large Language Diffusion Models]: Autoregressive models (ARMs) are widely regarded as the cornerstone of large language models (LLMs). We challenge this notion by introducing LLaDA, a diffusion model trained from scratch under the pre-training and supervised fine-tuning (SFT) paradigm. LLaDA mode...
1998. Statistical inference and probabilistic modeling for constraint-based nlp. In B. Schro篓der, W. Lenders, W. Hess, and T. Portele, editors, Computers, Linguis- tics, and Phonetics between Language and Speech: Proceedings of the 4th Conference on Natural Language Processing (KONVENS'98),...
Chronos: Pretrained (Language) Models for Probabilistic Time Series Forecasting - YieldXLabs/chronos-forecasting
Thus the application of graphical models to practical problems requires the solution of inference problems, and here graphical models are particularly powerful in allowing the general inference problem to be solved exactly through graphical manipulations. For tree-structured graphs the framework of belief ...