Search Results for author:
Found 6 papers, 2 papers with code
EpiK-Eval: Evaluation for Language Models as Epistemic Models
In the age of artificial intelligence, the role of large language models (LLMs) is becoming increasingly central.
PatchBlender: A Motion Prior for Video Transformers
We show that our method is successful at enabling vision transformers to encode the temporal component of video data.
Scaling Laws for the Few-Shot Adaptation of Pre-trained Image Classifiers
Empirical science of neural scaling laws is a rapidly growing area of significant importance to the future of machine learning, particularly in the light of recent breakthroughs achieved by large-scale pre-trained models such as GPT-3, CLIP and DALL-e.
Out-of-Distribution Classification and Clustering
This includes left out classes from the same dataset, as well as entire datasets never trained on.
Fully Quantized Transformer for Machine Translation
State-of-the-art neural machine translation methods employ massive amounts of parameters.
Towards Lossless Encoding of Sentences
A lot of work has been done in the field of image compression via machine learning, but not much attention has been given to the compression of natural language.
