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Outperforms Reference

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Preview of Rank Aggregation
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Rank Aggregation (249_main_paper.pdf)

1.64 MBEN 10 pages
Spectral methods rank items using scarce pairwise preferences and additional feature information
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Preview of Stochastic Planning Inference
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Stochastic Planning Inference (wu22a.pdf)

548 KBEN 12 pages
Stochastic planning is reduced to probabilistic inference in large discrete graphical models, requiring approximation schemes due to hardness of inference.
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Preview of Pre-print
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Pre-print (ocamlrc.pdf)

429 KBEN 4 pages
The Perceus algorithm is a precise and garbage-free reference counting scheme that has shown good performance in practice
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Preview of Probabilistic Multimodal Modeling for Human-Robot Interaction Tasks
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Probabilistic Multimodal Modeling for Human-Robot Interaction Tasks (p47.pdf)

4.51 MBEN 9 pages
Probabilistic Multimodal Modeling for Human-Robot Interaction Tasks: This work introduces a reformulation of Interaction Primitives, called ensemble Bayesian...
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Preview of Fine-Tuning Models
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Fine-Tuning Models (1909.08593.pdf)

944 KBDaniel M. Ziegler* , Nisan Stiennon* , Jeffrey Wu , Tom B. Brown , Alec Radford , Dario Amodei , PauEN 26 pages
Researchers fine-tuned language models using reinforcement learning from human preferences, achieving good results on stylistic continuation and summarization
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Preview of pdf
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pdf (D17-1195.pdf)

201 KBYangfeng Ji ; Chenhao Tan ; Sebastian Martschat ; Yejin Choi ; Noah A. SmithEN 10 pages
Researchers presented a new language model, ENTITYNLM, that can explicitly model entities, dynamically update their representations, and contextually generate...
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Preview of Compounded Corruption: A Fast and Effective Data Augmentation Technique for Out-of-Distribution Sample Detection
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Compounded Corruption: A Fast and Effective Data Augmentation Technique for Out-of-Distribution Sample Detection (2207.13916.pdf)

4.72 MBEN 43 pages
A novel data augmentation technique, Compounded Corruptions (CnC), creates synthetic out-of-distribution samples by corrupting in-distribution data, enabling the training of effective OOD detection classifiers without requiring labeled OOD examples.
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Preview of arXiv:0907.4010v1 [stat.CO] 23 Jul 2009
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arXiv:0907.4010v1 [stat.CO] 23 Jul 2009 (0907.4010v1.pdf)

101 KBEN 10 pages
It addresses challenges in Bayesian inference for models with censored or order-restricted parameters, where analytical computations are difficult. The methods utilize accept-reject sampling, Gibbs sampling, and Markov Chain Monte-Carlo techniques.
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