Search Results for author:
Found 9 papers, 5 papers with code
Establishing Task Scaling Laws via Compute-Efficient Model Ladders
We develop task scaling laws and model ladders to predict the individual task performance of pretrained language models (LMs) in the overtrained setting.
Scalable Data Ablation Approximations for Language Models through Modular Training and Merging
Training data compositions for Large Language Models (LLMs) can significantly affect their downstream performance.
OLMo: Accelerating the Science of Language Models
Given the importance of these details in scientifically studying these models, including their biases and potential risks, we believe it is essential for the research community to have access to powerful, truly open LMs.
Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research
As a result, it is challenging to conduct and advance scientific research on language modeling, such as understanding how training data impacts model capabilities and limitations.
Paloma: A Benchmark for Evaluating Language Model Fit
Evaluations of language models (LMs) commonly report perplexity on monolithic data held out from training.
Just CHOP: Embarrassingly Simple LLM Compression
Large language models (LLMs) enable unparalleled few- and zero-shot reasoning capabilities but at a high computational footprint.
AAVAE: Augmentation-Augmented Variational Autoencoders
We empirically evaluate the proposed AAVAE on image classification, similar to how recent contrastive and non-contrastive learning algorithms have been evaluated.
AASAE: Augmentation-Augmented Stochastic Autoencoders
We empirically evaluate the proposed AASAE on image classification, similar to how recent contrastive and non-contrastive learning algorithms have been evaluated.
Disentangling Factors of Variation with Cycle-Consistent Variational Auto-Encoders
Our non-adversarial approach is in contrast with the recent works that combine adversarial training with auto-encoders to disentangle representations.
