Search Results for author:
Found 7 papers, 3 papers with code
Efficient, sparse representation of manifold distance matrices for classical scaling
Thus for large point sets it is common to use a low-rank approximation to the distance matrix, which fits in memory and can be efficiently analyzed using methods such as multidimensional scaling (MDS).
Deep Generative Modeling for Scene Synthesis via Hybrid Representations
We show a principled way to train this model by combining discriminator losses for both a 3D object arrangement representation and a 2D image-based representation.
Incorporating Context into Language Encoding Models for fMRI
By varying the amount of context used in the models and providing the models with distorted context, we show that this improvement is due to a combination of better word embeddings learned by the LSTM language model and contextual information.
Selecting Informative Contexts Improves Language Model Finetuning
Here we present a general fine-tuning method that we call information gain filtration for improving the overall training efficiency and final performance of language model fine-tuning.
Interpretable multi-timescale models for predicting fMRI responses to continuous natural speech
To understand how the human brain represents this information, one approach is to build encoding models that predict fMRI responses to natural language using representations extracted from neural network language models (LMs).
Low-Dimensional Structure in the Space of Language Representations is Reflected in Brain Responses
We find that this representation embedding can predict how well each individual feature space maps to human brain responses to natural language stimuli recorded using fMRI.
Selecting Informative Contexts Improves Language Model Fine-tuning
Here we present a general fine-tuning method that we call information gain filtration for improving the overall training efficiency and final performance of language model fine-tuning.
