Search Results for author:
Found 6 papers, 2 papers with code
Global Contrastive Batch Sampling via Optimization on Sample Permutations
Contrastive Learning has recently achieved state-of-the-art performance in a wide range of tasks.
CALM: Contrastive Aligned Audio-Language Multirate and Multimodal Representations
By aligning audio representations to pretrained language representations and utilizing contrastive information between acoustic inputs, CALM is able to bootstrap audio embedding competitive with existing audio representation models in only a few hours of training time.
Efficient Domain Adaptation of Language Models via Adaptive Tokenization
While adaptive tokenization incurs a 6% increase in model parameters in our experimentation, due to the introduction of 10k new domain-specific tokens, our approach, using 64 vCPUs, is 72x faster than further pretraining the language model on domain-specific corpora on 8 TPUs.
Filtered Inner Product Projection for Crosslingual Embedding Alignment
Due to widespread interest in machine translation and transfer learning, there are numerous algorithms for mapping multiple embeddings to a shared representation space.
Out-of-Vocabulary Embedding Imputation with Grounded Language Information by Graph Convolutional Networks
In this paper, we propose an approach for embedding imputation which uses grounded information in the form of a knowledge graph.
The Global Anchor Method for Quantifying Linguistic Shifts and Domain Adaptation
We show both theoretically and empirically that the global anchor method is equivalent to the alignment method, a widely-used method for comparing word embeddings, in terms of detecting corpus-level language shifts.
