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Found 7 papers, 1 papers with code
Experimenting with Large Language Models and vector embeddings in NASA SciX
Open-source Large Language Models enable projects such as NASA SciX (i. e., NASA ADS) to think out of the box and try alternative approaches for information retrieval and data augmentation, while respecting data copyright and users' privacy.
Finite Gaussian Neurons: Defending against adversarial attacks by making neural networks say "I don't know"
To further validate the capacity of Finite Gaussian Neurons to protect from adversarial attacks, I compare the behavior of FGNs to that of Bayesian Neural Networks against both randomized and adversarial images, and show how the behavior of the two architectures differs.
Improving astroBERT using Semantic Textual Similarity
The NASA Astrophysics Data System (ADS) is an essential tool for researchers that allows them to explore the astronomy and astrophysics scientific literature, but it has yet to exploit recent advances in natural language processing.
Building astroBERT, a language model for Astronomy & Astrophysics
The existing search tools for exploring the NASA Astrophysics Data System (ADS) can be quite rich and empowering (e. g., similar and trending operators), but researchers are not yet allowed to fully leverage semantic search.
Enhancement of Spatial Clustering-Based Time-Frequency Masks using LSTM Neural Networks
By using LSTMs to enhance spatial clustering based time-frequency masks, we achieve both the signal modeling performance of multiple single-channel LSTM-DNN speech enhancers and the signal separation performance and generality of multi-channel spatial clustering.
Improved MVDR Beamforming Using LSTM Speech Models to Clean Spatial Clustering Masks
Spatial clustering techniques can achieve significant multi-channel noise reduction across relatively arbitrary microphone configurations, but have difficulty incorporating a detailed speech/noise model.
Combining Spatial Clustering with LSTM Speech Models for Multichannel Speech Enhancement
The system is compared to several baselines on the CHiME3 dataset in terms of speech quality predicted by the PESQ algorithm and word error rate of a recognizer trained on mis-matched conditions, in order to focus on generalization.
